diff --git a/001693/KhaliqLab/snc_dopaminergic_ais/001693_demo.ipynb b/001693/KhaliqLab/snc_dopaminergic_ais/001693_demo.ipynb new file mode 100644 index 0000000..3800a6d --- /dev/null +++ b/001693/KhaliqLab/snc_dopaminergic_ais/001693_demo.ipynb @@ -0,0 +1,2105 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "34cf20e8", + "metadata": {}, + "source": [ + "# Khaliq Lab - Intracellular Recordings of Substantia Nigra Dopaminergic Neurons\n", + "\n", + "This notebook demonstrates how to access the NWB files published at [DANDI:001693](https://dandiarchive.org/dandiset/001693) (\"Khaliq Embargo 2026\").\n", + "\n", + "**Embargo:** This dandiset is currently embargoed. Access requires a DANDI account that has been granted permission, and streaming requires authentication (shown below). Once the dandiset is public, the `client.dandi_authenticate()` call can be removed.\n", + "\n", + "## Scientific context\n", + "The c\n", + "The dataset contains whole-cell current-clamp recordings from dopaminergic neurons in the substantia nigra pars compacta, investigating how the axon initial segment (AIS) contributes to intrinsic firing properties. Each recording session (one subject on one date) contains multiple cells and three protocols per cell:\n", + "\n", + "- **SF** - Spontaneous Firing (baseline intrinsic firing, no current injection)\n", + "- **CS** - Current Steps (response to stepwise current injection)\n", + "- **ADP** - After Depolarization (post-stimulus depolarization following brief current pulses)\n", + "\n", + "Cells are classified as \"with AIS component\" or \"without AIS component\" to probe how AIS morphology shapes excitability." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3e1436af", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:00:57.571009Z", + "iopub.status.busy": "2026-04-28T17:00:57.570911Z", + "iopub.status.idle": "2026-04-28T17:00:58.199290Z", + "shell.execute_reply": "2026-04-28T17:00:58.198755Z" + } + }, + "outputs": [], + "source": [ + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import remfile\n", + "from dandi.dandiapi import DandiAPIClient\n", + "from pynwb import NWBHDF5IO" + ] + }, + { + "cell_type": "markdown", + "id": "71255014", + "metadata": {}, + "source": [ + "## Load a local session\n", + "\n", + "While the dataset is being prepared for upload, we read directly from a local NWB file in the repository's `nwbfiles/` directory. The streaming code is preserved below (commented out) and can be re-enabled once the dandiset is published. With the current embargo, streaming requires `client.dandi_authenticate()`, which reads the DANDI token from the `DANDI_API_KEY` environment variable or prompts interactively." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "84f7cd50", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:00:58.200767Z", + "iopub.status.busy": "2026-04-28T17:00:58.200498Z", + "iopub.status.idle": "2026-04-28T17:01:01.597561Z", + "shell.execute_reply": "2026-04-28T17:01:01.597107Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: Multi-cell intracellular current-clamp recordings from 1 Dopaminergic neuron(s) in Substantia Nigra of subject #3 on 3 Nov 2021
identifier: subject3++20211103
session_start_time2021-11-03 13:26:31.563000-04:00
timestamps_reference_time2021-11-03 13:26:31.563000-04:00
file_create_date
02026-04-28 08:18:21.532977-06:00
experimenter('Khaliq, Zayd', 'Sansalone, Lorenzo')
icephys_electrodes
IntracellularElectrodeCell12 (IntracellularElectrode)
description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)
location: substantia nigra pars compacta
device (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
cell_id: DopaminergicCell12
acquisition
CurrentClampSeriesAisProtocolADPCell12 (CurrentClampSeries)
resolution: -1.0
comments: no comments
description: Concatenated voltage response across all sweeps of the ADP protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.
conversion: 1.0
offset: 0.0
unit: volts
data
HDF5 dataset
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timestamps
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electrode (IntracellularElectrode)
description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)
location: substantia nigra pars compacta
device (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
cell_id: DopaminergicCell12
gain: 1.0
stimulus_description: CurrentClampStimulusSeriesAisProtocolADPCell12
timestamp_link
0: stimulus/CurrentClampStimulusSeriesAisProtocolADPCell12/timestamps
CurrentClampSeriesAisProtocolCSCell12 (CurrentClampSeries)
resolution: -1.0
comments: no comments
description: Concatenated voltage response across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.
conversion: 1.0
offset: 0.0
unit: volts
data
HDF5 dataset
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timestamps
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timestamps_unit: seconds
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electrode (IntracellularElectrode)
description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)
location: substantia nigra pars compacta
device (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
cell_id: DopaminergicCell12
gain: 1.0
stimulus_description: CurrentClampStimulusSeriesAisProtocolCSCell12
timestamp_link
0: stimulus/CurrentClampStimulusSeriesAisProtocolCSCell12/timestamps
CurrentClampSeriesAisProtocolSFCell12 (CurrentClampSeries)
resolution: -1.0
comments: no comments
description: Concatenated voltage response across all sweeps of the SF protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.
conversion: 1.0
offset: 0.0
unit: volts
data
HDF5 dataset
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timestamps
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electrode (IntracellularElectrode)
description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)
location: substantia nigra pars compacta
device (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
cell_id: DopaminergicCell12
gain: 1.0
stimulus_description: none
stimulus
CurrentClampStimulusSeriesAisProtocolADPCell12 (CurrentClampStimulusSeries)
resolution: -1.0
comments: no comments
description: Concatenated current stimulus across all sweeps of the ADP protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via stimulus_start_index and stimulus_index_count.
conversion: 1.0
offset: 0.0
unit: amperes
data
HDF5 dataset
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timestamps (link to acquisition/CurrentClampSeriesAisProtocolADPCell12/timestamps)
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timestamps_unit: seconds
interval: 1
electrode (IntracellularElectrode)
description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)
location: substantia nigra pars compacta
device (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
cell_id: DopaminergicCell12
gain: 1.0
stimulus_description: N/A
CurrentClampStimulusSeriesAisProtocolCSCell12 (CurrentClampStimulusSeries)
resolution: -1.0
comments: no comments
description: Concatenated current stimulus across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via stimulus_start_index and stimulus_index_count.
conversion: 1.0
offset: 0.0
unit: amperes
data
HDF5 dataset
Data typefloat64
Shape(4600000,)
Array size35.10 MiB
Chunk shape(1250000,)
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timestamps (link to acquisition/CurrentClampSeriesAisProtocolCSCell12/timestamps)
HDF5 dataset
Data typefloat64
Shape(4600000,)
Array size35.10 MiB
Chunk shape(1250000,)
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timestamps_unit: seconds
interval: 1
electrode (IntracellularElectrode)
description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)
location: substantia nigra pars compacta
device (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
cell_id: DopaminergicCell12
gain: 1.0
stimulus_description: N/A
keywords
HDF5 dataset
Data typeobject
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Array size80.00 bytes
Chunk shapeNone
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Compression optsNone
Uncompressed size (bytes)80
Compressed size (bytes)160
Compression ratio0.5

['Dopaminergic' 'substantia nigra pars compacta' 'patch-clamp'\n", + " 'intracellular' 'current-clamp' 'axon initial segment' 'ADP' 'CS' 'SF'\n", + " 'with AIS component']
devices
MultiClamp700B (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
intervals
epochs (TimeIntervals)
description: Temporal intervals marking when each experimental protocol was performed on each cell. Each epoch spans all sweeps (repeated trials) of a single protocol recording. Three protocols were used: SF (Spontaneous Firing) captures baseline intrinsic firing patterns without current injection; CS (Current Steps) tests neuronal excitability with stepwise current injections of varying amplitudes; ADP (After Depolarization) examines membrane dynamics following brief current pulses.
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
protocol
Protocol abbreviation (SF=Spontaneous Firing, CS=Current Steps, ADP=After Depolarization)
cell_id
Cell identifier (e.g., C1, C2, ...)
ais_status
Axon initial segment component status
tags
user-defined tags
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start_timestop_timeprotocolcell_idais_statustags
id
00.00059.99998SFC12with AIS component[spontaneous_firing, baseline]
199.693323.69298CSC12with AIS component[current_steps, excitability_test]
2338.303423.30298ADPC12with AIS component[after_depolarization, post_stimulus]
invalid_times (TimeIntervals)
description: Time intervals containing voltage saturation artifacts that should be excluded from analysis. Saturation occurs when the recorded voltage exceeds the amplifier's measurement range (detected as values beyond +/- 200 mV), causing the signal to clip at the amplifier limits. During these intervals, the recorded voltage does not reflect true membrane potential. Each interval includes a reference to the affected time series and the extreme voltage values observed.
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
time_series
Reference to the TimeSeries where this invalid interval was detected
time_series_name
Name of the TimeSeries where this invalid interval was detected
voltage_min
Minimum voltage (V) during this saturated interval
voltage_max
Maximum voltage (V) during this saturated interval
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start_timestop_timetime_seriestime_series_namevoltage_minvoltage_max
id
0281.37236283.68658CurrentClampSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampSeries at 0x138129176502480\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: CurrentClampStimulusSeriesAisProtocolCSCell12\\n timestamp_link: (\\n CurrentClampStimulusSeriesAisProtocolCSCell12 <class 'pynwb.icephys.CurrentClampStimulusSeries'>\\n )\\n timestamps: <HDF5 dataset \"timestamps\": shape (4600000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\nCurrentClampSeriesAisProtocolCSCell12-0.9786380.994568
subject (Subject)
age: P11.5Y
age__reference: birth
description: Rhesus macaque subject with intracellular recordings from Dopaminergic neurons in Substantia Nigra
sex: F
species: Macaca mulatta
subject_id: #3
epochs (TimeIntervals)
description: Temporal intervals marking when each experimental protocol was performed on each cell. Each epoch spans all sweeps (repeated trials) of a single protocol recording. Three protocols were used: SF (Spontaneous Firing) captures baseline intrinsic firing patterns without current injection; CS (Current Steps) tests neuronal excitability with stepwise current injections of varying amplitudes; ADP (After Depolarization) examines membrane dynamics following brief current pulses.
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
protocol
Protocol abbreviation (SF=Spontaneous Firing, CS=Current Steps, ADP=After Depolarization)
cell_id
Cell identifier (e.g., C1, C2, ...)
ais_status
Axon initial segment component status
tags
user-defined tags
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start_timestop_timeprotocolcell_idais_statustags
id
00.00059.99998SFC12with AIS component[spontaneous_firing, baseline]
199.693323.69298CSC12with AIS component[current_steps, excitability_test]
2338.303423.30298ADPC12with AIS component[after_depolarization, post_stimulus]
invalid_times (TimeIntervals)
description: Time intervals containing voltage saturation artifacts that should be excluded from analysis. Saturation occurs when the recorded voltage exceeds the amplifier's measurement range (detected as values beyond +/- 200 mV), causing the signal to clip at the amplifier limits. During these intervals, the recorded voltage does not reflect true membrane potential. Each interval includes a reference to the affected time series and the extreme voltage values observed.
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
time_series
Reference to the TimeSeries where this invalid interval was detected
time_series_name
Name of the TimeSeries where this invalid interval was detected
voltage_min
Minimum voltage (V) during this saturated interval
voltage_max
Maximum voltage (V) during this saturated interval
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
start_timestop_timetime_seriestime_series_namevoltage_minvoltage_max
id
0281.37236283.68658CurrentClampSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampSeries at 0x138129176502480\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: CurrentClampStimulusSeriesAisProtocolCSCell12\\n timestamp_link: (\\n CurrentClampStimulusSeriesAisProtocolCSCell12 <class 'pynwb.icephys.CurrentClampStimulusSeries'>\\n )\\n timestamps: <HDF5 dataset \"timestamps\": shape (4600000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\nCurrentClampSeriesAisProtocolCSCell12-0.9786380.994568
experiment_description: Intracellular patch-clamp electrophysiology recordings from dopaminergic neurons in the substantia nigra pars compacta to investigate intrinsic firing properties and the role of axon initial segment (AIS) morphology in neuronal excitability. The experimental protocol consisted of three sequential current-clamp recordings to characterize intrinsic firing properties of dopaminergic neurons. First, spontaneous firing (SF) was recorded to establish baseline intrinsic firing patterns without current injection. Second, current steps (CS) protocols tested neuronal response to stepwise current injection of varying amplitudes. Third, after-depolarization (ADP) measurements examined post-stimulus depolarization following current injection. Recordings were performed on cells both with and without axon initial segment (AIS) components to investigate the role of AIS morphology in intrinsic excitability.
session_id: subject3++20211103
lab: Khaliq Lab
institution: National Institute of Neurological Disorders and Stroke, National Institutes of Health
protocol: The experimental protocol consisted of three sequential current-clamp recordings to characterize intrinsic firing properties of dopaminergic neurons. First, spontaneous firing (SF) was recorded to establish baseline intrinsic firing patterns without current injection. Second, current steps (CS) protocols tested neuronal response to stepwise current injection of varying amplitudes. Third, after-depolarization (ADP) measurements examined post-stimulus depolarization following current injection. Recordings were performed on cells both with and without axon initial segment (AIS) components to investigate the role of AIS morphology in intrinsic excitability.
intracellular_recordings (IntracellularRecordingsTable)
description: A table to group together a stimulus and response from a single electrode and a single simultaneous recording and for storing metadata about the intracellular recording.
category_tables
electrodes (IntracellularElectrodesTable)
description: Table for storing intracellular electrode related metadata
columns
electrode
Column for storing the reference to the intracellular electrode
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
electrode
id
0IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n
1IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n
2IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n
3IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n

... and 31 more row(s).

stimuli (IntracellularStimuliTable)
description: Table for storing intracellular stimulus related metadata
columns
stimulus
Column storing the reference to the recorded stimulus for the recording (rows)
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
stimulus
id
0(None, None, None)
1(None, None, None)
2(None, None, None)
3(0, 200000, CurrentClampStimulusSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampStimulusSeries at 0x138129163651344\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated current stimulus across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via stimulus_start_index and stimulus_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: N/A\\n timestamps: CurrentClampSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampSeries at 0x138129176502480\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: CurrentClampStimulusSeriesAisProtocolCSCell12\\n timestamp_link: (\\n CurrentClampStimulusSeriesAisProtocolCSCell12 <class 'pynwb.icephys.CurrentClampStimulusSeries'>\\n )\\n timestamps: <HDF5 dataset \"timestamps\": shape (4600000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n\\n timestamps_unit: seconds\\n unit: amperes\\n)

... and 31 more row(s).

responses (IntracellularResponsesTable)
description: Table for storing intracellular response related metadata
columns
response
Column storing the reference to the recorded response for the recording (rows)
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
response
id
0(0, 1000000, CurrentClampSeriesAisProtocolSFCell12 pynwb.icephys.CurrentClampSeries at 0x138129163652624\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (3000000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the SF protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: none\\n timestamps: <HDF5 dataset \"timestamps\": shape (3000000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)
1(1000000, 1000000, CurrentClampSeriesAisProtocolSFCell12 pynwb.icephys.CurrentClampSeries at 0x138129163652624\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (3000000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the SF protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: none\\n timestamps: <HDF5 dataset \"timestamps\": shape (3000000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)
2(2000000, 1000000, CurrentClampSeriesAisProtocolSFCell12 pynwb.icephys.CurrentClampSeries at 0x138129163652624\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (3000000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the SF protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: none\\n timestamps: <HDF5 dataset \"timestamps\": shape (3000000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)
3(0, 200000, CurrentClampSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampSeries at 0x138129176502480\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: CurrentClampStimulusSeriesAisProtocolCSCell12\\n timestamp_link: (\\n CurrentClampStimulusSeriesAisProtocolCSCell12 <class 'pynwb.icephys.CurrentClampStimulusSeries'>\\n )\\n timestamps: <HDF5 dataset \"timestamps\": shape (4600000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)

... and 31 more row(s).

columns
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
electrodesstimuliresponses
idelectrodeidstimulusidresponse
(intracellular_recordings, id)
00IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n0(None, None, None)0(0, 1000000, CurrentClampSeriesAisProtocolSFCell12 pynwb.icephys.CurrentClampSeries at 0x138129163652624\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (3000000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the SF protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: none\\n timestamps: <HDF5 dataset \"timestamps\": shape (3000000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)
11IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n1(None, None, None)1(1000000, 1000000, CurrentClampSeriesAisProtocolSFCell12 pynwb.icephys.CurrentClampSeries at 0x138129163652624\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (3000000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the SF protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: none\\n timestamps: <HDF5 dataset \"timestamps\": shape (3000000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)
22IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n2(None, None, None)2(2000000, 1000000, CurrentClampSeriesAisProtocolSFCell12 pynwb.icephys.CurrentClampSeries at 0x138129163652624\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (3000000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the SF protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: none\\n timestamps: <HDF5 dataset \"timestamps\": shape (3000000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)
33IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n3(0, 200000, CurrentClampStimulusSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampStimulusSeries at 0x138129163651344\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated current stimulus across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via stimulus_start_index and stimulus_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: N/A\\n timestamps: CurrentClampSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampSeries at 0x138129176502480\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: CurrentClampStimulusSeriesAisProtocolCSCell12\\n timestamp_link: (\\n CurrentClampStimulusSeriesAisProtocolCSCell12 <class 'pynwb.icephys.CurrentClampStimulusSeries'>\\n )\\n timestamps: <HDF5 dataset \"timestamps\": shape (4600000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n\\n timestamps_unit: seconds\\n unit: amperes\\n)3(0, 200000, CurrentClampSeriesAisProtocolCSCell12 pynwb.icephys.CurrentClampSeries at 0x138129176502480\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (4600000,), type \"<f8\">\\n description: Concatenated voltage response across all sweeps of the CS protocol for cell C12. Individual sweeps are exposed as rows of the IntracellularRecordingsTable, each pointing to a region of this series via response_start_index and response_index_count.\\n electrode: IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\\nFields:\\n cell_id: DopaminergicCell12\\n description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\\n device: MultiClamp700B pynwb.device.Device at 0x138129163685568\\nFields:\\n description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\\n manufacturer: Molecular Devices\\n\\n location: substantia nigra pars compacta\\n\\n gain: 1.0\\n interval: 1\\n offset: 0.0\\n resolution: -1.0\\n stimulus_description: CurrentClampStimulusSeriesAisProtocolCSCell12\\n timestamp_link: (\\n CurrentClampStimulusSeriesAisProtocolCSCell12 <class 'pynwb.icephys.CurrentClampStimulusSeries'>\\n )\\n timestamps: <HDF5 dataset \"timestamps\": shape (4600000,), type \"<f8\">\\n timestamps_unit: seconds\\n unit: volts\\n)

... and 31 more row(s).

icephys_simultaneous_recordings (SimultaneousRecordingsTable)
description: A table for grouping different intracellular recordings from theIntracellularRecordingsTable table together that were recorded simultaneously from different electrodes.
columns
recordings
Column with references to one or more rows in the IntracellularRecordingsTable table
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
recordings
id
0[0]
1[1]
2[2]
3[3]

... and 31 more row(s).

icephys_sequential_recordings (SequentialRecordingsTable)
description: A table for grouping different intracellular recording simultaneous_recordings from the SimultaneousRecordingsTable table together. This is typically used to group together simultaneous_recordings where the a sequence of stimuli of the same type with varying parameters have been presented in a sequence.
columns
simultaneous_recordings
Column with references to one or more rows in the SimultaneousRecordingsTable table
stimulus_type
Column storing the type of stimulus used for the sequential recording
protocol
Protocol abbreviation (SF=Spontaneous Firing, CS=Current Steps, ADP=After Depolarization)
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
simultaneous_recordingsstimulus_typeprotocol
id
0[0, 1, 2]spontaneous_firingSF
1[3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]current_stepsCS
2[26, 27, 28, 29, 30, 31, 32, 33, 34]after_depolarizationADP
icephys_repetitions (RepetitionsTable)
description: A table for grouping different intracellular recording sequential recordings together. With each SimultaneousRecording typically representing a particular type of stimulus, the RepetitionsTable table is typically used to group sets of stimuli applied in sequence.
columns
sequential_recordings
Column with references to one or more rows in the SequentialRecordingsTable table
cell_id
Cell identifier (e.g., C1, C2, ...)
ais_status
Axon initial segment component status
electrode_name
Name of the intracellular electrode used for this cell
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sequential_recordingscell_idais_statuselectrode_name
id
0[0, 1, 2]C12with AIS componentIntracellularElectrodeCell12
icephys_experimental_conditions (ExperimentalConditionsTable)
description: A table for grouping different intracellular recording repetitions together that belong to the same experimental conditions.
columns
repetitions
Column with references to one or more rows in the RepetitionsTable table
ais_component_status
Experimental condition based on axon initial segment component presence
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
repetitionsais_component_status
id
0[0]with AIS component
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x138129163694304\n", + "Fields:\n", + " acquisition: {\n", + " CurrentClampSeriesAisProtocolADPCell12 ,\n", + " CurrentClampSeriesAisProtocolCSCell12 ,\n", + " CurrentClampSeriesAisProtocolSFCell12 \n", + " }\n", + " devices: {\n", + " MultiClamp700B \n", + " }\n", + " epochs: epochs \n", + " experiment_description: Intracellular patch-clamp electrophysiology recordings from dopaminergic neurons in the substantia nigra pars compacta to investigate intrinsic firing properties and the role of axon initial segment (AIS) morphology in neuronal excitability. The experimental protocol consisted of three sequential current-clamp recordings to characterize intrinsic firing properties of dopaminergic neurons. First, spontaneous firing (SF) was recorded to establish baseline intrinsic firing patterns without current injection. Second, current steps (CS) protocols tested neuronal response to stepwise current injection of varying amplitudes. Third, after-depolarization (ADP) measurements examined post-stimulus depolarization following current injection. Recordings were performed on cells both with and without axon initial segment (AIS) components to investigate the role of AIS morphology in intrinsic excitability.\n", + " experimenter: ['Khaliq, Zayd' 'Sansalone, Lorenzo']\n", + " file_create_date: [datetime.datetime(2026, 4, 28, 8, 18, 21, 532977, tzinfo=tzoffset(None, -21600))]\n", + " icephys_electrodes: {\n", + " IntracellularElectrodeCell12 \n", + " }\n", + " icephys_experimental_conditions: experimental_conditions \n", + " icephys_repetitions: repetitions \n", + " icephys_sequential_recordings: sequential_recordings \n", + " icephys_simultaneous_recordings: simultaneous_recordings \n", + " identifier: subject3++20211103\n", + " institution: National Institute of Neurological Disorders and Stroke, National Institutes of Health\n", + " intervals: {\n", + " epochs ,\n", + " invalid_times \n", + " }\n", + " intracellular_recordings: intracellular_recordings \n", + " invalid_times: invalid_times \n", + " keywords: \n", + " lab: Khaliq Lab\n", + " protocol: The experimental protocol consisted of three sequential current-clamp recordings to characterize intrinsic firing properties of dopaminergic neurons. First, spontaneous firing (SF) was recorded to establish baseline intrinsic firing patterns without current injection. Second, current steps (CS) protocols tested neuronal response to stepwise current injection of varying amplitudes. Third, after-depolarization (ADP) measurements examined post-stimulus depolarization following current injection. Recordings were performed on cells both with and without axon initial segment (AIS) components to investigate the role of AIS morphology in intrinsic excitability.\n", + " session_description: Multi-cell intracellular current-clamp recordings from 1 Dopaminergic neuron(s) in Substantia Nigra of subject #3 on 3 Nov 2021\n", + " session_id: subject3++20211103\n", + " session_start_time: 2021-11-03 13:26:31.563000-04:00\n", + " stimulus: {\n", + " CurrentClampStimulusSeriesAisProtocolADPCell12 ,\n", + " CurrentClampStimulusSeriesAisProtocolCSCell12 \n", + " }\n", + " subject: subject pynwb.file.Subject at 0x138129163687248\n", + "Fields:\n", + " age: P11.5Y\n", + " age__reference: birth\n", + " description: Rhesus macaque subject with intracellular recordings from Dopaminergic neurons in Substantia Nigra\n", + " sex: F\n", + " species: Macaca mulatta\n", + " subject_id: #3\n", + "\n", + " timestamps_reference_time: 2021-11-03 13:26:31.563000-04:00" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Streaming from DANDI (dandiset is uploaded; auth still required while embargoed).\n", + "import h5py\n", + "import remfile\n", + "from dandi.dandiapi import DandiAPIClient\n", + "\n", + "dandiset_id = \"001693\"\n", + "nwbfile_path = \"sub-#3/sub-#3_ses-subject3++20211103_icephys.nwb\"\n", + "\n", + "with DandiAPIClient() as client:\n", + " # Needed only while the dandiset is embargoed; remove once it is public.\n", + " client.dandi_authenticate()\n", + " asset = client.get_dandiset(dandiset_id, \"draft\").get_asset_by_path(nwbfile_path)\n", + " # strip_query=False is important for embargoed assets: the signed query\n", + " # string is what authorizes the S3 read. strip_query=True returns a bare\n", + " # URL that 403s for non-public dandisets.\n", + " s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)\n", + "\n", + "rem_file = remfile.File(s3_url)\n", + "h5_file = h5py.File(rem_file, \"r\")\n", + "io = NWBHDF5IO(file=h5_file, load_namespaces=True)\n", + "nwbfile = io.read()\n", + "\n", + "nwbfile" + ] + }, + { + "cell_type": "markdown", + "id": "5077d018", + "metadata": {}, + "source": [ + "## Top-level session metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a622f84c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:01.599101Z", + "iopub.status.busy": "2026-04-28T17:01:01.598570Z", + "iopub.status.idle": "2026-04-28T17:01:01.612080Z", + "shell.execute_reply": "2026-04-28T17:01:01.611540Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "session_description : Multi-cell intracellular current-clamp recordings from 1 Dopaminergic neuron(s) in Substantia Nigra of subject #3 on 3 Nov 2021\n", + "session_start_time : 2021-11-03 13:26:31.563000-04:00\n", + "experimenter : ('Khaliq, Zayd', 'Sansalone, Lorenzo')\n", + "lab : Khaliq Lab\n", + "institution : National Institute of Neurological Disorders and Stroke, National Institutes of Health\n", + "\n", + "experiment_description:\n", + "Intracellular patch-clamp electrophysiology recordings from dopaminergic neurons in the substantia nigra pars compacta to investigate intrinsic firing properties and the role of axon initial segment (AIS) morphology in neuronal excitability. The experimental protocol consisted of three sequential current-clamp recordings to characterize intrinsic firing properties of dopaminergic neurons. First, spontaneous firing (SF) was recorded to establish baseline intrinsic firing patterns without current injection. Second, current steps (CS) protocols tested neuronal response to stepwise current injection of varying amplitudes. Third, after-depolarization (ADP) measurements examined post-stimulus depolarization following current injection. Recordings were performed on cells both with and without axon initial segment (AIS) components to investigate the role of AIS morphology in intrinsic excitability.\n" + ] + } + ], + "source": [ + "print(\"session_description :\", nwbfile.session_description)\n", + "print(\"session_start_time :\", nwbfile.session_start_time)\n", + "print(\"experimenter :\", nwbfile.experimenter)\n", + "print(\"lab :\", nwbfile.lab)\n", + "print(\"institution :\", nwbfile.institution)\n", + "print()\n", + "print(\"experiment_description:\")\n", + "print(nwbfile.experiment_description)" + ] + }, + { + "cell_type": "markdown", + "id": "25c2750a", + "metadata": {}, + "source": [ + "## Subject\n", + "\n", + "Ages are stored as ISO 8601 durations (e.g. `P9.8Y` for 9.8 years). For subjects whose age was not recorded in the source spreadsheet (currently #4 and #6), we use the open-ended range `P0Y/` - meaning \"0 years or older\" - to represent total uncertainty without making an unverified claim about a lower bound. See `src/khaliq_lab_to_nwb/conversion_abf/conversion_notes.md` for details." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4238c73", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:01.613201Z", + "iopub.status.busy": "2026-04-28T17:01:01.613082Z", + "iopub.status.idle": "2026-04-28T17:01:01.625139Z", + "shell.execute_reply": "2026-04-28T17:01:01.624674Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

subject (Subject)

age: P11.5Y
age__reference: birth
description: Rhesus macaque subject with intracellular recordings from Dopaminergic neurons in Substantia Nigra
sex: F
species: Macaca mulatta
subject_id: #3
" + ], + "text/plain": [ + "subject pynwb.file.Subject at 0x138129163687248\n", + "Fields:\n", + " age: P11.5Y\n", + " age__reference: birth\n", + " description: Rhesus macaque subject with intracellular recordings from Dopaminergic neurons in Substantia Nigra\n", + " sex: F\n", + " species: Macaca mulatta\n", + " subject_id: #3" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.subject" + ] + }, + { + "cell_type": "markdown", + "id": "286a7c8e", + "metadata": {}, + "source": [ + "## Device and IntracellularElectrode\n", + "\n", + "One device is shared across all recordings; each cell has its own `IntracellularElectrode` entry." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e1ca76cc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:01.626525Z", + "iopub.status.busy": "2026-04-28T17:01:01.626408Z", + "iopub.status.idle": "2026-04-28T17:01:01.638681Z", + "shell.execute_reply": "2026-04-28T17:01:01.638295Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

MultiClamp700B (Device)

description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
" + ], + "text/plain": [ + "MultiClamp700B pynwb.device.Device at 0x138129163685568\n", + "Fields:\n", + " description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\n", + " manufacturer: Molecular Devices" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = nwbfile.devices[\"MultiClamp700B\"]\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8c0a3e58", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:01.639741Z", + "iopub.status.busy": "2026-04-28T17:01:01.639641Z", + "iopub.status.idle": "2026-04-28T17:01:01.651920Z", + "shell.execute_reply": "2026-04-28T17:01:01.651506Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

IntracellularElectrodeCell12 (IntracellularElectrode)

description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)
location: substantia nigra pars compacta
device (Device)
description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode
manufacturer: Molecular Devices
cell_id: DopaminergicCell12
" + ], + "text/plain": [ + "IntracellularElectrodeCell12 pynwb.icephys.IntracellularElectrode at 0x138129163685904\n", + "Fields:\n", + " cell_id: DopaminergicCell12\n", + " description: Intracellular electrode for Dopaminergic neuron (cell C12, with AIS component)\n", + " device: MultiClamp700B pynwb.device.Device at 0x138129163685568\n", + "Fields:\n", + " description: Molecular Devices MultiClamp 700B amplifier used for whole-cell patch-clamp recordings in current-clamp mode\n", + " manufacturer: Molecular Devices\n", + "\n", + " location: substantia nigra pars compacta" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.icephys_electrodes[\"IntracellularElectrodeCell12\"]" + ] + }, + { + "cell_type": "markdown", + "id": "94e0ae6b", + "metadata": {}, + "source": [ + "## Experimental protocols\n", + "\n", + "Each session ran three sequential current-clamp protocols on every cell:\n", + "\n", + "- **SF (Spontaneous Firing)** - baseline intrinsic firing with no current injection. The cell runs free; we record its natural pacemaking pattern. Typically a single continuous sweep, no stimulus series in NWB.\n", + "- **CS (Current Steps)** - stepwise current injection of varying amplitudes. Each step is one sweep (~4 s long here), separated by a few seconds of recovery before the next step. Tests excitability: f-I relationship, rheobase, gain.\n", + "- **ADP (After Depolarization)** - brief depolarizing pulses delivered after holding at a hyperpolarized potential. Probes rebound depolarization and post-stimulus membrane dynamics.\n", + "\n", + "Cells are partitioned into \"with AIS component\" vs \"without AIS component\" - the experimental condition the dataset compares.\n", + "\n", + "### Protocol epochs\n", + "\n", + "`nwbfile.epochs` is a `TimeIntervals` table with **one row per `(cell, protocol)` pair**. Each row spans the full window of that protocol on that cell - from the first sweep's start to the last sweep's end, inter-sweep gaps included. Custom columns:\n", + "\n", + "- `protocol` - abbreviation (SF / CS / ADP)\n", + "- `cell_id` - the cell this protocol was run on\n", + "- `ais_status` - \"with AIS component\" / \"without AIS component\"\n", + "- `tags` - descriptive labels for the protocol type\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ff72e26c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:01.653186Z", + "iopub.status.busy": "2026-04-28T17:01:01.653053Z", + "iopub.status.idle": "2026-04-28T17:01:01.668677Z", + "shell.execute_reply": "2026-04-28T17:01:01.668210Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_timestop_timeprotocolcell_idais_statustags
id
00.00059.99998SFC12with AIS component[spontaneous_firing, baseline]
199.693323.69298CSC12with AIS component[current_steps, excitability_test]
2338.303423.30298ADPC12with AIS component[after_depolarization, post_stimulus]
\n", + "
" + ], + "text/plain": [ + " start_time stop_time protocol cell_id ais_status \\\n", + "id \n", + "0 0.000 59.99998 SF C12 with AIS component \n", + "1 99.693 323.69298 CS C12 with AIS component \n", + "2 338.303 423.30298 ADP C12 with AIS component \n", + "\n", + " tags \n", + "id \n", + "0 [spontaneous_firing, baseline] \n", + "1 [current_steps, excitability_test] \n", + "2 [after_depolarization, post_stimulus] " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.epochs.to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "69d4711a", + "metadata": {}, + "source": [ + "## Acquisition and stimulus: how the data is stored\n", + "\n", + "The recordings are split between two NWB containers:\n", + "\n", + "- `nwbfile.acquisition` - what was *measured*: voltage traces (`CurrentClampSeries`)\n", + "- `nwbfile.stimulus` - what was *commanded*: current waveforms (`CurrentClampStimulusSeries`)\n", + "\n", + "For each `(cell, protocol)` combination, all sweeps from that protocol are concatenated into **one** `CurrentClampSeries` (voltage, in volts) and **one** `CurrentClampStimulusSeries` (current, in amperes). SF recordings have no stimulus series because no current is injected.\n", + "\n", + "Both series carry an explicit per-sample `timestamps` array (in seconds from session start). Within a sweep, sampling is regular at 50 kHz; the gaps *between* sweeps - typically a few seconds while the experimenter advances the protocol - are real wall-clock pauses that the timestamps preserve. The stimulus series doesn't store its own timestamps array; it soft-links to the response's, so the timestamps dataset is only on disk once.\n", + "\n", + "You can read either series directly:\n", + "\n", + "```python\n", + "v_series = nwbfile.acquisition[\"CurrentClampSeriesAisProtocolCSCell12\"]\n", + "voltage = v_series.data[:] # all samples, in volts\n", + "timestamps = v_series.timestamps[:] # absolute time of each sample, in seconds\n", + "```\n", + "\n", + "Below we list the series in the file, then plot one full `(cell, protocol)` pair so the concatenation and inter-sweep gaps are visible.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "99eb6020", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:01.669941Z", + "iopub.status.busy": "2026-04-28T17:01:01.669783Z", + "iopub.status.idle": "2026-04-28T17:01:01.682697Z", + "shell.execute_reply": "2026-04-28T17:01:01.682199Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Acquisition (voltage) series : 3 # one per (cell, protocol)\n", + "Stimulus (current) series : 2 # SF has no stimulus\n", + "\n", + "Acquisition series:\n", + " CurrentClampSeriesAisProtocolADPCell12\n", + " CurrentClampSeriesAisProtocolCSCell12\n", + " CurrentClampSeriesAisProtocolSFCell12\n", + "\n", + "Stimulus series:\n", + " CurrentClampStimulusSeriesAisProtocolADPCell12\n", + " CurrentClampStimulusSeriesAisProtocolCSCell12\n" + ] + } + ], + "source": [ + "print(\n", + " f\"Acquisition (voltage) series : {len(nwbfile.acquisition)} # one per (cell, protocol)\"\n", + ")\n", + "print(\n", + " f\"Stimulus (current) series : {len(nwbfile.stimulus)} # SF has no stimulus\"\n", + ")\n", + "print()\n", + "print(\"Acquisition series:\")\n", + "for name in nwbfile.acquisition:\n", + " print(f\" {name}\")\n", + "print()\n", + "print(\"Stimulus series:\")\n", + "for name in nwbfile.stimulus:\n", + " print(f\" {name}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c6838824", + "metadata": {}, + "source": [ + "### Visualize one full (cell, protocol) series per protocol\n", + "\n", + "Below we plot the full concatenated trace for each of the three protocols on this cell. In every case the voltage axis is clipped to the physiological range (-120 to 60 mV) so saturation artifacts (flagged in `invalid_times`, shown later) don't blow up the y-axis. Flat segments between blocks of activity are real wall-clock waiting periods preserved by the timestamps.\n" + ] + }, + { + "cell_type": "markdown", + "id": "7efac61a", + "metadata": {}, + "source": [ + "#### SF - spontaneous firing\n", + "\n", + "No current injection (`I = 0`), so there is no `CurrentClampStimulusSeries` and we only plot the voltage. This trace shows the cell's autonomous pacemaking - the regular train of action potentials it generates with no external drive.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "41c40d73", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:01.684253Z", + "iopub.status.busy": "2026-04-28T17:01:01.684112Z", + "iopub.status.idle": "2026-04-28T17:01:03.775206Z", + "shell.execute_reply": "2026-04-28T17:01:03.774679Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# SF - spontaneous firing. No stimulus series; plot voltage only.\n", + "sf_response_name = next(n for n in nwbfile.acquisition if \"ProtocolSF\" in n)\n", + "v_series = nwbfile.acquisition[sf_response_name]\n", + "\n", + "t = np.asarray(v_series.timestamps[:])\n", + "v = np.asarray(v_series.data[:]) * v_series.conversion\n", + "\n", + "fig, ax_v = plt.subplots(figsize=(11, 3), constrained_layout=True)\n", + "ax_v.plot(t, v * 1e3, color=\"C0\", linewidth=0.4)\n", + "ax_v.set_ylabel(\"Vm (mV)\")\n", + "ax_v.set_ylim(-120, 60)\n", + "ax_v.set_xlabel(\"Time (seconds, from session start)\")\n", + "ax_v.set_title(f\"{v_series.name} (full series, {len(t):,} samples)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a615e104", + "metadata": {}, + "source": [ + "#### ADP - after-depolarization\n", + "\n", + "Each sweep starts with a hyperpolarizing pre-conditioning pulse, then the cell is released (or briefly depolarized) and the post-stimulus dynamics are recorded. Across sweeps, the holding voltage is varied. Both voltage response and the injected current command are present.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2d511ee4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:03.776616Z", + "iopub.status.busy": "2026-04-28T17:01:03.776487Z", + "iopub.status.idle": "2026-04-28T17:01:05.365627Z", + "shell.execute_reply": "2026-04-28T17:01:05.365229Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ADP - after-depolarization. Has both response and stimulus.\n", + "adp_response_name = next(n for n in nwbfile.acquisition if \"ProtocolADP\" in n)\n", + "adp_stimulus_name = adp_response_name.replace(\n", + " \"CurrentClampSeries\", \"CurrentClampStimulusSeries\"\n", + ")\n", + "\n", + "v_series = nwbfile.acquisition[adp_response_name]\n", + "i_series = nwbfile.stimulus[adp_stimulus_name]\n", + "\n", + "t = np.asarray(v_series.timestamps[:])\n", + "v = np.asarray(v_series.data[:]) * v_series.conversion\n", + "i = np.asarray(i_series.data[:]) * i_series.conversion\n", + "\n", + "fig, (ax_v, ax_i) = plt.subplots(2, 1, figsize=(11, 5), sharex=True)\n", + "ax_v.plot(t, v * 1e3, color=\"C0\", linewidth=0.4)\n", + "ax_v.set_ylabel(\"Vm (mV)\")\n", + "ax_v.set_ylim(-120, 60)\n", + "ax_v.set_title(f\"{v_series.name} (full concatenated series, {len(t):,} samples)\")\n", + "ax_i.plot(t, i * 1e12, color=\"C1\", linewidth=0.4)\n", + "ax_i.set_ylabel(\"I (pA)\")\n", + "ax_i.set_xlabel(\"Time (seconds, from session start)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "210589b0", + "metadata": {}, + "source": [ + "#### CS - current steps\n", + "\n", + "A series of square current pulses, one per sweep, scanning depolarizing and hyperpolarizing amplitudes (here roughly -165 pA to +200 pA in steps of a few tens of pA). Each step lasts about 4 seconds, separated by a few seconds of recovery before the next sweep. Both voltage response and the injected current command are recorded, so this cell shows two stacked panels.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a8bc6611", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:05.366995Z", + "iopub.status.busy": "2026-04-28T17:01:05.366854Z", + "iopub.status.idle": "2026-04-28T17:01:08.519182Z", + "shell.execute_reply": "2026-04-28T17:01:08.518804Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# CS - current steps. Has both voltage response and stimulus current command.\n", + "cs_response_name = next(n for n in nwbfile.acquisition if \"ProtocolCS\" in n)\n", + "cs_stimulus_name = cs_response_name.replace(\"CurrentClampSeries\", \"CurrentClampStimulusSeries\")\n", + "\n", + "v_series = nwbfile.acquisition[cs_response_name]\n", + "i_series = nwbfile.stimulus[cs_stimulus_name]\n", + "\n", + "t = np.asarray(v_series.timestamps[:])\n", + "v = np.asarray(v_series.data[:]) * v_series.conversion\n", + "i = np.asarray(i_series.data[:]) * i_series.conversion\n", + "\n", + "# Mask out any saturation artifacts flagged in invalid_times so they don't distract.\n", + "# matplotlib breaks the line wherever values are NaN, leaving a clean gap.\n", + "# Apply the same mask to both response and stimulus so the gap appears in both panels.\n", + "v_plot = v.copy()\n", + "i_plot = i.copy()\n", + "if nwbfile.invalid_times is not None:\n", + " for row in nwbfile.invalid_times.to_dataframe().itertuples():\n", + " if row.time_series_name == cs_response_name:\n", + " mask = (t >= row.start_time) & (t <= row.stop_time)\n", + " v_plot[mask] = np.nan\n", + " i_plot[mask] = np.nan\n", + "\n", + "fig, (ax_v, ax_i) = plt.subplots(2, 1, figsize=(11, 5), sharex=True)\n", + "ax_v.plot(t, v_plot * 1e3, color=\"C0\", linewidth=0.4)\n", + "ax_v.set_ylabel(\"Vm (mV)\")\n", + "ax_v.set_ylim(-120, 60)\n", + "ax_v.set_title(f\"{v_series.name} (full concatenated series, {len(t):,} samples)\")\n", + "ax_i.plot(t, i_plot * 1e12, color=\"C1\", linewidth=0.4)\n", + "ax_i.set_ylabel(\"I (pA)\")\n", + "ax_i.set_xlabel(\"Time (seconds, from session start)\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8ce02551", + "metadata": {}, + "source": [ + "Note: where the voltage trace shows a small gap, samples flagged in `nwbfile.invalid_times` (voltage saturation, beyond the amplifier's usable range) have been masked out so they don't distract from the physiological signal. The full saturation interval is still preserved in the underlying data and is shown explicitly later in the notebook.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b58a2377", + "metadata": {}, + "source": [ + "## IntracellularRecordings table and sweep extraction\n", + "\n", + "`nwbfile.intracellular_recordings` is the **per-sweep index** for the file. It's an `AlignedDynamicTable` that joins three sub-tables row by row:\n", + "\n", + "| sub-table | column | what it holds |\n", + "|---|---|---|\n", + "| `electrodes` | `electrode` | reference to the `IntracellularElectrode` (which cell was being recorded) |\n", + "| `stimuli` | `stimulus` | a `(start_index, count, series)` triple pointing into the stimulus series. `(None, None, None)` for SF sweeps with no stimulus |\n", + "| `responses` | `response` | the analogous triple pointing into the response series |\n", + "\n", + "**One row of this table is one sweep.** The `(start_index, count, series)` triple is NWB's `TimeSeriesReference` - a typed pointer saying \"samples `[start_index, start_index + count)` of `series` are this sweep.\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "315ff187", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:08.520542Z", + "iopub.status.busy": "2026-04-28T17:01:08.520410Z", + "iopub.status.idle": "2026-04-28T17:01:08.548195Z", + "shell.execute_reply": "2026-04-28T17:01:08.547764Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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electrodesstimuliresponses
idelectrodeidstimulusidresponse
(intracellular_recordings, id)
00IntracellularElectrodeCell12 pynwb.icephys.Int...0(None, None, None)0(0, 1000000, CurrentClampSeriesAisProtocolSFCe...
11IntracellularElectrodeCell12 pynwb.icephys.Int...1(None, None, None)1(1000000, 1000000, CurrentClampSeriesAisProtoc...
22IntracellularElectrodeCell12 pynwb.icephys.Int...2(None, None, None)2(2000000, 1000000, CurrentClampSeriesAisProtoc...
33IntracellularElectrodeCell12 pynwb.icephys.Int...3(0, 200000, CurrentClampStimulusSeriesAisProto...3(0, 200000, CurrentClampSeriesAisProtocolCSCel...
44IntracellularElectrodeCell12 pynwb.icephys.Int...4(200000, 200000, CurrentClampStimulusSeriesAis...4(200000, 200000, CurrentClampSeriesAisProtocol...
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" + ], + "text/plain": [ + " electrodes \\\n", + " id \n", + "(intracellular_recordings, id) \n", + "0 0 \n", + "1 1 \n", + "2 2 \n", + "3 3 \n", + "4 4 \n", + "\n", + " \\\n", + " electrode \n", + "(intracellular_recordings, id) \n", + "0 IntracellularElectrodeCell12 pynwb.icephys.Int... \n", + "1 IntracellularElectrodeCell12 pynwb.icephys.Int... \n", + "2 IntracellularElectrodeCell12 pynwb.icephys.Int... \n", + "3 IntracellularElectrodeCell12 pynwb.icephys.Int... \n", + "4 IntracellularElectrodeCell12 pynwb.icephys.Int... \n", + "\n", + " stimuli \\\n", + " id \n", + "(intracellular_recordings, id) \n", + "0 0 \n", + "1 1 \n", + "2 2 \n", + "3 3 \n", + "4 4 \n", + "\n", + " \\\n", + " stimulus \n", + "(intracellular_recordings, id) \n", + "0 (None, None, None) \n", + "1 (None, None, None) \n", + "2 (None, None, None) \n", + "3 (0, 200000, CurrentClampStimulusSeriesAisProto... \n", + "4 (200000, 200000, CurrentClampStimulusSeriesAis... \n", + "\n", + " responses \\\n", + " id \n", + "(intracellular_recordings, id) \n", + "0 0 \n", + "1 1 \n", + "2 2 \n", + "3 3 \n", + "4 4 \n", + "\n", + " \n", + " response \n", + "(intracellular_recordings, id) \n", + "0 (0, 1000000, CurrentClampSeriesAisProtocolSFCe... \n", + "1 (1000000, 1000000, CurrentClampSeriesAisProtoc... \n", + "2 (2000000, 1000000, CurrentClampSeriesAisProtoc... \n", + "3 (0, 200000, CurrentClampSeriesAisProtocolCSCel... \n", + "4 (200000, 200000, CurrentClampSeriesAisProtocol... " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.intracellular_recordings.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "id": "46ae7276", + "metadata": {}, + "source": [ + "### Extract one sweep\n", + "\n", + "To pull a single sweep, look up its IR row and slice the underlying series with the triple. Below we filter the table to CS sweeps and plot the middle one.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ff8fdd1a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:08.549423Z", + "iopub.status.busy": "2026-04-28T17:01:08.549247Z", + "iopub.status.idle": "2026-04-28T17:01:08.841261Z", + "shell.execute_reply": "2026-04-28T17:01:08.840677Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Walk the IR table directly: each row's responses.response is a (start_index, count, series) triple.\n", + "# Filter to rows whose response series is a CS series and pick a sweep in the middle.\n", + "ir_df = nwbfile.intracellular_recordings.to_dataframe()\n", + "response_col = ir_df[(\"responses\", \"response\")]\n", + "cs_mask = response_col.apply(lambda ref: \"ProtocolCS\" in ref[2].name)\n", + "cs_rows = ir_df[cs_mask]\n", + "\n", + "sweep_pos = len(cs_rows) // 2\n", + "middle = cs_rows.iloc[sweep_pos]\n", + "v_start, v_count, v_series = middle[(\"responses\", \"response\")]\n", + "i_start, i_count, i_series = middle[(\"stimuli\", \"stimulus\")]\n", + "\n", + "# Slice the concatenated series to extract just this sweep\n", + "voltage = np.asarray(v_series.data[v_start : v_start + v_count])\n", + "current = np.asarray(i_series.data[i_start : i_start + i_count])\n", + "t_v = np.asarray(v_series.timestamps[v_start : v_start + v_count])\n", + "t_i = np.asarray(i_series.timestamps[i_start : i_start + i_count])\n", + "\n", + "fig, (ax_v, ax_i) = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n", + "ax_v.plot(t_v, voltage * 1e3, color=\"C0\", linewidth=0.7)\n", + "ax_v.set_ylabel(\"Vm (mV)\")\n", + "ax_v.set_title(f\"{v_series.name} (sweep {sweep_pos + 1} of {len(cs_rows)})\")\n", + "ax_i.plot(t_i, current * 1e12, color=\"C1\", linewidth=0.7)\n", + "ax_i.set_ylabel(\"I (pA)\")\n", + "ax_i.set_xlabel(\"Time (seconds, from session start)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4b1a3e39", + "metadata": {}, + "source": [ + "## Invalid times - voltage saturation artifacts\n", + "\n", + "`nwbfile.invalid_times` flags intervals where the recorded voltage exceeded the amplifier's usable range (detected as absolute voltage beyond 200 mV). These samples do not reflect true membrane potential and should be excluded from analysis.\n", + "\n", + "Custom columns:\n", + "\n", + "- `time_series` - reference to the affected `CurrentClampSeries`\n", + "- `time_series_name` - the name of that series (convenient for grouping)\n", + "- `voltage_min`, `voltage_max` - extremes observed inside the interval (V)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7ce389a4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:08.842475Z", + "iopub.status.busy": "2026-04-28T17:01:08.842344Z", + "iopub.status.idle": "2026-04-28T17:01:08.858114Z", + "shell.execute_reply": "2026-04-28T17:01:08.857696Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_timestop_timetime_series_namevoltage_minvoltage_max
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0281.37236283.68658CurrentClampSeriesAisProtocolCSCell12-0.9786380.994568
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" + ], + "text/plain": [ + " start_time stop_time time_series_name voltage_min \\\n", + "id \n", + "0 281.37236 283.68658 CurrentClampSeriesAisProtocolCSCell12 -0.978638 \n", + "\n", + " voltage_max \n", + "id \n", + "0 0.994568 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "invalid_df = nwbfile.invalid_times.to_dataframe()\n", + "invalid_df[\n", + " [\"start_time\", \"stop_time\", \"time_series_name\", \"voltage_min\", \"voltage_max\"]\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "98e8188d", + "metadata": {}, + "source": [ + "### Visualizing a saturation interval\n", + "\n", + "We overlay the invalid interval on the affected voltage trace to make the artifact visible." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d2138b10", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T17:01:08.859496Z", + "iopub.status.busy": "2026-04-28T17:01:08.859383Z", + "iopub.status.idle": "2026-04-28T17:01:09.438171Z", + "shell.execute_reply": "2026-04-28T17:01:09.437743Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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bNmLGjBni1KlTWWLL73rzul9q2uTV4yRznV5Vs2ZN0apVqzzFIYQQv/32mwAg1q5dm+dl8sLCwkKMHj061zLZfbYsXLhQyGQyrc+LnPar7N4fIbL/DNd8vkybNq3A4xAi63dD9+7dhUKhEEFBQdK0p0+fCnNzc9GyZUtpmuaY9vT0lPZxIYSYMGGC0NPTE9HR0dlu79XlX/0MyG4/ebWeqampolatWuL999+Xpl26dEkAEOPHj9cq6+Pjk6WOXbp0ESYmJuLJkyfStMDAQKGvr6/1PoWEhAg9PT2xYMECrXVev35d6OvrS9M1nzV169YVKSkpUrnVq1cLAHnar52dnYW3t7f0+m3f35z2m1OnTgkAYtOmTVrTDxw4oDVd83nVqVMnre1//vnnAoBWrESlAYeXE73DNMNjzc3NdbaNESNGZDvdxcUFXl5eWtO2bdsGDw8PWFlZ4fnz59LD09MTKpUKJ0+e1Crft29fWFlZSa89PDwAAPfv339tXAVR98znoMfExOD58+do1aoV7t+/j5iYGK2yNWrUQNOmTaXXmt6c999/H05OTlmmZ1eHMWPGSM9lMhnGjBmD1NRUHDlyRIrn/PnzmDJlCoCM4YO+vr6wt7fH2LFjkZKSAgB48eIF/vnnH/Tp0wdxcXHS+xwZGQkvLy8EBgZqDY8GgGHDhr32nOnDhw8jOjoa/fv312o/PT09NG7cGMeOHZPiVCgUOH78eJbhhpmlp6dj69at6Nu3r9RrrxnOv2nTplxj0fQO7dmzB2lpabmWXbt2LaytrWFjY4PGjRtLwy3Hjx+vVc7b21urzS9evIjw8HCMGjVK67zVTp06oVq1anm6FsK+ffugp6eHcePGaU2fNGkShBDYv38/gIzz9dVqNWbOnJlluLTmvTly5AhSU1Mxfvx4rTLDhg2DUqnMNZ78Hg9Dhw7FgQMH0Lp1a5w+fRrz5s2Dh4cHKleujLNnz77Ret9kv9SVO3fuYPTo0WjatKk0TLmgWFpa4vz587neTSDzfpaQkIDnz5+jWbNmEEJkOf2koGR3gcKCjkOlUuHQoUPo3r07XF1dpen29vYYMGAATp8+Le0zGp988onWqB0PDw+oVCo8ePAg39vPSeZ6RkVFISYmBh4eHlpD/g8cOAAA0qgkjbFjx2q9VqlUOHLkCLp376514cpKlSpluUbEn3/+CbVajT59+mh9ZtrZ2aFy5crSZ6bms2bEiBFap0H4+PjAwsLirer+tu/vq/vNtm3bYGFhgQ8++ECrTg0aNICZmZlUJ83n1dixY7W2/+rnLlFpweHlRO8wzTm+cXFxOtuGi4tLnqcHBgbi2rVr2Q7BA7JeoClzsgpASsBzS+Q0Mtf9TS+0c+bMGcyaNQt+fn5ITEzUmhcTE6P1z9CrsWrmvTosUDP91TrI5XKtf1KBjAuAAdA6j9nCwgKLFy/G4sWL8eDBAxw9ehRLlizBypUrYWFhgfnz5+PevXsQQmDGjBmYMWNGtnULDw9H+fLlpdc5tWNmgYGBADIS4+xo3nNDQ0N8/fXXmDRpEmxtbdGkSRN07twZgwcPhp2dnVT+0KFDiIiIwHvvvYd79+5J09u0aYPff/8dX3/9dY7n67Zq1Qoffvgh5syZg++++w6tW7dG9+7dMWDAgCwXFOrWrRvGjBkDmUwGc3Nz6bZRr3r1PdD8U1q1atUsZatVq4bTp09nG9ur63BwcMiSlFavXl1rG0FBQZDL5ahRo0au68ouHoVCAVdX11z/iX6T48HLywteXl5ITEzEpUuXsHXrVvz444/o3Lkz7ty5Axsbm3yt9032S10ICwtDp06dYGFhge3bt7/2x6akpKQsP7Jl3o9ftXjxYnh7e8PR0RENGjRAx44dMXjwYK3j++HDh5g5cyb++uuvLJ8Fr26rIOjr62d7mkBBxxEREYHExMRsj5nq1atDrVbj0aNHWkP63+ZzPq/27NmD+fPnw9/fX/pxEoBWMvjgwQPI5fIsnwOvnoYSHh6OpKSkLNOzKxsYGAghBCpXrpxtXJph2Jpj99VyBgYGWb4X8utt3t/s9pvAwEDExMRoXdshM833eE51sra21voxnai0YNJN9A5TKpVwcHDAjRs38lQ+p3OEVSpVjsvkdEXy7Kar1Wp88MEHmDp1arbLaJJMjZz+GRbZXIDqVdWqVQMAXL9+Xeohz4+goCC0bdsW1apVw9KlS+Ho6AiFQoF9+/bhu+++07rwW26xvk0dXsfZ2RlDhw5Fjx494Orqik2bNmH+/PlSbJMnT84y2kDj1X8O83Jlec16f/vtt2yTjsy3DRo/fjy6dOmCXbt24eDBg5gxYwYWLlyIf/75B/Xq1QMAqTe7T58+2W7vxIkTaNOmTbbzZDIZtm/fjnPnzuHvv//GwYMHMXToUHz77bc4d+4czMzMpLIVKlTI0y2FCvvq+oXpbY4HExMTeHh4wMPDA+XKlcOcOXOwf/9+eHt752u9b7JfFrSYmBh06NAB0dHROHXqVJ5usbd161YMGTJEa1pux2+fPn3g4eGBnTt34tChQ/jmm2/w9ddf488//0SHDh2gUqnwwQcf4MWLF/jss89QrVo1mJqa4smTJ/Dx8cny2ZKd/H5WGxoaZvkBqyDiKAi6/IwEMi4W17VrV7Rs2RKrVq2Cvb09DAwMsH79+mwv2liQ1Go1ZDIZ9u/fn209M39O6crbvL/Z7TdqtTrX0Ug5/ahOVNox6SZ6x3Xu3BmrV6+Gn5+f1vDn7Gh+fX71SsgFNczPzc0N8fHxBXpP1Zz++ezSpQsWLlyIjRs3vlHS/ffffyMlJQV//fWXVk+BZuhcQVOr1bh//77WDw93794FgCy3z3qVlZUV3NzcpB9XND0jBgYGBfpeay5UZGNjk6f1urm5YdKkSZg0aRICAwNRt25dfPvtt9i4cSMSEhKwe/du9O3bF7169cqy7Lhx47Bp06Yck26NJk2aoEmTJliwYAE2b96MgQMHYsuWLfj444/frJKZaC5cFxAQkKV3PyAgQJoP5LwfOjs748iRI4iLi9Pq7b5z547WNtzc3KBWq3Hr1i3UrVv3tfFk7v1KTU1FcHBwrm3ytseDRsOGDQEAoaGh+V6vrvbLvEpOTkaXLl1w9+5dHDlyJNdRBZl5eXnh8OHD+dqWvb09Ro0ahVGjRiE8PBz169fHggUL0KFDB1y/fh13797FL7/8gsGDB0vLZLeNnPargvisLog4XmVtbQ0TExMEBARkmXfnzh3I5fK3uijYm9ixYweMjIxw8OBBrVEw69ev1yrn7OwMtVqN4OBgrd7ZzKNwgIzPPyMjoyzTsyvr5uYGIQRcXFyy/Kj86raBjF7kzJ81aWlpCA4ORp06dfJQ08Lh5uaGI0eOoHnz5rn+UJm5Tpk/ryIiIgp0FANRccFzuonecVOnToWpqSk+/vhjPHv2LMv8oKAgLF++HEBGz3i5cuWynFu9atWqAomlT58+8PPzw8GDB7PMi46ORnp6er7XqRkm/Oo/n02bNkX79u3x888/Y9euXVmWS01NxeTJk3Ncr6Z3IHNvQExMTJZ/1ArSypUrpedCCKxcuRIGBgZo27YtAODq1at4/vx5luUePHiAW7duSUM6bWxs0Lp1a/z0009ScpRZXm4Xkx0vLy8olUp89dVX2Z5HrVlvYmIikpOTtea5ubnB3NxcGtq5c+dOJCQkYPTo0ejVq1eWR+fOnbFjxw6toaCZRUVFZemp0SSrOS2TXw0bNoSNjQ1+/PFHrXXu378ft2/f1rqqcU77YceOHaFSqbTaFgC+++47yGQy6RzQ7t27Qy6XY+7cuVl6GDX19PT0hEKhwP/+9z+tuq9duxYxMTHZXmVZI7/Hw9GjR7Ndz759+wD8N8Q9P+vV1X6ZFyqVCn379oWfnx+2bdv22h8gM7O3t4enp6fWI7ftvDos28bGBg4ODtI+lN1nixBC+hzOLKf9ytnZGXp6em/1WV0QcWS3znbt2mH37t1ap8U8e/YMmzdvRosWLbLc2lDX9PT0IJPJtEYBhISEZNlfNaMvXn0PV6xYkWV9np6e2LVrl9Z5+/fu3ZOu0aDRs2dP6OnpYc6cOVk+r4QQiIyMBJDxWWNtbY0ff/xR68reGzZseO17Xtj69OkDlUqFefPmZZmXnp4uxevp6QkDAwOsWLFCq+6v3qWEqLRgTzfRO87NzQ2bN29G3759Ub16dQwePBi1atVCamoqzp49i23btmnd2/Pjjz/GokWL8PHHH6Nhw4Y4efKk1OP6tqZMmYK//voLnTt3ho+PDxo0aICEhARcv34d27dvR0hICMqVK5evdTZo0ABARs+ol5cX9PT00K9fPwAZt9Zq164devbsiS5duqBt27YwNTVFYGAgtmzZgtDQ0Bzv1d2uXTsoFAp06dIFw4cPR3x8PNasWQMbG5tsE4a3ZWRkhAMHDsDb2xuNGzfG/v37sXfvXnz++efScL3Dhw9j1qxZ6Nq1K5o0aSLd23vdunVISUnRuo/s999/jxYtWsDd3R3Dhg2Dq6srnj17Bj8/Pzx+/BhXr17Nd4xKpRI//PADBg0ahPr166Nfv36wtrbGw4cPsXfvXjRv3hwrV67E3bt30bZtW/Tp0wc1atSAvr4+du7ciWfPnklts2nTJpQtWzbHW9l17doVa9aswd69e9GzZ88s83/55ResWrUKPXr0gJubG+Li4rBmzRoolUp07Ngx33XLjoGBAb7++msMGTIErVq1Qv/+/aVbhlWsWBETJkyQyua0H3bp0gVt2rTBF198gZCQENSpUweHDh3C7t27MX78eGn0QKVKlfDFF19IFyzr2bMnDA0NceHCBTg4OGDhwoWwtrbG9OnTMWfOHLRv3x5du3ZFQEAAVq1ahUaNGuGjjz7KtT75OR66desGFxcXdOnSBW5ubkhISMCRI0fw999/o1GjRujSpcsbrbeg98uTJ09KiWdERAQSEhIwf/58AEDLli3RsmVLABkXrvvrr7/QpUsXvHjxAhs3btRaz+veu7yKi4tDhQoV0KtXL9SpUwdmZmY4cuQILly4gG+//RZAxlB/Nzc3TJ48GU+ePIFSqcSOHTuy7f3Lab+ysLBA7969sWLFCshkMri5uWHPnj1ZrouRm4KIIzvz58/H4cOH0aJFC4waNQr6+vr46aefkJKSgsWLF+c5voLSqVMnLF26FO3bt8eAAQMQHh6O77//HpUqVcK1a9ekcg0aNMCHH36IZcuWITIyUrplmOb7L3Nv/+zZs3Ho0CE0b94cI0eOlH5Yq1WrFvz9/aVybm5umD9/PqZPn46QkBB0794d5ubmCA4Oxs6dO/HJJ59g8uTJMDAwwPz58zF8+HC8//776Nu3L4KDg7F+/fq3Pqe7oLVq1QrDhw/HwoUL4e/vj3bt2sHAwACBgYHYtm0bli9fjl69esHa2hqTJ0/GwoUL0blzZ3Ts2BFXrlzB/v378/09T1QiFN6F0omoOLt7964YNmyYqFixolAoFMLc3Fw0b95crFixQuv2PYmJicLX11dYWFgIc3Nz0adPHxEeHp7jLcM0t7PKzNnZWXTq1CnbOOLi4sT06dNFpUqVhEKhEOXKlRPNmjUTS5YsEampqUKI/257k90tp16NIz09XYwdO1ZYW1sLmUyW7W2/lixZIho1aiTMzMyEQqEQlStXFmPHjpVu15S5Ppn99ddfonbt2sLIyEhUrFhRfP3112LdunVat67Krb4Astw6KLu6eXt7C1NTUxEUFCTatWsnTExMhK2trZg1a5bWLZru378vZs6cKZo0aSJsbGyEvr6+sLa2Fp06dRL//PNPlu0HBQWJwYMHCzs7O2FgYCDKly8vOnfuLLZv3y6Vyem2O5nnZa6rEBm3K/Ly8hIWFhbCyMhIuLm5CR8fH3Hx4kUhhBDPnz8Xo0ePFtWqVROmpqbCwsJCNG7cWPzxxx9CCCGePXsm9PX1xaBBg7JsUyMxMVGYmJiIHj16ZBvL5cuXRf/+/YWTk5MwNDQUNjY2onPnzlIMubXBqzS3X9q2bVu287du3Srq1asnDA0NRZkyZcTAgQPF48ePtcrkth/GxcWJCRMmCAcHB2FgYCAqV64svvnmG63b6GisW7dO2paVlZVo1aqVOHz4sFaZlStXimrVqgkDAwNha2srRo4cmeWWXTndXiuvx8Pvv/8u+vXrJ9zc3ISxsbEwMjISNWrUEF988YWIjY194/UKkbf9Mq+3DNMct9k9Mn9OtGrVKsdyBfmvUkpKipgyZYqoU6eOMDc3F6ampqJOnTpi1apVWuVu3bolPD09hZmZmShXrpwYNmyYuHr1apbbfeW2X0VERIgPP/xQmJiYCCsrKzF8+HBx48aNbG8ZZmpqmm28BRHHq++1EBnHp5eXlzAzMxMmJiaiTZs2WrdUFCLnz56cbof2qvzcMmzt2rWicuXKwtDQUFSrVk2sX78+28/8hIQEMXr0aFGmTBlhZmYmunfvLgICAgQAsWjRIq2yR48eFfXq1RMKhUK4ubmJn3/+WUyaNEkYGRlliXXHjh2iRYsWwtTUVJiamopq1aqJ0aNHi4CAAK1yq1atEi4uLsLQ0FA0bNhQnDx5UrRq1eqtbhn2pu9vbvuNEBm3M2vQoIEwNjYW5ubmwt3dXUydOlU8ffpUKqNSqcScOXOEvb29MDY2Fq1btxY3btzIEitRaSATooCuREFERDrh4+OD7du3Iz4+vqhDISKiTPz9/VGvXj1s3LgRAwcOzLVs9+7dcfPmTelOD0T07uA53UREREREr5GUlJRl2rJlyyCXy6VTFXIqGxgYiH379qF169a6DJGIiime001ERERE9BqLFy/GpUuX0KZNG+jr62P//v3Yv38/PvnkkyxXXXd1dYWPjw9cXV3x4MED/PDDD1AoFDneEpOISjcm3UREREREr9GsWTMcPnwY8+bNQ3x8PJycnDB79mx88cUXWcq2b98ev//+O8LCwmBoaIimTZviq6++0rrdGBG9O3hONxEREREREZGO8JxuIiIiIiIiIh1h0k1ERERERESkIzynWwfUajWePn0Kc3NzyGSyog6HiIiIiIiICpgQAnFxcXBwcIBcnnN/NpNuHXj69GmWq1gSERERERFR6fPo0SNUqFAhx/lMunXA3NwcQMabr1QqizgaIiLSuZgYYNs2wNgYMDQs6miIiIhKtpQUICkJ6N0bsLAo6mhyFBsbC0dHRyn/ywmTbh3QDClXKpVMuomI3gVCZCTcVlaAqWlRR0NERFSyJSRk/FUqMx7F3OtOKeaF1IiIiIiIiIh0hEk3ERERERERkY6UqKT75MmT6NKlCxwcHCCTybBr1y6t+UIIzJw5E/b29jA2NoanpycCAwO1yrx48QIDBw6EUqmEpaUlfH19ER8fr1Xm2rVr8PDwgJGRERwdHbF48WJdV42IiIiIiIhKoRJ1TndCQgLq1KmDoUOHomfPnlnmL168GP/73//wyy+/wMXFBTNmzICXlxdu3boFIyMjAMDAgQMRGhqKw4cPIy0tDUOGDMEnn3yCzZs3A8g4Gb5du3bw9PTEjz/+iOvXr2Po0KGwtLTEJ598UqD1UalUSEtLK9B1UuljYGAAPT29og6DiIiIiIjeQIlKujt06IAOHTpkO08IgWXLluHLL79Et27dAAC//vorbG1tsWvXLvTr1w+3b9/GgQMHcOHCBTRs2BAAsGLFCnTs2BFLliyBg4MDNm3ahNTUVKxbtw4KhQI1a9aEv78/li5dWmBJtxACYWFhiI6OLpD1UelnaWkJOzs73vediIiIiKiEKVFJd26Cg4MRFhYGT09PaZqFhQUaN24MPz8/9OvXD35+frC0tJQSbgDw9PSEXC7H+fPn0aNHD/j5+aFly5ZQKBRSGS8vL3z99deIioqClZVVlm2npKQgJSVFeh0bG5trrJqE28bGBiYmJkykKEdCCCQmJiI8PBwAYG9vX8QRERERERFRfpSapDssLAwAYGtrqzXd1tZWmhcWFgYbGxut+fr6+ihTpoxWGRcXlyzr0MzLLuleuHAh5syZk6c4VSqVlHCXLVs2T8vQu83Y2BgAEB4eDhsbGw41JyqGHkYlo+UDJ+BBUUdCRERUGphjuFJgelGHUUBK1IXUiqvp06cjJiZGejx69CjHsppzuE1MTAorPCoFNPsLrwFAVDwFPE8o6hCIiIhKlZ9ii//9ufOq1CTddnZ2AIBnz55pTX/27Jk0z87OThqmq5Geno4XL15olcluHZm38SpDQ0MolUqtx+twSDnlB/cXIiIiIqKSqdQk3S4uLrCzs8PRo0elabGxsTh//jyaNm0KAGjatCmio6Nx6dIlqcw///wDtVqNxo0bS2VOnjyp1aN4+PBhVK1aNduh5URERNP33SvqEIiIiKiYKlFJd3x8PPz9/eHv7w8g4+Jp/v7+ePjwIWQyGcaPH4/58+fjr7/+wvXr1zF48GA4ODige/fuAIDq1aujffv2GDZsGP7991+cOXMGY8aMQb9+/eDg4AAAGDBgABQKBXx9fXHz5k1s3boVy5cvx8SJE4uo1kREVNw9T+SpH0RERJS9EpV0X7x4EfXq1UO9evUAABMnTkS9evUwc+ZMAMDUqVMxduxYfPLJJ2jUqBHi4+Nx4MAB6R7dALBp0yZUq1YNbdu2RceOHdGiRQusXr1amm9hYYFDhw4hODgYDRo0wKRJkzBz5swCv0d3SdOlSxe0b98+23mnTp2CTCbDtWvXCjkqIiIiIiKi4k0mhBBFHURpExsbCwsLC8TExGQ5vzs5ORnBwcFwcXHR+jGguNu1axc+/PBDPHjwABUqVNCaN3ToUFy/fh0XLlwoouhKv5K63xC9KypO21vUIRAREZU6IdNbABYWRR1GjnLL+zIrUT3dVHQ6d+4Ma2trbNiwQWt6fHw8tm3bBl9fXxw/fhwymQwHDx5EvXr1YGxsjPfffx/h4eHYv38/qlevDqVSiQEDBiAxMbFoKkJERERERFSImHRTnujr62Pw4MHYsGEDMg+O2LZtG1QqFfr37y9Nmz17NlauXImzZ8/i0aNH6NOnD5YtW4bNmzdj7969OHToEFasWFEU1SAiIiIiIipUTLopz4YOHYqgoCCcOHFCmrZ+/Xp8+OGHsMg07GP+/Plo3rw56tWrB19fX5w4cQI//PAD6tWrBw8PD/Tq1QvHjh0riioQEREREREVKv2iDoD+U33GAagK6RR7PZkMt+dlf2G0nFSrVg3NmjXDunXr0Lp1a9y7dw+nTp3C3LlztcrVrl1bem5rawsTExO4urpqTfv333/frgJEREREREQlAJPuYiS/SXBR8PX1xdixY/H9999j/fr1cHNzQ6tWrbTKGBgYSM9lMpnWa800tVpdKPESEREREREVJQ4vp3zp06cP5HI5Nm/ejF9//RVDhw6FTCYr6rCIiIiIiIiKJfZ0U76YmZmhb9++mD59OmJjY+Hj41PUIRERERERERVb7OmmfPP19UVUVBS8vLzg4OBQ1OEQEREREREVWzIhCunKXe+Q3G6SnpycjODgYLi4uMDIyKiIIqSShvsNUfFWcdreog6BiIio1AmZ3gLIdJek4ia3vC8z9nQTERERERER6QiTbiIiIiIiIiIdYdJNREREREREpCNMuomIiIiIiIh0hEk3ERERERERkY4w6SYiIiIiIiLSESbdRERERERERDrCpJuIiIiIiIhIR5h0ExEREREREekIk+7iJCkJiIkpnEdSUr7Da926NcaPH1/w9c7B8ePHIZPJEB0dDQDYsGEDLC0tc11m9uzZqFu37htvMyQkBDKZDP7+/m+8DiIiIiIiIg39og6AXkpKAnbvBqKiCmd7VlZAt26AsXGeF/nzzz9hYGCgw6By17dvX3Ts2LHA1ufj44Po6Gjs2rVLmubo6IjQ0FCUK1euwLZDRERERETvLibdxUVqakbCbWwMGBnpdlvJyRnbSk3NV9JdpkwZHQb1esbGxjDOR7xvQk9PD3Z2djrdBhERERERvTs4vLy4MTICTE11+3jDpD7z8PKKFSviq6++wtChQ2Fubg4nJyesXr1aKtusWTN89tlnWstHRETAwMAAJ0+eBAD89ttvaNiwIczNzWFnZ4cBAwYgPDw8x+1nN7x80aJFsLW1hbm5OXx9fZGcnJynusyePRu//PILdu/eDZlMBplMhuPHj2cZXq4Z4n7w4EHUq1cPxsbGeP/99xEeHo79+/ejevXqUCqVGDBgABITE6X1q9VqLFy4EC4uLjA2NkadOnWwfft2aX5UVBQGDhwIa2trGBsbo3Llyli/fn2eYiciIiIiopKDSTe9sW+//RYNGzbElStXMGrUKIwcORIBAQEAgIEDB2LLli0QQkjlt27dCgcHB3h4eAAA0tLSMG/ePFy9ehW7du1CSEgIfHx88rz9P/74A7Nnz8ZXX32Fixcvwt7eHqtWrcrTspMnT0afPn3Qvn17hIaGIjQ0FM2aNcux/OzZs7Fy5UqcPXsWjx49Qp8+fbBs2TJs3rwZe/fuxaFDh7BixQqp/MKFC/Hrr7/ixx9/xM2bNzFhwgR89NFHOHHiBABgxowZuHXrFvbv34/bt2/jhx9+4JB2IiIiIqJSiMPL6Y117NgRo0aNAgB89tln+O6773Ds2DFUrVoVffr0wfjx43H69Gkpyd68eTP69+8PmUwGABg6dKi0LldXV/zvf/9Do0aNEB8fDzMzs9duf9myZfD19YWvry8AYP78+Thy5EieervNzMxgbGyMlJSUPA0nnz9/Ppo3bw4A8PX1xfTp0xEUFARXV1cAQK9evXDs2DF89tlnSElJwVdffYUjR46gadOmUv1Onz6Nn376Ca1atcLDhw9Rr149NGzYEEDGyAEiIiIiIip92NNNb6x27drSc5lMBjs7O2l4uLW1Ndq1a4dNmzYBAIKDg+Hn54eBAwdKy1y6dAldunSBk5MTzM3N0apVKwDAw4cP87T927dvo3HjxlrTNEluQctcV1tbW5iYmEgJt2aapu737t1DYmIiPvjgA5iZmUmPX3/9FUFBQQCAkSNHYsuWLahbty6mTp2Ks2fP6iRuIiIiIiIqWky66Y29eiVzmUwGtVotvR44cCC2b9+OtLQ0bN68Ge7u7nB3dwcAJCQkwMvLC0qlEps2bcKFCxewc+dOAEBqamrhVSKPMtdVJpPlWvf4+HgAwN69e+Hv7y89bt26JZ3X3aFDBzx48AATJkzA06dP0bZtW0yePLmQakNERERERIWFSTfpTLdu3ZCcnIwDBw5g8+bNWr3cd+7cQWRkJBYtWgQPDw9Uq1Yt14uoZad69eo4f/681rRz587leXmFQgGVSpWvbeZFjRo1YGhoiIcPH6JSpUpaD0dHR6mctbU1vL29sXHjRixbtkzrQnRERERERFQ68Jxu0hlTU1N0794dM2bMwO3bt9G/f39pnpOTExQKBVasWIERI0bgxo0bmDdvXr7W/+mnn8LHxwcNGzZE8+bNsWnTJty8eVNr2HduKlasiIMHDyIgIABly5aFhYVFvrafE3Nzc0yePBkTJkyAWq1GixYtEBMTgzNnzkCpVMLb2xszZ85EgwYNULNmTaSkpGDPnj2oXr16gWyfiIiIiIiKDybdxU0eb3lV7Lfx0sCBA9GxY0e0bNkSTk5O0nRra2ts2LABn3/+Of73v/+hfv36WLJkCbp27Zrndfft2xdBQUGYOnUqkpOT8eGHH2LkyJE4ePBgnpYfNmwYjh8/joYNGyI+Ph7Hjh0rsAuazZs3D9bW1li4cCHu378PS0tL1K9fH59//jmAjF726dOnIyQkBMbGxvDw8MCWLVsKZNtERERERFR8yETmezpRgYiNjYWFhQViYmKgVCq15iUnJyM4OBguLi4wyny/7KQkYPduICqqcIK0sgK6dQOMjQtne/RWctxviKhYqDhtb1GHQEREVOqETG8BFNBoVF3ILe/LjD3dxYWxcUYSXFgXEVMomHATERERERHpGJPu4sTYmIlwAcrtXt/79++X7h9ORERERESkK0y6qdTy9/fPcV758uULLxAiIiIiInpnMemmUqtSpUpFHQIREREREb3jeJ9uIiIiIiIiIh1h0l1E1Gp1UYdAJQj3FyIiIiKikqlUDS+fPXs25syZozWtatWquHPnDoCM2y5NmjQJW7ZsQUpKCry8vLBq1SrY2tpK5R8+fIiRI0fi2LFjMDMzg7e3NxYuXAh9/YJ5qxQKBeRyOZ4+fQpra2soFArIZLICWTeVPkIIpKamIiIiAnK5HAqFoqhDIiIiIiKifChVSTcA1KxZE0eOHJFeZ06WJ0yYgL1792Lbtm2wsLDAmDFj0LNnT5w5cwYAoFKp0KlTJ9jZ2eHs2bMIDQ3F4MGDYWBggK+++qpA4pPL5XBxcUFoaCiePn1aIOuk0s/ExAROTk6Qyzk4hYiIiIioJCl1Sbe+vj7s7OyyTI+JicHatWuxefNmvP/++wCA9evXo3r16jh37hyaNGmCQ4cO4datWzhy5AhsbW1Rt25dzJs3D5999hlmz55dYL2MCoUCTk5OSE9Ph0qlKpB1Uumlp6cHfX19joggIiIiIiqBSl3SHRgYCAcHBxgZGaFp06ZYuHAhnJyccOnSJaSlpcHT01MqW61aNTg5OcHPzw9NmjSBn58f3N3dtYabe3l5YeTIkbh58ybq1auX7TZTUlKQkpIivY6NjX1tnDKZDAYGBjAwMHiL2hIREREREVFxVqrGqjZu3BgbNmzAgQMH8MMPPyA4OBgeHh6Ii4tDWFgYFAoFLC0ttZaxtbVFWFgYACAsLEwr4dbM18zLycKFC2FhYSE9HB0dC7ZiREREREREVCKVqp7uDh06SM9r166Nxo0bw9nZGX/88QeMjY11tt3p06dj4sSJ0uvY2Fgm3kRERERERFS6erpfZWlpiSpVquDevXuws7NDamoqoqOjtco8e/ZMOgfczs4Oz549yzJfMy8nhoaGUCqVWg8iIiIiIiKiUp10x8fHIygoCPb29mjQoAEMDAxw9OhRaX5AQAAePnyIpk2bAgCaNm2K69evIzw8XCpz+PBhKJVK1KhRo9DjJyIiIiIiopKtVA0vnzx5Mrp06QJnZ2c8ffoUs2bNgp6eHvr37w8LCwv4+vpi4sSJKFOmDJRKJcaOHYumTZuiSZMmAIB27dqhRo0aGDRoEBYvXoywsDB8+eWXGD16NAwNDYu4dkRERERERFTSlKqk+/Hjx+jfvz8iIyNhbW2NFi1a4Ny5c7C2tgYAfPfdd5DL5fjwww+RkpICLy8vrFq1SlpeT08Pe/bswciRI9G0aVOYmprC29sbc+fOLaoqERERERERUQkmE0KIog6itImNjYWFhQViYmJ4fjcR0Tug4rS9RR0CERFRqRMyvQVgYVHUYeQor3lfqT6nm4iIiIiIiKgoMekmIiIiIiIi0hEm3UREREREREQ6wqSbiIiIiIiISEeYdBMRERERERHpCJNuIiIiIiIiIh1h0k1ERERERESkI0y6iYiIiIiIiHSESTcRERERERGRjjDpJiIiIiIiItKRt0q6U1JSCioOIiIiIiIiolInX0n3/v374e3tDVdXVxgYGMDExARKpRKtWrXCggUL8PTpU13FSURERERERFTi5Cnp3rlzJ6pUqYKhQ4dCX18fn332Gf78808cPHgQP//8M1q1aoUjR47A1dUVI0aMQEREhK7jJiIiIiIiIir29PNSaPHixfjuu+/QoUMHyOVZ8/Q+ffoAAJ48eYIVK1Zg48aNmDBhQsFGSkRERERERFTC5Cnp9vPzy9PKypcvj0WLFr1VQERERERERESlRZ7P6Z48eTLu3Lmjy1iIiIiIiIiISpU8J927d+9GzZo10axZM6xbtw4JCQm6jIuIiIiIiIioxMtz0h0YGIhjx46hSpUq+PTTT2FnZ4ehQ4fi7NmzuoyPiIiIiIiIqMTK1y3DWrZsiQ0bNiAsLAzLly9HYGAgWrRogerVq2PJkiV49uyZruIkIiIiIiIiKnHylXRrmJqaYujQoTh16hTu3r2Lnj17YuHChXByciro+IiIiIiIiIhKrDdKujUSEhJw6tQpnDhxAlFRUXB1dS2ouIiIiIiIiIhKvDdKuk+fPo2hQ4fC3t4e48aNQ5UqVXDq1Cncvn27oOMjIiIiIiIiKrHydJ9uAAgNDcUvv/yCDRs24O7du2jSpAmWLl2Kfv36wczMTJcxEhEREREREZVIeU66HR0dUbZsWQwaNAi+vr6oXr26LuMiIiIiIiIiKvHynHT/8ccf6Nq1K/T187wIERERERER0Tstzxl0z549tV6Hh4cjPDwcarVaa3rt2rULJjIiIiIiIiKiEi7f3daXLl2Ct7c3bt++DSEEAEAmk0EIAZlMBpVKVeBBEhEREREREZVE+U66hw4diipVqmDt2rWwtbWFTCbTRVxEREREREREJV6+k+779+9jx44dqFSpki7iISIiIiIiIio18n2f7rZt2+Lq1au6iIWIiIiIiIioVMl3T/fPP/8Mb29v3LhxA7Vq1YKBgYHW/K5duxZYcEREREREREQlWb6Tbj8/P5w5cwb79+/PMo8XUiMiIiIiIiL6T76Hl48dOxYfffQRQkNDoVartR5MuImIiIiIiIj+k++kOzIyEhMmTICtra0u4iEiIiIiIiIqNfKddPfs2RPHjh3TRSxEREREREREpUq+z+muUqUKpk+fjtOnT8Pd3T3LhdTGjRtXYMERUd4IIRASmQiXcqZFHQoREREREWUiE0KI/Czg4uKS88pkMty/f/+tgyoOvv/+e3zzzTcICwtDnTp1sGLFCrz33nt5WjY2NhYWFhaIiYmBUqnUcaSFIyElHQp9OQz08j044p0TnZgKSxNFoW7zysMo9Fh1FgfGe8C5jCniktNgbW4IIOO4JCLdqjhtb1GHQEREVOqETG8BWFgUdRg5ymvel++e7uDg4LcKrCTYunUrJk6ciB9//BGNGzfGsmXL4OXlhYCAANjY2BR1eEWi4fwj6FLHHot71SnqUIq11HQ16s49jJBFnQp1u2mqjN/O2i87hcYuZXA++AU+qGGLhJR0BIbHY3aXmlh/Jhizu9bEsiOBGO9ZGbdDY+FUxgTlzA1x/v4LDGjshD8vP0aPeuXxLDYFKiFQ3tIYj6MSUcHKBEIIxCalw8LEACnpKggBGBnoFWo9iYiIiIhKGnZbZmPp0qUYNmwYhgwZgho1auDHH3+EiYkJ1q1bV9ShFZmkNBVuPo0t6jCKvTSVGkBG8l2Y9OT/9WZHxKUAAC4/iMLZoEhExKXg3P1IXHwQhRN3I3Dk9jMcuf0MU7Zfw8zdN7HudDA+33kd6So1Jv5xFZEJqRjw8zm0+eY4AKDF18fwIDIBx+9GoM7cQwCAkRsvo/v3ZwAAjRYcwU8nggAANWYewLXH0QCAenMP4UVCKgCg+aJ/kJKuQnKaCm2WZKxXrRb45WwIACBdpcYfFx4BAGKT07D+TMaPeyq1wIEbYQAy3ts/Lz8GkPH+Hr71TCpzMeQFgIxh9rde7qdCCFx68CLLcwC4Fx4nPQ98FgfNgJ9z9yOlNoyMT0H6y+ePXiQiNjkNAJCYmo6YxDRpveFxydK6IuNTpOcvElKl5dNVaiSn/Xd3h4SUdOl5Ye8rRERERFS48pR0L1q0CElJSXla4fnz57F3b8kdZpeamopLly7B09NTmiaXy+Hp6Qk/P79sl0lJSUFsbKzWozTKnDRQ9jQJW3ympKowGOhlHUJuqP/f4Z2SntF2mjZMSs34m5iWDhNFRm91wstpYTHJSE1XI1WllpLRyIRUJKb81/73wuNxJywjcY2IS8HZoMiM9aWqcOVhNAAgKjENj14kIjVdjSfRSXgclYTIhFQEP09AfEo6nsenYNZfN5GcpsKT6CRM3XENarXA2XvPMefvWwCAB5EJGLHxElRqgeDnCZj4x1UIIXDlYRSG/XoRAOD/KAq9fsw4Nu8+i0fH/52CEAL3wuPx4Q8Z0x9HJeHDH/ygUgtEJaTCc+lJKdn94LuTuPssHgDQb/U5HLyZkeQ3mH8EK/65BwDwWHwMk/+4CgD4dIs/Wn6TcTFJv6BIvLfgqPS+NJh/BNcfxwAA6s87jJ9OZpxuM/Ovm2j77QkAQExiGmrOOgi1WkAIgSpf7sfzl8n61O1X8d3hu1K5iVv9pXVP//M6zt/PeJ+/PnAHPVZl/OghhECtWQelNj1wIxR/XX0KIOMHidl/3YRandGO+6+H4saTGGmdh26GSfOS01TSDxZEREREVHDylHTfunULTk5OGDVqFPbv34+IiAhpXnp6Oq5du4ZVq1ahWbNm6Nu3L8zNzXUWsK49f/4cKpUqyy3RbG1tERYWlu0yCxcuhIWFhfRwdHQsjFALXQp75F5LM8w77mWvaGGRZzpvW5NY62VKxJPSMtpOSrZT/0u+jRX6Wss9i02Who1r2vx53H89uCnpKilRl9af+l9CHp+SLiX5ccnpiE7M6O1+9CIRUS97vp9EJeF5fMbzsJhk6Xl00n/vW2q6GpEvyz+NTkLkyzIJqSokpWnWn4bol73OarVAZEJGnFGJaVKPf3KaCmGxGb3RoTFJeBqT8QPiwxeJUo9zdGKq9ANDXPJ/P5iEZ6q3JraAsDjEvHweGvNfL7cmiX/wIkGapkmmLwS/wJPojO3ei8j4sSImKU1aT8jzjGX+uPgYv//7EABw7Uk0/rzyRGqX3/99iO2XMnr6z9x7Lv24EZmQiviUdDyOSgQAjNh4GVO3Z/xAEBgehw1nQ6Q4Rm66jLl7Mn7QeByViE9+u4QHLzKWW3s6GB3/d0qKfd3pYJx7meTHp6Sj4rS90nqCnyfg853XpbJXHkbhdOBz6XXm0QBERERE77o8ndP966+/4urVq1i5ciUGDBiA2NhY6OnpwdDQEImJGf+w1atXDx9//DF8fHxgZGSk06CLm+nTp2PixInS69jY2FKZeLOn+/U0Pd2xSYXb061S/3c9xLiXiWS66r9pmrZLTNNOulPS1NCMTNckqWGxydB/OVGTVIbHpUjJaHhsirQ9zXpVQuDpy6QyIi4Fd8Myeo6jElMRGJ7x/FGmRPtxVCIevUz2nkQnIeBlr3loTBIuhEQByEj+T71M5B5EJuLyw4zpYTHJuPQg4/mjF0nS86jEVJy8qymfgCuPogFkJKWaMg9fJEo90SHPExAUES/VOfP677+cbqLQkxJjYwM9xCWn4eHLuNNVavwTEA4gY7i4Zrh7UHgCHkZmlJHLZIjO9B6o1AJ/Xn4ivaeaXueQyETov7xIobFCD8lpKvzm9wBAxqiC1Jf7lUoIhMYk4X5ERpIuhMCGMyEv39MkmBtl3E2irKkhhBDYdC4jgQ9+noDrL7dlotCDWi3w68v1B4XHw1Shhx0vE3qVWuDI7WeYu+cW2lS1Rl1HSyzYexsAcOlBFOo7WWHytqu49CAKY9+vhOuPYzBuyxUkp6kRML89TgREYOSmy3Aua4L9n3rg0M1n+OZgABwsjTC3Wy34BUVi55UnsFUaov97TngQmSj9ECKXySB7+Vcuy7gIoOa5XC6DTPbfPM0PTfEp6Xj4IhEmCj0Y6Mmh0Mu44KO+ngwqtcCLhFSkqdSwNFEgISUdyWlqtKtpC6+adiAiIiIqDPm+erlarca1a9fw4MEDJCUloVy5cqhbty7KlSunqxgLVWpqKkxMTLB9+3Z0795dmu7t7Y3o6Gjs3r37tesojVcvrzhtL8wM9XFjjldRh1KsPYhMQKtvjmPTx43RvFLhHRMXQl6g94/apz8Y6MmknneXcqYIfp6A+k6WuPwwGtXtlbgdGgtLEwOpp9irpi0O3XqGFpXK4d/gF0hJV6N1VWucvBuB1lVtcPNpDOKT09Guph3+fdlz276mHUIiExARlwJbpRHMjfQRFpsMGYD4FBXcyytxOzQOFcuZICAsDvp6cpS3NM4Y5p2YChtzQ8QlpyMhJR0G+nLYKo3w+EUijBR6KGuqQGhMMlzKmeJJVBLiUtJRxkSBClbGuPE0BmVNDZGqUkOtFkhITYebtRmeRifBxtwIz+NTXg6PByqWM8GDyEQ4WBrjWWwy0lUCVe3McfdZHNJVApVszF72FCfhPRcr+AVFQl9PjobOVrjxNAb68oyYrz+JQVkzBWqXt8Au/6d4z6UMlEYGCHgWC+cypngclYjOtR3wq18ITA31UcNeibvhcZBBhnY1bPHz6WA0r1QWBnpyJKepkJymRnhsMuo5W+FCcMb55pO9qmLq9mtwL2+BqnbmSE5T4U5YHKITUzGkuQuWHr6LcmYKfNLSDfP23IJH5XJISlXBzsIIj6KS8CQqCX0bVcDPp4LRwNkK6SqBcuYK3HwaCyGAqe2rYszmK6hdwQImCj04lTHB9SexiIhLxictXbHx3ENYGBsgMTUdX3SqjpEbL8Pa3BD1nKzgWs4Uu/yfIDYpDb4tXHD/eQL2Xw+DnYURVvSvh5m7b+DKo2jUcrDA7K41sPhAAK4+jsZ7LmUxoqUrTt17joM3w9DAyQo96pdHQFgcDt18hiq2ZnAsYwKZTAYhBNRCQAhALfDyuZCeqwWkMpppJgb6cCprjJQ0NdJUaqSpBNJUaqSmq6GnJ4OViQIKPTmik9JgbKCHW6ExePQiCb8MzdvdKPKKVy8nIiIqeO/s1cvlcjnq1q2LunXrvk18xZZCoUCDBg1w9OhRKelWq9U4evQoxowZU7TBFQK1WiAuJR2xL4e+RiakSj2SxgpeqTozlVogMTUdCSkqJKamIyIuBftfXvQrNqlwh9Zqhm1npkm4rc0NEfw8AR3d7bDvehg+rF8BOy4/xozONTBvzy0ojfRhbmSAW6GxmNetFr7cdQMbfRvjo7XnYWWiwKqB9TFi42WsH9IIf/k/xYEbYTg8sSU8Fh+DqaE+Nn3cGA3mH8HEdlXgUckaLb85hlUD68NYoYfP/7yOpX0zrnjvu+EiDoxujquPY7Dn6lP8OvQ9nLgbgW2XHuG7Po3x97VQHLgRil2jm2Pbpce4GPICf49pgZOBEfjheBB2f9wcB2+G4cCNMPw5qjkO3wzD0TvhWNG/HnZcfoLLD6Pww8AGOHQrDH9efoLvB9bHbv8nOB4QgV2jm+Pk3QisPxOCdT6NcP1JNL4/FoSVA+oh+HkCvj4QgA1DGsHIQA8jn13CN73rwMHSCKM3XcYwD1c0cSuL/qvPYUQrN3Sp44A0tUCdChb4uIUrNpwNwfUnMfh+YH3EJqXh6J1wzO9eE05lTDF6c8byH9SwRXhcCqrYmmFU60rYfyMMe68/xerBDfAwMhHRian4+sPaKG9pjIshL9CoYhn0alABxwMicPj2M4z3rAylkQGuPorGsJauaFSxDIKfx8PKRIGx71fG1cfR2Hz+IZb1rYuKZU2QphJwszZFrwaOuBDyArv9n2Ba++owM9JH//ci0bJyObSvZYeAZ3HY8u8jDG3uAqeyJqhkY4ZnsSno1aACDPTkWDO4IcqZGaKGgxIxiWkoa6ZA+1p2sDE3QlhMMtrVsMX71Wyh0M8o+yw2BdXtzSGTybDx48ZISlNB+bL3vVmlcvisfTVp/2zmVg5Dmud8C0pdufssDmM3Xyn07RIREdG7K9893e+CrVu3wtvbGz/99BPee+89LFu2DH/88Qfu3LmT5Vzv7JSEnu5Jf1xFYHgcElLSkZiqQkq6GkmpKqjUAuZG+lAaG0BpbICypgo4WBrB3sIYe66FYv+nHoUWoxACaSqBdHVG71X6y16sdLUa6Zq/aoG09IznaiGQkq5GSnpGL5dKLbR6vjTP01Uvl1NlrCdNrYZKJZD6skxKeka51PSM3sjktIz3JzlNhfiUdMQlpyMpTQUZMn6IMDPUh4lCD2VNDVHF1gwPXiSifU079HvPqdDeq43nHuDLXTfwXsUyCItNRkd3e9R0UOLSgygMbuqMk3cj0KuhI07ejUDb6ja49CAKzdzK4drjjJ7JZ3HJsDRWQE8uQ3RSKmzMjZCUqoKRgRwymQxpKjUM9ORQqUXG8F95Rq+k5h7gOT0nKm7SVWq0XnIcPw1qgJoOBffLOXu6iYiICt7wJuUxvXvdog4jRzrr6X4X9O3bFxEREZg5cybCwsJQt25dHDhwIE8Jd0nxsUdGD5OJQg/GBnowVujByCDjnMjsxCSm4edT9/HTiSDoyWXS8E6VWkCtFlCJ//6q1IBKrZaugJ2SpkZyugopaf8lxCkvh3+mpKuQmv5fEp2a/l9iLZPJoC/PeGjO0VToy6Evl0NfLoPey+l6L8voyWUwNNCDQk8OQ/2M8vpyORT6L5eXy2Gg99+6DPTkMDaQScsr9PWg0M8oo9CTQ6Evh5GBHowM5DDUz/hraqj/MsnW17pNV2ZrTt7H9kuP8TgqSWtIrEotch0yq1ILpKtfJv/p//1IoHkf09VqpKW//AHh5Q8PmmG0yWlqfNmpOnxbuGglvF3qOAAAXK3NAAAd3e0BZPQyAkDtCpYAAHsLY2kZG/OMazJkHtmg2S8y1znzdnJ6TlTc6OvJMcWrKj759RJGt6mEek6WcLAwhtJYn/suERFRMfNeheI7tDw/2NOtAyWhp/tN+AVF4sTdCAgI6MkyklX5ywsd6ckzej810/XkMhi+TGIV+nIYG+jB8OVzg5dJsZGBHAo9PRi8TIozHpoEWVZi/wFOTc+4n3RMUlrGhaFeXvQp4/165eJQmebL5fjvQlD6chi8/JFA/+X7Jf3wkOlHA035jB8YSu57RlTYLj14gc3nH+F2aCzCYpMRn5wuHYOZj1d55s86+cvXL49b2csLvwGQLpZHREREBae0nNPNpFsHSmvSTURUmmlOYVGLjFE7Qp1xxXj1y5E8agFpVI9mlIom6W695HhRhk5ERFQqlZakm8PLiYiIkDH0XJ/XiyQiIqIClu+kOzk5GStWrMCxY8cQHh4OtVqtNf/y5csFFhwRERERERFRSZbvpNvX1xeHDh1Cr1698N577/EcUiIiIiIiIqIc5Dvp3rNnD/bt24fmzZvrIh4iIiIiIiKiUiP7+0Plonz58jA3N9dFLERERERERESlSr6T7m+//RafffYZHjx4oIt4iIiIiIiIiEqNfA8vb9iwIZKTk+Hq6goTExMYGBhozX/x4kWBBUdERERERERUkuU76e7fvz+ePHmCr776Cra2tryQGhEREREREVEO8p10nz17Fn5+fqhTp44u4iEiIiIiIiIqNfJ9Tne1atWQlJSki1iIiIiIiIiISpV8J92LFi3CpEmTcPz4cURGRiI2NlbrQUREREREREQZ8j28vH379gCAtm3bak0XQkAmk0GlUhVMZEREREREREQlXL6T7mPHjukiDiIiIiIiIqJSJ89J940bN1CrVi20atVKl/EQERERERERlRp5Pqe7du3aaNy4MdasWYO4uDhdxkRERERERERUKuQ56T5x4gRq1qyJSZMmwd7eHt7e3jh16pQuYyMiIiIiIiIq0fKcdHt4eGDdunUIDQ3FihUrEBISglatWqFKlSr4+uuvERYWpss4iYiIiIiIiEqcfN8yzNTUFEOGDMGJEydw9+5d9O7dG99//z2cnJzQtWtXXcRIREREREREVCLlO+nOrFKlSvj888/x5ZdfwtzcHHv37i2ouIiIiIiIiIhKvHzfMkzj5MmTWLduHXbs2AG5XI4+ffrA19e3IGMjIiIiIiIiKtHylXQ/ffoUGzZswIYNG3Dv3j00a9YM//vf/9CnTx+YmprqKkYiIiIiIiKiEinPSXeHDh1w5MgRlCtXDoMHD8bQoUNRtWpVXcZGREREREREVKLlOek2MDDA9u3b0blzZ+jp6ekyJiIiIiIiIqJSIc9J919//aXLOIiIiIiIiIhKnbe6ejkRERERERER5YxJNxEREREREZGOMOkmIiIiIiIi0hEm3UREREREREQ6wqSbiIiIiIiISEeYdBMRERERERHpCJNuIiIiIiIiIh1h0k1ERERERESkI0y6iYiIiIiIiHSESTcRERERERGRjjDpJiIiIiIiItKRUpV0V6xYETKZTOuxaNEirTLXrl2Dh4cHjIyM4OjoiMWLF2dZz7Zt21CtWjUYGRnB3d0d+/btK6wqEBERERERUSlSqpJuAJg7dy5CQ0Olx9ixY6V5sbGxaNeuHZydnXHp0iV88803mD17NlavXi2VOXv2LPr37w9fX19cuXIF3bt3R/fu3XHjxo2iqA4RERERERGVYPpFHUBBMzc3h52dXbbzNm3ahNTUVKxbtw4KhQI1a9aEv78/li5dik8++QQAsHz5crRv3x5TpkwBAMybNw+HDx/GypUr8eOPPxZaPYiIiIiIiKjkK3U93YsWLULZsmVRr149fPPNN0hPT5fm+fn5oWXLllAoFNI0Ly8vBAQEICoqSirj6emptU4vLy/4+fnluM2UlBTExsZqPYiIiIiIiIhKVU/3uHHjUL9+fZQpUwZnz57F9OnTERoaiqVLlwIAwsLC4OLiorWMra2tNM/KygphYWHStMxlwsLCctzuwoULMWfOnAKuDREREREREZV0xb6ne9q0aVkujvbq486dOwCAiRMnonXr1qhduzZGjBiBb7/9FitWrEBKSopOY5w+fTpiYmKkx6NHj3S6PSIiIiIiIioZin1P96RJk+Dj45NrGVdX12ynN27cGOnp6QgJCUHVqlVhZ2eHZ8+eaZXRvNacB55TmZzOEwcAQ0NDGBoavq4qRERERERE9I4p9km3tbU1rK2t32hZf39/yOVy2NjYAACaNm2KL774AmlpaTAwMAAAHD58GFWrVoWVlZVU5ujRoxg/fry0nsOHD6Np06ZvVxEiIiIiIiJ65xT74eV55efnh2XLluHq1au4f/8+Nm3ahAkTJuCjjz6SEuoBAwZAoVDA19cXN2/exNatW7F8+XJMnDhRWs+nn36KAwcO4Ntvv8WdO3cwe/ZsXLx4EWPGjCmqqhEREREREVEJVex7uvPK0NAQW7ZswezZs5GSkgIXFxdMmDBBK6G2sLDAoUOHMHr0aDRo0ADlypXDzJkzpduFAUCzZs2wefNmfPnll/j8889RuXJl7Nq1C7Vq1SqKahEREREREVEJJhNCiKIOorSJjY2FhYUFYmJioFQqizocIiLSsYrT9hZ1CERERKVOyPQWgIVFUYeRo7zmfaVmeDkRERERERFRccOkm4iIiIiIiEhHmHQTERERERER6QiTbiIiIiIiIiIdYdJNREREREREpCNMuomIiIiIiIh0hEk3ERERERERkY4w6SYiIiIiIiLSESbdRERERERERDrCpJuIiIiIiIhIR5h0ExEREREREekIk24iIiIiIiIiHWHSTURERERERKQjTLqJiIiIiIiIdIRJNxEREREREZGOMOkmIiIiIiIi0hEm3UREREREREQ6wqSbiIiIiIiISEeYdBMRERERERHpCJNuIiIiIiIiIh1h0k1ERERERESkI0y6iYiIiIiIiHSESTcRERERERGRjjDpJiIiIiIiItIRJt1EREREREREOsKkm4iIiIiIiEhHmHQTERERERER6QiTbiIiIiIiIiIdYdJNREREREREpCNMuomIiIiIiIh0hEk3ERERERERkY4w6SYiIiIiIiLSESbdRERERERERDrCpJuIiIiIiIhIR5h0ExEREREREekIk24iIiIiIiIiHSkxSfeCBQvQrFkzmJiYwNLSMtsyDx8+RKdOnWBiYgIbGxtMmTIF6enpWmWOHz+O+vXrw9DQEJUqVcKGDRuyrOf7779HxYoVYWRkhMaNG+Pff//VQY2IiIiIiIiotCsxSXdqaip69+6NkSNHZjtfpVKhU6dOSE1NxdmzZ/HLL79gw4YNmDlzplQmODgYnTp1Qps2beDv74/x48fj448/xsGDB6UyW7duxcSJEzFr1ixcvnwZderUgZeXF8LDw3VeRyIiKplW9ahW1CEQERGVKoYydVGHUGBkQghR1EHkx4YNGzB+/HhER0drTd+/fz86d+6Mp0+fwtbWFgDw448/4rPPPkNERAQUCgU+++wz7N27Fzdu3JCW69evH6Kjo3HgwAEAQOPGjdGoUSOsXLkSAKBWq+Ho6IixY8di2rRpeYoxNjYWFhYWiImJgVKpLIBaExFRcXb33lO0+/lKUYdBRERUanibx2HOmA6AhUVRh5KjvOZ9+oUYk075+fnB3d1dSrgBwMvLCyNHjsTNmzdRr149+Pn5wdPTU2s5Ly8vjB8/HkBGb/qlS5cwffp0ab5cLoenpyf8/Pxy3HZKSgpSUlKk17GxsQVUKyIiKgmqWJsixPkhYGUFmJoWdThEREQlW0ICEBVV1FEUmBIzvPx1wsLCtBJuANLrsLCwXMvExsYiKSkJz58/h0qlyraMZh3ZWbhwISwsLKSHo6NjQVSJiIiIiIiISrgiTbqnTZsGmUyW6+POnTtFGWKeTJ8+HTExMdLj0aNHRR0SERERERERFQNFOrx80qRJ8PHxybWMq6trntZlZ2eX5Srjz549k+Zp/mqmZS6jVCphbGwMPT096OnpZVtGs47sGBoawtDQME9xEhERERER0bujSJNua2trWFtbF8i6mjZtigULFiA8PBw2NjYAgMOHD0OpVKJGjRpSmX379mktd/jwYTRt2hQAoFAo0KBBAxw9ehTdu3cHkHEhtaNHj2LMmDEFEicRERERERG9O0rMOd0PHz6Ev78/Hj58CJVKBX9/f/j7+yM+Ph4A0K5dO9SoUQODBg3C1atXcfDgQXz55ZcYPXq01As9YsQI3L9/H1OnTsWdO3ewatUq/PHHH5gwYYK0nYkTJ2LNmjX45ZdfcPv2bYwcORIJCQkYMmRIkdSbiIiIiIiISq4Sc/XymTNn4pdffpFe16tXDwBw7NgxtG7dGnp6etizZw9GjhyJpk2bwtTUFN7e3pg7d660jIuLC/bu3YsJEyZg+fLlqFChAn7++Wd4eXlJZfr27YuIiAjMnDkTYWFhqFu3Lg4cOJDl4mpEREREREREr1Pi7tNdEvA+3URE75iYGGDzZt4yjIiIqCBobhk2YADv003Z0/yOwft1ExG9I2JjgaSkjOeJiUUbCxERUUmXkpLxvRobC8hkRR1NjjT53uv6sdnTrQOPHz/mvbqJiIiIiIjeAY8ePUKFChVynM+kWwfUajUCAgJQo0YNPHr0iEPMi5HY2Fg4OjqyXYoZtkvxwzYpntguxQ/bpHhiuxRPbJfih23ydoQQiIuLg4ODA+TynK9RzuHlOiCXy1G+fHkAgFKp5A5cDLFdiie2S/HDNime2C7FD9ukeGK7FE9sl+KHbfLmLPJwznmJuWUYERERERERUUnDpJuIiIiIiIhIR5h064ihoSFmzZoFQ0PDog6FMmG7FE9sl+KHbVI8sV2KH7ZJ8cR2KZ7YLsUP26Rw8EJqRERERERERDrCnm4iIiIiIiIiHWHSTURERERERKQjTLqJiIiIiIiIdIRJdzYWLlyIRo0awdzcHDY2NujevTsCAgKk+SEhIZDJZNk+tm3bJpU7evQomjVrBnNzc9jZ2eGzzz5Denp6rttu3bp1lnWOGDFCZ3UtKV7XJgAQFhaGQYMGwc7ODqampqhfvz527NihVebFixcYOHAglEolLC0t4evri/j4+Fy3nZycjNGjR6Ns2bIwMzPDhx9+iGfPnhV4HUuiomwXHivZK6g2WbBgAZo1awYTExNYWlrmadtCCMycORP29vYwNjaGp6cnAgMDC6pqJVpRtouPj0+WY6V9+/YFVbUSrSDaJSQkBL6+vnBxcYGxsTHc3Nwwa9YspKam5rptfrdkryjbhN8rOSuoz7CuXbvCyckJRkZGsLe3x6BBg/D06dNct81jJXtF2SY8Vt6AoCy8vLzE+vXrxY0bN4S/v7/o2LGjcHJyEvHx8UIIIdLT00VoaKjWY86cOcLMzEzExcUJIYTw9/cXCoVCzJkzRwQGBorjx4+LatWqiUmTJuW67VatWolhw4ZprTsmJkbndS7uXtcmQgjxwQcfiEaNGonz58+LoKAgMW/ePCGXy8Xly5elMu3btxd16tQR586dE6dOnRKVKlUS/fv3z3XbI0aMEI6OjuLo0aPi4sWLokmTJqJZs2Y6q2tJUpTtwmMlewXVJjNnzhRLly4VEydOFBYWFnna9qJFi4SFhYXYtWuXuHr1qujatatwcXERSUlJBV3NEqco28Xb21u0b99e61h58eJFQVexRCqIdtm/f7/w8fERBw8eFEFBQWL37t3Cxsbmtd/3/G7JXlG2Cb9XclZQn2FLly4Vfn5+IiQkRJw5c0Y0bdpUNG3aNNdt81jJXlG2CY+V/GPSnQfh4eECgDhx4kSOZerWrSuGDh0qvZ4+fbpo2LChVpm//vpLGBkZidjY2BzX06pVK/Hpp5++dcylXXZtYmpqKn799VetcmXKlBFr1qwRQghx69YtAUBcuHBBmr9//34hk8nEkydPst1OdHS0MDAwENu2bZOm3b59WwAQfn5+BVmlUqGw2kUIHit59SZtktn69evzlNyp1WphZ2cnvvnmG2ladHS0MDQ0FL///vubV6CUKqx2ESIj6e7WrdvbhPvOeNt20Vi8eLFwcXHJcT6/W/KusNpECH6v5EdBtcvu3buFTCYTqamp2c7nsZJ3hdUmQvBYeRMcXp4HMTExAIAyZcpkO//SpUvw9/eHr6+vNC0lJQVGRkZa5YyNjZGcnIxLly7lur1NmzahXLlyqFWrFqZPn47ExMS3rEHpk12bNGvWDFu3bsWLFy+gVquxZcsWJCcno3Xr1gAAPz8/WFpaomHDhtIynp6ekMvlOH/+fLbbuXTpEtLS0uDp6SlNq1atGpycnODn56eDmpVshdUuGjxWXu9N2uRNBAcHIywsTOtYsbCwQOPGjXmsZKOw2kXj+PHjsLGxQdWqVTFy5EhERka+9TpLo4Jql5iYmBz/ZwD43ZIfhdUmGvxeyZuCaJcXL15g06ZNaNasGQwMDLItw2Ml7wqrTTR4rORTUWf9xZ1KpRKdOnUSzZs3z7HMyJEjRfXq1bWmHTx4UMjlcrF582aRnp4uHj9+LDw8PAQAsXnz5hzX9dNPP4kDBw6Ia9euiY0bN4ry5cuLHj16FFh9SoOc2iQqKkq0a9dOABD6+vpCqVSKgwcPSvMXLFggqlSpkmV91tbWYtWqVdlua9OmTUKhUGSZ3qhRIzF16tS3rEnpUpjtIgSPlbx40zbJLK89qmfOnBEAxNOnT7Wm9+7dW/Tp0+eN61AaFWa7CCHE77//Lnbv3i2uXbsmdu7cKapXry4aNWok0tPT37YqpUpBtIsQQgQGBgqlUilWr16dYxl+t+RNYbaJEPxeyau3bZepU6cKExMTAUA0adJEPH/+PMdt8VjJm8JsEyF4rLwJ/aJJ9UuO0aNH48aNGzh9+nS285OSkrB582bMmDFDa3q7du3wzTffYMSIERg0aBAMDQ0xY8YMnDp1CnJ5zgMMPvnkE+m5u7s77O3t0bZtWwQFBcHNza1gKlXC5dQmM2bMQHR0NI4cOYJy5cph165d6NOnD06dOgV3d/ciivbdUdjtwmPl9XisFE+F3S79+vWTnru7u6N27dpwc3PD8ePH0bZt2zdeb2lTEO3y5MkTtG/fHr1798awYcMKM/xSqbDbhN8refO27TJlyhT4+vriwYMHmDNnDgYPHow9e/ZAJpMVdlVKjcJuEx4rb6Cos/7ibPTo0aJChQri/v37OZb59ddfhYGBgQgPD892vlqtFk+ePBGJiYnSuav//vtvnmOIj48XAMSBAwfyHX9plFOb3Lt3TwAQN27c0Jretm1bMXz4cCGEEGvXrhWWlpZa89PS0oSenp74888/s93e0aNHBQARFRWlNd3JyUksXbr0LWtTehR2u2SHx4q2t2mTzPLaoxoUFCQAiCtXrmhNb9mypRg3bly+4y+tCrtdclKuXDnx448/vvHypU1BtMuTJ09E5cqVxaBBg4RKpcp1e/xueb3CbpPs8Hslq4L6DNN49OiRACDOnj2b7XweK69X2G2SHR4rr8dzurMhhMCYMWOwc+dO/PPPP3Bxccmx7Nq1a9G1a1dYW1tnO18mk8HBwQHGxsb4/fff4ejoiPr16+c5Fn9/fwCAvb19vupQ2ryuTTTnkbw6ikBPTw9qtRoA0LRpU0RHR2udU//PP/9ArVajcePG2W63QYMGMDAwwNGjR6VpAQEBePjwIZo2bVogdSvJiqpdssNjJUNBtMmbcHFxgZ2dndaxEhsbi/Pnz/NYQdG1S3YeP36MyMjId/5YAQquXZ48eYLWrVujQYMGWL9+fa4j2gB+t+SmqNokO/xe+Y+uPsM081JSUrKdz2MlZ0XVJtnhsZIHRZbuF2MjR44UFhYW4vjx41qXwk9MTNQqFxgYKGQymdi/f3+261m8eLG4du2auHHjhpg7d64wMDAQO3fulOY/fvxYVK1aVZw/f14IkfGL1Ny5c8XFixdFcHCw2L17t3B1dRUtW7bUWV1Lite1SWpqqqhUqZLw8PAQ58+fF/fu3RNLliwRMplM7N27V1pP+/btRb169cT58+fF6dOnReXKlbVuTfVqmwiRcasKJycn8c8//4iLFy/m6VYK74qiahceKzkrqDZ58OCBuHLlinQ7xCtXrogrV65It0UUQoiqVatqjUZYtGiRsLS0lM4f7tatG28Z9lJRtUtcXJyYPHmy8PPzE8HBweLIkSOifv36onLlyiI5Oblw34RiqCDa5fHjx6JSpUqibdu24vHjx1rr0eB3S94VVZvweyV3BdEu586dEytWrBBXrlwRISEh4ujRo6JZs2bCzc1N+jzisZJ3RdUmPFbeDJPubADI9rF+/XqtctOnTxeOjo45Dllq06aNsLCwEEZGRqJx48Zi3759WvODg4MFAHHs2DEhhBAPHz4ULVu2FGXKlBGGhoaiUqVKYsqUKbzvnchbm9y9e1f07NlT2NjYCBMTE1G7du0st0mIjIwU/fv3F2ZmZkKpVIohQ4Zo/bP6apsIIURSUpIYNWqUsLKyEiYmJqJHjx5aX9zvsqJqFx4rOSuoNvH29s52PZmPjVfXq1arxYwZM4Stra0wNDQUbdu2FQEBATqucclQVO2SmJgo2rVrJ6ytrYWBgYFwdnYWw4YNE2FhYYVQ6+KvINpl/fr1Oa5Hg98teVdUbcLvldwVRLtcu3ZNtGnTRnqPK1asKEaMGCEeP34sleGxkndF1SY8Vt6MTAgh8tU1TkRERERERER5wnO6iYiIiIiIiHSESTcRERERERGRjjDpJiIiIiIiItIRJt1EREREREREOsKkm4iIiIiIiEhHmHQTERERERER6QiTbiIiIiIiIiIdYdJNREREREREpCNMuomIqFjx8fFB9+7di2z7gwYNwldffVVk2y8IGzZsgKWlZYGuc/bs2bC1tYVMJsOuXbsKdN0lScWKFbFs2bKiDkPnmjRpgh07dhR1GEREpQKTbiIiKjQymSzXx+zZs7F8+XJs2LChSOK7evUq9u3bh3HjxhXJ9our27dvY86cOfjpp58QGhqKDh06FHVIRebChQv45JNPimz7x48fh0wmQ3R0dIGsL6cfaL788ktMmzYNarW6QLZDRPQuY9JNRESFJjQ0VHosW7YMSqVSa9rkyZNhYWFR4L20ebVixQr07t0bZmZmRbL94iooKAgA0K1bN9jZ2cHQ0DBLmdTU1MIOq0hYW1vDxMSkqMMoEGlpaTnO69ChA+Li4rB///5CjIiIqHRi0k1ERIXGzs5OelhYWEAmk2lNMzMzyzK8vHXr1hg7dizGjx8PKysr2NraYs2aNUhISMCQIUNgbm6OSpUqZUkObty4gQ4dOsDMzAy2trYYNGgQnj9/nmNsKpUK27dvR5cuXbSmr1q1CpUrV4aRkRFsbW3Rq1cvaZ5arcbChQvh4uICY2Nj1KlTB9u3b9da/ubNm+jcuTOUSiXMzc3h4eEhJbFqtRpz585FhQoVYGhoiLp16+LAgQPSsiEhIZDJZPjzzz/Rpk0bmJiYoE6dOvDz89PaxoYNG+Dk5AQTExP06NEDkZGRWvOvXr2KNm3awNzcHEqlEg0aNMDFixdzaan/zJ49W3pP5HI5ZDIZgP9OA1iwYAEcHBxQtWpVAMD169fx/vvvw9jYGGXLlsUnn3yC+Ph4aX2a5b766ivY2trC0tISc+fORXp6OqZMmYIyZcqgQoUKWL9+fa5xbd++He7u7tJ2PD09kZCQIM3/+eefUb16dRgZGaFatWpYtWqVNC81NRVjxoyBvb09jIyM4OzsjIULFwIAhBCYPXs2nJycYGhoCAcHB62RD68OL3/48CG6desGMzMzKJVK9OnTB8+ePdN6/+rWrYvffvsNFStWhIWFBfr164e4uLgc6/bgwQN06dIFVlZWMDU1Rc2aNbFv3z6EhISgTZs2AAArKyvIZDL4+PgAAA4cOIAWLVrA0tISZcuWRefOnaX9DPhvX9q6dStatWoFIyMjbNq0CUOGDEFMTIzWaBMA0NPTQ8eOHbFly5Zc24GIiPJAEBERFYH169cLCwuLLNO9vb1Ft27dpNetWrUS5ubmYt68eeLu3bti3rx5Qk9PT3To0EGsXr1a3L17V4wcOVKULVtWJCQkCCGEiIqKEtbW1mL69Oni9u3b4vLly+KDDz4Qbdq0yTGey5cvCwAiLCxMmnbhwgWhp6cnNm/eLEJCQsTly5fF8uXLpfnz588X1apVEwcOHBBBQUFi/fr1wtDQUBw/flwIIcTjx49FmTJlRM+ePcWFCxdEQECAWLdunbhz544QQoilS5cKpVIpfv/9d3Hnzh0xdepUYWBgIO7evSuEECI4OFgAENWqVRN79uwRAQEBolevXsLZ2VmkpaUJIYQ4d+6ckMvl4uuvvxYBAQFi+fLlwtLSUuu9rVmzpvjoo4/E7du3xd27d8Uff/wh/P3989ROcXFxYv369QKACA0NFaGhoVI7mZmZiUGDBokbN26IGzduiPj4eGFvby969uwprl+/Lo4ePSpcXFyEt7e3Vvuam5uL0aNHizt37oi1a9cKAMLLy0ssWLBAamMDAwPx6NGjbGN6+vSp0NfXF0uXLhXBwcHi2rVr4vvvvxdxcXFCCCE2btwo7O3txY4dO8T9+/fFjh07RJkyZcSGDRuEEEJ88803wtHRUZw8eVKEhISIU6dOic2bNwshhNi2bZtQKpVi37594sGDB+L8+fNi9erV0radnZ3Fd999J4QQQqVSibp164oWLVqIixcvinPnzokGDRqIVq1aSeVnzZolzMzMpPfk5MmTws7OTnz++ec5vuedOnUSH3zwgbh27ZoICgoSf//9tzhx4oRIT08XO3bsEABEQECACA0NFdHR0UIIIbZv3y527NghAgMDxZUrV0SXLl2Eu7u7UKlUQoj/9qWKFStK70tISIhYtmyZUCqVUttq3kMhhPjhhx+Es7NzHvYSIiLKDZNuIiIqEvlJulu0aCG9Tk9PF6ampmLQoEHStNDQUAFA+Pn5CSGEmDdvnmjXrp3Weh89eiQlK9nZuXOn0NPTE2q1Wpq2Y8cOoVQqRWxsbJbyycnJwsTERJw9e1Zruq+vr+jfv78QQojp06cLFxcXkZqamu02HRwcxIIFC7SmNWrUSIwaNUoI8V+i9PPPP0vzb968KQCI27dvCyGE6N+/v+jYsaPWOvr27av13pqbm0sJ55vYuXOnePV3em9vb2FraytSUlKkaatXrxZWVlYiPj5emrZ3714hl8ulHzO8vb2Fs7OzlAwKIUTVqlWFh4eH9FrTxr///nu28Vy6dEkAECEhIdnOd3Nzk5JojXnz5ommTZsKIYQYO3aseP/997XaWuPbb78VVapUybHNMifdhw4dEnp6euLhw4fSfE37/Pvvv0KIjKTbxMREax+aMmWKaNy4cbbrF0IId3d3MXv27GznHTt2TAAQUVFROS4vhBARERECgLh+/boQ4r99admyZVrlcjoOhRBi9+7dQi6Xa7UVERHlH4eXExFRsVe7dm3puZ6eHsqWLQt3d3dpmq2tLQAgPDwcQMZw6mPHjsHMzEx6VKtWDQC0htxmlpSUBENDQ2n4NAB88MEHcHZ2hqurKwYNGoRNmzYhMTERAHDv3j0kJibigw8+0NrOr7/+Km3D398fHh4eMDAwyLK92NhYPH36FM2bN9ea3rx5c9y+fTvH+tvb22vV9fbt22jcuLFW+aZNm2q9njhxIj7++GN4enpi0aJFOb4H+eXu7g6FQiG9vn37NurUqQNTU1Ot+qjVagQEBEjTatasCbn8v39BbG1ttdpT08aaOr6qTp06aNu2Ldzd3dG7d2+sWbMGUVFRAICEhAQEBQXB19dXq13mz58v1dvHxwf+/v6oWrUqxo0bh0OHDknr7t27N5KSkuDq6ophw4Zh586dSE9PzzaO27dvw9HREY6OjtK0GjVqwNLSUqsNK1asCHNzc+m1vb19jnUDgHHjxmH+/Plo3rw5Zs2ahWvXruVYViMwMBD9+/eHq6srlEolKlasCCBj+HtmDRs2fO26NIyNjaFWq5GSkpLnZYiIKCsm3UREVOy9mrTKZDKtaZpEWXOl5fj4eHTp0gX+/v5aj8DAQLRs2TLbbZQrVw6JiYlaFwQzNzfH5cuX8fvvv8Pe3h4zZ85EnTp1EB0dLZ2nvHfvXq1t3Lp1Szqv29jYuMDr/2pd82L27Nm4efMmOnXqhH/++Qc1atTAzp073zquzMl1fryuPTXTcqqjnp4eDh8+jP3796NGjRpYsWIFqlatiuDgYKld1qxZo9UuN27cwLlz5wAA9evXR3BwMObNm4ekpCT06dNHOlff0dERAQEBWLVqFYyNjTFq1Ci0bNky14uOvUl9c2u/jz/+GPfv38egQYNw/fp1NGzYECtWrMh1G126dMGLFy+wZs0anD9/HufPnweQ9QJ3+WmzFy9ewNTUtMD2YyKidxWTbiIiKnXq16+PmzdvomLFiqhUqZLWI6eko27dugCAW7duaU3X19eHp6cnFi9ejGvXriEkJERKXA0NDfHw4cMs29D0fNauXRunTp3KNmFTKpVwcHDAmTNntKafOXMGNWrUyHNdq1evLiVYGprkMrMqVapgwoQJOHToEHr27PnaC5W9ierVq+Pq1ataFzQ7c+YM5HK5dKG1giKTydC8eXPMmTMHV65cgUKhwM6dO2FrawsHBwfcv38/S7u4uLhIyyuVSvTt2xdr1qzB1q1bsWPHDrx48QJAxo8lXbp0wf/+9z8cP34cfn5+uH79erb1ffToER49eiRNu3XrFqKjo/PVhtlxdHTEiBEj8Oeff2LSpElYs2YNAEgjC1QqlVQ2MjISAQEB+PLLL9G2bVtUr15d6vl/HYVCobWuzG7cuIF69eq9VT2IiAjQL+oAiIiICtro0aOxZs0a9O/fH1OnTkWZMmVw7949bNmyBT///DP09PSyLGNtbY369evj9OnTUgK+Z88e3L9/Hy1btoSVlRX27dsHtVqNqlWrwtzcHJMnT8aECROgVqvRokULxMTE4MyZM1AqlfD29saYMWOwYsUK9OvXD9OnT4eFhQXOnTuH9957D1WrVsWUKVMwa9YsuLm5oW7duli/fj38/f2xadOmPNd13LhxaN68OZYsWYJu3brh4MGDWldAT0pKwpQpU9CrVy+4uLjg8ePHuHDhAj788MO3fp9fNXDgQMyaNQve3t6YPXs2IiIiMHbsWAwaNEg6BaAgnD9/HkePHkW7du1gY2OD8+fPIyIiAtWrVwcAzJkzB+PGjYOFhQXat2+PlJQUXLx4EVFRUZg4cSKWLl0Ke3t71KtXD3K5HNu2bYOdnR0sLS2xYcMGqFQqNG7cGCYmJti4cSOMjY3h7OycJQ5PT0+4u7tj4MCBWLZsGdLT0zFq1Ci0atUqX8O4XzV+/Hh06NABVapUQVRUFI4dOybVzdnZGTKZDHv27EHHjh1hbGwMKysrlC1bFqtXr4a9vT0ePnyIadOm5WlbFStWRHx8PI4ePYo6derAxMREuiXaqVOn0K5duzeuBxERZWBPNxERlTqaHmSVSoV27drB3d0d48ePh6Wlpda5xK/6+OOPtRJeS0tL/Pnnn3j//fdRvXp1/Pjjj/j9999Rs2ZNAMC8efMwY8YMLFy4ENWrV0f79u2xd+9eqUe1bNmy+OeffxAfH49WrVqhQYMGWLNmjTTceNy4cZg4cSImTZoEd3d3HDhwAH/99RcqV66c57o2adIEa9aswfLly1GnTh0cOnQIX375pTRfT08PkZGRGDx4MKpUqYI+ffqgQ4cOmDNnjlRGJpNhw4YNed5mTkxMTHDw4EG8ePECjRo1Qq9evdC2bVusXLnyrdedmVKpxMmTJ9GxY0dUqVIFX375Jb799lt06NABQEY7/vzzz1i/fj3c3d3RqlUrbNiwQWoXc3NzLF68GA0bNkSjRo0QEhKCffv2QS6Xw9LSEmvWrEHz5s1Ru3ZtHDlyBH///TfKli2bJQ6ZTIbdu3fDysoKLVu2hKenJ1xdXbF169a3qp9KpcLo0aOlfapKlSrSLc/Kly+POXPmYNq0abC1tcWYMWMgl8uxZcsWXLp0CbVq1cKECRPwzTff5GlbzZo1w4gRI9C3b19YW1tj8eLFAIAnT57g7NmzGDJkyFvVhYiIAJkQQhR1EERERMVBUlISqlatiq1bt2a5GFlpFRwcjCpVquDWrVv5SvapdPvss88QFRWF1atXF3UoREQlHoeXExERvWRsbIxff/0Vz58/L+pQCs2+ffvwySefMOEmLTY2Npg4cWJRh0FEVCqwp5uIiIiIiIhIR3hONxEREREREZGOMOkmIiIiIiIi0hEm3UREREREREQ6wqSbiIiIiIiISEeYdBMRERERERHpCJNuIiIiIiIiIh1h0k1ERERERESkI0y6iYiIiIiIiHSESTcRERERERGRjjDpJiIiIiIiItKR/wPM8jszrEyOywAAAABJRU5ErkJggg==", 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simultaneous_recordingsstimulus_typeprotocol
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sequential_recordingscell_idais_statuselectrode_name
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