- class brainpy.measure.unitary_LFP(times, spikes, spike_type, xmax=0.2, ymax=0.2, va=200.0, lambda_=0.2, sig_i=2.1, sig_e=3.1500000000000004, location='soma layer', seed=None)#
A kernel-based method to calculate unitary local field potentials (uLFP) from a network of spiking neurons .
This method calculates LFP only from the neuronal spikes. It does not consider the subthreshold synaptic events, or the dendritic voltage-dependent ion channels.
If you have spike data of excitatory and inhibtiory neurons, you can get the LFP by the following methods:
>>> import brainpy as bp >>> n_time = 1000 >>> n_exc = 100 >>> n_inh = 25 >>> times = bm.arange(n_time) * 0.1 >>> exc_sps = bp.math.random.random((n_time, n_exc)) < 0.3 >>> inh_sps = bp.math.random.random((n_time, n_inh)) < 0.4 >>> lfp = bp.measure.unitary_LFP(times, exc_sps, 'exc') >>> lfp += bp.measure.unitary_LFP(times, inh_sps, 'inh')
times (ndarray) – The times of the recording points.
spikes (ndarray) – The spikes of excitatory neurons recorded by brainpy monitors.
spike_type (str) – The neuron type of the spike trains. It can be “exc” or “inh”.
location (str) – The location of the spikes recorded. It can be “soma layer”, “deep layer”, “superficial layer” and “surface”.
xmax (float) – Size of the array (in mm).
ymax (float) – Size of the array (in mm).
lambda (float) – The space constant (mm).
sig_i (float) – The std-dev of inhibition (in ms)
sig_e (float) – The std-dev for excitation (in ms).
seed (int) – The random seed.