Loader class for CBGTC results (in respective files)

class analyseur.cbgtc.loader.CommonLoader(full_filepath=' ')

This is the parent class for LoadSpikeTimes and LoadChannelVorG

• Instantiated with the full file path

  • sets atrributes: full_filepath, filename

• Contains instance method get_region_name()

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get_region_name(nucleus)

Returns region name for respective nucleus name for which the spike times are in the file.

Nuclei	Region name
[“CSN”, “PTN”, “IN”]	“cortex”
[“FSI”, “GPe”, “GPi”, “MSN”, “STN”]	“bg”
[“TRN”, “MD”]	“thalamus”

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class analyseur.cbgtc.loader.FetchConnectionList

Fetches the connection_lists_i.dat and connection_lists_j.dat from connection_list/.

Methods
within_cortex()
within_bg()
within_thalamus()
cortex_to_bg()
cortex_to_thalamus()
thalamus_to_cortex()
bg_to_thalamus()

Use Cases

1. Pre-requisites

1.1. Import Modules and Instantiate

from analyseur.cbgtc.loader import FetchConnectionList

loadST = LoadSpikeTimes("spikes_GPi.csv")

2. Cases

2.1. Load file and fetch the connection lists

conn_i, self.conn_j = fetch(rootfolder=folder_path, verbose=True)

such that the folder_path is the CBGT data directory whose structure is shown below

.
├── BG/
│   ├── connection_list/
│   │   ├── scale=4_nbchannels=4/
│   │   │   └── model_9/
│   │   └── active_cortex_inputs_scale=4_nbchannels=4/
│   │       └── model_9/
│   └── ...
├── CORTEX/
│   ├── connection_list/
│   │   ├── Thalamus_inputs_nbpops=4/
│   │   └── nbpops=4/
│   └── ...
├── THALAMUS/
│   ├── connection_list/
│   │   ├── nbpops=4/
│   │   ├── BG_inputs_nbpops=4/
│   │   └── active_cortex_inputs_nbpops=4/
│   └── ...
├── ...
:

where

• terminal folders in connection_list/ contains files connection_lists_i.dat and connection_lists_j.dat

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classmethod bg_to_thalamus(rootfolder=None, verbose=False, nuclei_filter=False)

Allows to fetch the synapses connection lists between the basal ganglia and thalamus.

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classmethod cortex_to_bg(rootfolder=None, verbose=False, nuclei_filter=False)

Allows to fetch the synapses connection lists between the cortex and basal ganglia.

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classmethod cortex_to_thalamus(rootfolder=None, verbose=False, nuclei_filter=False)

Allows to fetch the synapses connection lists between the cortex and thalamus.

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classmethod thalamus_to_cortex(rootfolder=None, verbose=False, nuclei_filter=False)

Allows to fetch the synapses connection lists between the thalamus and cortex.

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classmethod within_bg(rootfolder=None, verbose=False, nuclei_filter=False)

Allows to fetch the synapses connection lists within basal ganglia.

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classmethod within_cortex(rootfolder=None, verbose=False, nuclei_filter=False)

Allows to fetch the synapses connection lists within cortex.

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classmethod within_thalamus(rootfolder=None, verbose=False, nuclei_filter=False)

Allows to fetch the synapses connection lists within thalamus.

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class analyseur.cbgtc.loader.LoadChannelIorG(full_filepath=' ')

Loads the csv file containing measureables (currents and conductances) mean across the first 400 neurons in a particular nucleus and returns all their measurables in milliseconds by calling get_measurables().

Methods	Argument	Return
get_measurables()	no arguments instantiated with full file path	1-D array with respective measuralble sampled at 1 milliseconds key value is a array of spike times for respective neuron n<X>

NOTE: Unlike spike times (from get_spiketimes_superset()) whose time axis is in seconds, the time axis for the measurables is in milliseconds.

Use Cases

1. Pre-requisites

1.1. Import Modules and Instantiate

from analyseur.cbgtc.loader import LoadChannelIorG

loadIG = LoadChannelIorG("CSN_V_syn_GABAA_1msgrouped_mean_preprocessed4Matlab_SHIFT.csv")

2. Cases

2.1. Load file and get the whole spike times

I_GABAB_for_CSN = loadIG.get_measurables()

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get_measurables()

Returns a 1-D array (numpy.array data type) containing the measureables (currents and conductances) whose time axis is in milliseconds.

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class analyseur.cbgtc.loader.LoadMembraneVorI(full_filepath=' ')

get_measurables()

Returns a 1-D array (numpy.array data type) containing the measureables (currents and conductances) whose time axis is in milliseconds.

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class analyseur.cbgtc.loader.LoadSpikeTimes(full_filepath=' ')

Loads the csv file containing spike times for all the neurons in a particular nucleus and returns all their spike times in seconds by calling get_spiketimes_superset().

Methods	Argument	Return
get_spiketimes_superset()	no arguments instantiated with full file path	dictionary with keys, n<X> where X ∊ [0, N] ⊂ 𝗭 key value is a array of spike times for respective neuron n<X>
get_spiketimes_subset()	superset (return of get_spiketimes_superset()) “neurons” (“all”, range or list)	dictionary with keys, n<X> where X ∊ neurons key value is a array of spike times for respective neuron n<X>

Use Cases

1. Pre-requisites

1.1. Import Modules and Instantiate

from analyseur.cbgtc.loader import LoadSpikeTimes

loadST = LoadSpikeTimes("spikes_GPi.csv")

2. Cases

2.1. Load file and get the whole spike times

spiketimes_superset = loadST.get_spiketimes_superset()

2.2. From the whole spike times get a subset; specific range

neurons = range(30, 62)  # neuron id from "n30" to "n62"
spiketimes_subset = LoadSpikeTimes.get_spiketimes_subset(spiketimes_superset, neurons=neurons)

2.3. From the whole spike times get a subset; specific list

neurons = [1, 2, 3, 6, 9, 10, 11, 21, 31]  # neuron ids "n1", "n2", ..., "n21", "n31"
spiketimes_subset = LoadSpikeTimes.get_spiketimes_subset(spiketimes_superset, neurons=neurons)

2.4. From the whole spike times get a subset; first N neurons

N = 50  # first 50 neurons regardless of the neuron id
spiketimes_subset = LoadSpikeTimes.get_spiketimes_subset(spiketimes_superset, neurons=N)

2.5 Superset and subset are the same

spiketimes_subset = LoadSpikeTimes.get_spiketimes_subset(spiketimes_superset, neurons="all")

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extract_nucleus_name(filename)

Extracts <nucleus> name from spikes_<nucleus>.csv

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static get_spiketimes_subset(spiketimes_superset, window=None, neurons=None)

Returns a dictionary containing the spike times (in seconds) of desired neurons.

Parameters:

• spiketimes_superset – Dictionary returned using get_spiketimes_superset()

• window – Tuple in the form (start_time, end_time); (0, 10) [default]

• neurons –

  “all” or scalar or range(a, b) or list of neuron ids like [2, 3, 6, 7]

  • ”all” means subset = superset

  • N (a scalar) means subset of first N neurons in the superset

  • range(a, b) or [2, 3, 6, 7] means subset of selected neurons

Returns:

dictionary

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get_spiketimes_superset()

Returns a dictionary containing the spike times (numpy.array data type) in seconds for all the neurons recorded into the file as value of the key n<X> where \(X \in [0, N] \subset \mathbb{Z}\).

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