signalAnalysis

NeuromorphicControlToolbox.signalAnalysis.find_spikes(V, T, threshold)

Compute the spike times of a voltage trace or logical trace.

Description:

• spikes = find_spikes(V, T) –

  • If V is numeric, then compute the spiking times with the base threshold of 0.

  • If V is logical, then compute the spiking times based on the rising and falling edges.

• spikes = find_spikes(V, T, threshold) –

  • If V is numeric, then compute the spiking times with the given threshold.

  • If V is logical, then ignore given treshold and compute the spiking times based on the rising and falling edges.

Inputs:

• V (vector) – Value of the voltage.

• T (vector) – Sampling times of the voltage.

Optional Input:

threshold (numeric) – Value of the treshold.

Output:

spikes (matrix) – Returns a Nx3 matrix where N is the number of spikes and, for each row, the the first value is the start of the spike, the second the spike of the and the last the time of the peak. If the peak is not computable the last value is NaN.

NeuromorphicControlToolbox.signalAnalysis.find_bursts(spikes, burst_length_factor)

Compute from the spiking times if the trace contains burst and to which burst each spike belongs based on the interspike times.

Description:

• bursts = find_bursts(spikes) – Computes the bursts with a default grouping factor of 4 between the lower and higher group interspike times.

• bursts = find_bursts(spikes, burst_length_factor) – Computes the bursts with a default a custom grouping factor between the lower and higher group interspike times.

Inputs:

spikes (matrix) – Matrix Nx3 as returned from find_spikes() containing the spikes.

Optional Input:

burst_length_factor (numeric) – Value of the grouping factor.

Output:

bursts (cell array) – Cell array containing matrix Nx3 as returned from find_spikes(). Each cell represent the bursts and the matrix represents each spikes in the burst. An empty cell array is returned if the signal is not deemed bursting according to the grouping factor.