src.canns.analyzer.utils

Functions

firing_rate_to_spike_train(firing_rates, dt_rate, dt_spike)	Converts a low-resolution firing rate signal to a high-resolution binary spike train.
normalize_firing_rates(firing_rates[, method])	Normalizes firing rates to a range of [0, 1] based on the maximum firing rate.
spike_train_to_firing_rate(spike_train, dt_spike, dt_rate)	Converts a high-resolution spike train to a low-resolution firing rate signal.

Module Contents

src.canns.analyzer.utils.firing_rate_to_spike_train(firing_rates, dt_rate, dt_spike)

Converts a low-resolution firing rate signal to a high-resolution binary spike train.

This function generates spikes using a Bernoulli process in each high-resolution time bin. The probability of a spike in each bin is calculated as: P(spike in dt_spike) = rate (spikes/dt_rate) / dt_rate (sec) * dt_spike (sec)

Note

A Bernoulli process is used, not a Poisson process. This means that in each time bin, at most one spike can occur. For high firing rates, the computed spike probability may exceed 1 and will be clipped to 1. This can lead to deviations from the expected Poisson statistics at high rates.

Parameters:

• firing_rates (np.ndarray) – 2D array of shape (timesteps_rate, num_neurons) with firing rates in dt_rate.

• dt_rate (float) – The time step of the input firing rate in seconds (e.g., 0.1s).

• dt_spike (float) – The desired time step of the output spike train in seconds (e.g., 0.001s).

Returns:

A 2D integer array of shape (timesteps_spike, num_neurons) with binary values (0 or 1) representing the high-resolution spike train.

Return type:

np.ndarray

src.canns.analyzer.utils.normalize_firing_rates(firing_rates, method='min_max')

Normalizes firing rates to a range of [0, 1] based on the maximum firing rate.

Parameters:

• firing_rates (np.ndarray) – 2D array of shape (timesteps_rate, num_neurons) with firing rates in dt_rate.

• method (str) – Normalization method, either ‘min_max’ or ‘z_score’. - ‘min_max’: Normalizes to the range [0, 1]. - ‘z_score’: Normalizes to have mean 0 and standard deviation 1.

Returns:

A 2D array of shape (timesteps_rate, num_neurons) with normalized firing rates.

Return type:

np.ndarray

src.canns.analyzer.utils.spike_train_to_firing_rate(spike_train, dt_spike, dt_rate)

Converts a high-resolution spike train to a low-resolution firing rate signal.

This function bins the spikes into larger time windows (dt_rate) and calculates the average firing rate for each bin.

Parameters:

• spike_train (np.ndarray) – 2D array of shape (timesteps_spike, num_neurons) representing the high-res spike train.

• dt_spike (float) – The time step of the input spike train in seconds (e.g., 0.001s).

• dt_rate (float) – The desired time step of the output firing rate in dt_rate (e.g., 0.1s).

Returns:

A 2D array of shape (timesteps_rate, num_neurons) with firing rates in Hz.

Return type:

np.ndarray