SNN

Repository with several implementations of neural network models.
It's still in development.

Neurons implemented

Variation of voltage can be obtained with Runge Kutta Fourth Method or Euler Method.

Neuron	Implementation	Tests	Results
Hodking Huxley	HH.py	HH-test.py	HH
Izhikevich	IZHI.py	IZHI-test.py	IZHI
Leaky Integrate and Fire	LIF.py	LIF-test.py	LIF
Leaky Integrate and Fire - Synapses	LIF_Synapse.py	LIF_Synapse-test.py	LIF_SYN

Demonstration of result - Izhikevich Neuron

The code for this model was developed using the paper written by Izhikevich with the constant variables described at the document. This is the Code for a single neuron. The threshold used was -30 mV.

The parameter used to generate these graphs were:

        a = 0.1 or 0.02   # How fast the recovery is. It's proportional to the frequency of spikes for a constant input. 
        b = .2            # Sensitivity of the variable u to the subthreshold membrane fluctuation 
        c = -65           # After spike reset value of the voltage
        d = 8             # After spike reset value of the u variable 

This graph shows the fast and regular spiking with constant a = 0.1

This graph show the slow and regular spiking with constant a = 0.02

Izhikevich Random Network

This is a random network taken from the Izhikevich Paper with no learning and 750 Excitatory and 250 Inhibitory neurons. The results are just as expected.

References:

http://worldcomp-proceedings.com/proc/p2013/BIC3207.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1392413/pdf/jphysiol01442-0106.pdf
http://www.math.mcgill.ca/gantumur/docs/reps/RyanSicilianoHH.pdf
http://www.math.pitt.edu/~bdoiron/assets/ermentrout-and-terman-ch-1.pdf https://neuronaldynamics.epfl.ch/online/Ch1.S3.html
Izhikevich Paper
Hodgkin Huxel Paper