cd-network 0.1.1

A framework designed to calculate the output of neurons based on non-homogeneous Poisson processes and rate statistic calculations

CD-Network

CD-Network is a Python library designed for the analytical derivation of the stochastic output of coincidence detector (CD) cells. These cells receive inputs modeled as non-homogeneous Poisson processes (NHPP) with both excitatory and inhibitory components.

Features

CD Cells

ei(excitatory_input, inhibitory_inputs, delta_s, fs)

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.

• Parameters:

  • excitatory_input (np.ndarray): 1D array of spike times or binary spikes from the excitatory neuron.
  • inhibitory_inputs (np.ndarray): 1D or 2D array of spike times or binary spikes from 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.

• Returns:

  • np.ndarray: Output spike times or binary spike array after applying the excitatory-inhibitory interaction.

simple_ee(inputs, delta_s, fs)

Simplifies the model of excitatory-excitatory (EE) interaction where an output spike is generated whenever both inputs spike within a specified time interval.

• Parameters:

  • inputs (np.ndarray): 2D array of excitatory input spikes.
  • delta_s (float): Coincidence integration duration in seconds.
  • fs (float): Sampling frequency in Hz.

• Returns:

  • np.ndarray: Output spike times or binary spike array after applying the EE interaction.

ee(inputs, n_spikes, delta_s, fs)

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.

• Parameters:

  • inputs (np.ndarray): 2D array of excitatory input spikes.
  • 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.

• Returns:

  • np.ndarray: Output spike times or binary spike array based on the input conditions.

cd(excitatory_inputs, inhibitory_inputs, n_spikes, delta_s, fs)

Models the output of a coincidence detector (CD) cell which generates spikes based on the relative timing and number of excitatory and inhibitory inputs within a defined interval.

• Parameters:

  • excitatory_inputs (np.ndarray): 2D array of excitatory input spikes.
  • inhibitory_inputs (np.ndarray): 2D array of inhibitory input spikes.
  • n_spikes (int): Minimum excess of excitatory spikes over inhibitory spikes required to generate an output spike.
  • delta_s (float): Interval length in seconds.
  • fs (float): Sampling frequency in Hz.

• Returns:

  • np.ndarray: Output spike array after applying the CD interaction based on the relative timing and number of inputs.

Installation

You can install CD-Network directly from the source code:

git clone https://github.com/nuniz/CoincidenceDetectionNetwork.git
cd CoincidenceDetectionNetwork
pip install .

Contribution

run pre-commit to check all files in the repo.

pre-commit run --all-files

Reference

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.