CD-Network is a Python library designed for the analytical derivation of the stochastic output (instantaneous spikes rate) of coincidence detection (CD) neurons, based on non-homogeneous Poisson processes.
Each cell can run individually through its respective function (ei, simple_ee, ee, cd), or be configured via a network file.
Define how cells are interconnected within the network and how external inputs affect cell responses.
import numpy as np
from cd_network.network import CDNetwork
if __name__ == '__main__':
# Load the neural network configuration from a JSON file
config_path = r'config.json' # Path to the configuration file
network = CDNetwork(config_path)
# Define external inputs for the network
external_inputs = {
'external1': np.random.randn(1000),
'external2': np.random.randn(1000),
'external3': np.random.randn(1000)
}
# Run the network with the provided external inputs
outputs = network(external_inputs)
# Print the outputs of the network
print(outputs)To visualize the network's connections, use:
network.plot_network_connections()The CD network uses a JSON configuration file. Below is a breakdown of the configuration structure:
fs: Sampling frequency in Hz. This value is used across all cells for time-based calculations.
cells: An array of objects where each object represents a neural cell and its specific parameters:
type: Specifies the type of the cell (e.g., ei, simple_ee, cd).
id: A unique identifier for the cell.
params: Parameters specific to the cell type, such as delta_s for the time window in seconds and n_spikes for the minimum number of spikes required.
connections: An array defining the connections between cells or from external inputs to cells:
source: Identifier for the source of the input. This can be an external source or another cell.
target: Identifier for the cell receiving the input.
input_type: Specifies whether the input is excitatory or inhibitory.
Computes the output of an excitatory-inhibitory (EI) neuron model. The model outputs spikes based on the excitatory inputs, except when inhibited by any preceding spikes within a specified time window from the inhibitory inputs.
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Parameters:
excitatory_input (np.ndarray): 1D or 2D array of instantaneous rates of one or more excitatory neuron.inhibitory_inputs (np.ndarray): 1D or 2D array of instantaneous rate of one or more inhibitory neurons.delta_s (float): Coincidence integration duration in seconds, defining the time window for inhibition.fs (float): Sampling frequency in Hz.
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Returns:
np.ndarray: Output instantaneous rates array after applying the excitatory-inhibitory interaction.
Simplifies the model of excitatory-excitatory (EE) interaction where an output spikes rate is generated whenever both inputs spike within a specified time interval.
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Parameters:
inputs (np.ndarray): 2D array of excitatory input instantaneous rates.delta_s (float): Coincidence integration duration in seconds.fs (float): Sampling frequency in Hz.
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Returns:
np.ndarray: Output instantaneous rates array after applying the EE interaction.
A general excitatory-excitatory (EE) cell model that generates a spike whenever at least a minimum number of its inputs spike simultaneously within a specific time interval.
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Parameters:
inputs (np.ndarray): 2D array of excitatory input instantaneous rates.n_spikes (int): Minimum number of inputs that must spike simultaneously.delta_s (float): Coincidence integration duration in seconds.fs (float): Sampling frequency in Hz.
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Returns:
np.ndarray: Output instantaneous rates array based on the input conditions.
Models the output of a coincidence detector (CD) cell which generates spikes rate based on the relative timing and number of excitatory and inhibitory inputs within a defined interval.
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Parameters:
excitatory_inputs (np.ndarray): 2D array of excitatory input instantaneous rates.inhibitory_inputs (np.ndarray): 2D array of inhibitory input instantaneous rates.n_spikes (int): Minimum excess of excitatory spikes over inhibitory spikes required to generate an output spikes rate.delta_s (float): Interval length in seconds.fs (float): Sampling frequency in Hz.
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Returns:
np.ndarray: Output instantaneous rates after applying the CD interaction based on the relative timing and number of inputs.
You can install CD-Network directly from pypi:
pip install cd_networkOr you can install CD-Network directly from the source code:
git clone https://github.com/nuniz/CoincidenceDetectionNetwork.git
cd CoincidenceDetectionNetwork
pip install .Before contributing, run pre-commit to check all files in the repo.
pre-commit run --all-filesIf you use this software, please cite it as below.
@software{asaf_zorea_2023_8004059,
author = {Asaf Zorea},
title = {CoincidenceDetectionNetwork: Analytical derivation of the stochastic output of coincidence detection neurons},
month = jun,
year = 2024,
publisher = {Zenodo},
version = {v0.1.4},
doi = {10.5281/zenodo.12746266},
url = {https://doi.org/10.5281/zenodo.12746266}
}
Krips R, Furst M. Stochastic properties of coincidence-detector neural cells. Neural Comput. 2009 Sep;21(9):2524-53. doi: 10.1162/neco.2009.07-07-563. PMID: 19548801.
Zorea Asaf, and Miriam Furst. Contribution of Coincidence Detection to Speech Segregation in Noisy Environments. arXiv:2405.06072, arXiv, 9 May 2024. arXiv.org, https://doi.org/10.48550/arXiv.2405.06072.
Krips R, Furst M. Stochastic properties of auditory brainstem coincidence detectors in binaural perception. J Acoust Soc Am. 2009 Mar;125(3):1567-83. doi: 10.1121/1.3068446. PMID: 19275315.