Note
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Spike synchronization through subthreshold oscillation¶
This script reproduces the spike synchronization behavior of integrate-and-fire neurons in response to a subthreshold oscillation. This phenomenon is shown in Fig. 1 of 1
Neurons receive a weak 35 Hz oscillation, a gaussian noise current and an increasing DC. The time-locking capability is shown to depend on the input current given. The result is then plotted using matplotlib. All parameters are taken from the above paper.
References¶
- 1
Brody CD and Hopfield JJ (2003). Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37, 843-852.
First, we import all necessary modules for simulation, analysis, and plotting.
import nest
import nest.raster_plot
import matplotlib.pyplot as plt
Second, the simulation parameters are assigned to variables.
N = 1000 # number of neurons
bias_begin = 140. # minimal value for the bias current injection [pA]
bias_end = 200. # maximal value for the bias current injection [pA]
T = 600 # simulation time (ms)
# parameters for the alternating-current generator
driveparams = {'amplitude': 50., 'frequency': 35.}
# parameters for the noise generator
noiseparams = {'mean': 0.0, 'std': 200.}
neuronparams = {'tau_m': 20., # membrane time constant
'V_th': 20., # threshold potential
'E_L': 10., # membrane resting potential
't_ref': 2., # refractory period
'V_reset': 0., # reset potential
'C_m': 200., # membrane capacitance
'V_m': 0.} # initial membrane potential
Third, the nodes are created using Create
. We store the returned handles
in variables for later reference.
neurons = nest.Create('iaf_psc_alpha', N)
sr = nest.Create('spike_recorder')
noise = nest.Create('noise_generator')
drive = nest.Create('ac_generator')
Set the parameters specified above for the generators using set
.
drive.set(driveparams)
noise.set(noiseparams)
Set the parameters specified above for the neurons. Neurons get an internal current. The first neuron additionally receives the current with amplitude bias_begin, the last neuron with amplitude bias_end.
neurons.set(neuronparams)
neurons.I_e = [(n * (bias_end - bias_begin) / N + bias_begin)
for n in range(1, len(neurons) + 1)]
Connect alternating current and noise generators as well as `spike_recorder`s to neurons
nest.Connect(drive, neurons)
nest.Connect(noise, neurons)
nest.Connect(neurons, sr)
Simulate the network for time T.
nest.Simulate(T)
Plot the raster plot of the neuronal spiking activity.
nest.raster_plot.from_device(sr, hist=True)
plt.show()
Total running time of the script: ( 0 minutes 0.000 seconds)