def sim_synaptic_current(n_seconds,
fs,
n_neurons=1000,
firing_rate=2.,
tau_r=0.,
tau_d=0.01,
t_ker=None):
"""Simulate a signal as a synaptic current, which has 1/f characteristics with a knee.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
n_neurons : int, optional, default: 1000
Number of neurons in the simulated population.
firing_rate : float, optional, default: 2
Firing rate of individual neurons in the population.
tau_r : float, optional, default: 0.
Rise time of synaptic kernel, in seconds.
tau_d : float, optional, default: 0.01
Decay time of synaptic kernel, in seconds.
t_ker : float, optional
Length of time of the simulated synaptic kernel, in seconds.
Returns
-------
sig : 1d array
Simulated synaptic current.
Notes
-----
The resulting signal is most similar to unsigned intracellular current or conductance change.
Examples
--------
Simulate a synaptic current signal:
>>> sig = sim_synaptic_current(n_seconds=1, fs=500)
"""
# If not provided, compute t_ker as a function of decay time constant
if t_ker is None:
t_ker = 5. * tau_d
# Simulate an extra bit because the convolution will trim & turn off normalization
sig = sim_poisson_pop((n_seconds + t_ker),
fs,
n_neurons,
firing_rate,
mean=None,
variance=None)
ker = sim_synaptic_kernel(t_ker, fs, tau_r, tau_d)
sig = np.convolve(sig, ker, 'valid')[:-1]
return sig
def sim_2exp_cycle(n_seconds, fs, tau_r, tau_d):
return sim_synaptic_kernel(n_seconds, fs, tau_r=tau_r, tau_d=tau_d)
def sim_synaptic_current(n_seconds,
fs,
n_neurons=1000,
firing_rate=2.,
tau_r=0.,
tau_d=0.01,
t_ker=None):
"""Simulate a signal as a synaptic current, which has 1/f characteristics with a knee.
Parameters
----------
n_seconds : float
Simulation time, in seconds.
fs : float
Sampling rate of simulated signal, in Hz.
n_neurons : int, optional, default: 1000
Number of neurons in the simulated population.
firing_rate : float, optional, default: 2
Firing rate of individual neurons in the population.
tau_r : float, optional, default: 0.
Rise time of synaptic kernel, in seconds.
tau_d : float, optional, default: 0.01
Decay time of synaptic kernel, in seconds.
t_ker : float, optional
Length of time of the simulated synaptic kernel, in seconds.
Returns
-------
sig : 1d array
Simulated synaptic current.
Notes
-----
- This simulation is based on the one used in [1]_.
- The resulting signal is most similar to unsigned intracellular current or conductance change.
References
----------
.. [1] Gao, R., Peterson, E. J., & Voytek, B. (2017). Inferring synaptic
excitation/inhibition balance from field potentials. NeuroImage, 158, 70–78.
DOI: https://doi.org/10.1016/j.neuroimage.2017.06.078
Examples
--------
Simulate a synaptic current signal:
>>> sig = sim_synaptic_current(n_seconds=1, fs=500)
"""
# If not provided, compute t_ker as a function of decay time constant
if t_ker is None:
t_ker = 5. * tau_d
# Simulate an extra bit because the convolution will trim & turn off normalization
sig = sim_poisson_pop((n_seconds + t_ker),
fs,
n_neurons,
firing_rate,
mean=None,
variance=None)
ker = sim_synaptic_kernel(t_ker, fs, tau_r, tau_d)
sig = np.convolve(sig, ker, 'valid')[:compute_nsamples(n_seconds, fs)]
return sig