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https://github.com/nuniz/coincidencedetectionnetwork
Analytical derivation of the stochastic output of coincidence detection (CD) neurons
https://github.com/nuniz/coincidencedetectionnetwork
auditory-nerve brainstem-neuron cd cochlea coincidence-detection hearing hearing-aids neural-network neuron neuron-simulator neurons physical-modeling poission
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Analytical derivation of the stochastic output of coincidence detection (CD) neurons
- Host: GitHub
- URL: https://github.com/nuniz/coincidencedetectionnetwork
- Owner: nuniz
- License: mit
- Created: 2024-07-07T19:02:55.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-16T06:04:45.000Z (4 months ago)
- Last Synced: 2024-09-17T00:42:41.359Z (2 months ago)
- Topics: auditory-nerve, brainstem-neuron, cd, cochlea, coincidence-detection, hearing, hearing-aids, neural-network, neuron, neuron-simulator, neurons, physical-modeling, poission
- Language: Jupyter Notebook
- Homepage:
- Size: 477 KB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CD-Network
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.
[](https://zenodo.org/doi/10.5281/zenodo.12746265)
## Features
Each cell can run individually through its respective function (ei, simple_ee, ee, cd), or be configured via a network
file.
### Dynamic Connections (CD Network)
Define how cells are interconnected within the network and how external inputs affect cell
responses.
```python
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:
```python
network.plot_network_connections()
```
### Configuration File
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.
[Example Configuration File](example_notebooks/config.json)
### CD Cells
[My notes about CD neurons](notes.pdf)
#### `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 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.
- **Returns:**
- `np.ndarray`: Output instantaneous rates array after applying the excitatory-inhibitory interaction.
#### `simple_ee(inputs, delta_s, fs)`
Simplifies the model of excitatory-excitatory (EE) interaction where an output spikes rate is generated whenever both inputs
spike within a specified time interval.
- **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.
- **Returns:**
- `np.ndarray`: Output instantaneous rates 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 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.
- **Returns:**
- `np.ndarray`: Output instantaneous rates 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 rate 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 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.
- **Returns:**
- `np.ndarray`: Output instantaneous rates after applying the CD interaction based on the relative timing and number of
inputs.
## Installation
You can install CD-Network directly from pypi:
```bash
pip install cd_network
```
Or you can install CD-Network directly from the source code:
```bash
git clone https://github.com/nuniz/CoincidenceDetectionNetwork.git
cd CoincidenceDetectionNetwork
pip install .
```
## Contribution
Before contributing, run pre-commit to check all files in the repo.
```bash
pre-commit run --all-files
```
## Citation
If 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.
## Further Readings
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.