pyN(euron)

pyN (pronounced 'pine') is a python module for simulating spiking neural networks.

#Features:

• Simulate single spiking neurons of various types and complexities (Izhikevich, AdEx, Hodgkin Huxley)
• Simulate a network of aforementioned neurons.
• Uses Matplotlib for visualization of results
• Lightweight : uses as little memory and as much vectorizing as possible to boost performance.

#Tutorial

##Getting Started - Single Neuron simulation:

###1. Create some populations. A Population is a group of neurons with the same properties.

from pyN import *
one_neurons = IzhikevichPopulation(name='Charly the Neuron', N=1)

###2. Create a Network and put the Population inside it. The populations parameter takes an array of Populations. You can also create an empty Network and add Populations manually, if you like.

brain = Network(populations=[one_neurons])

###[Optional]: Create some stimuli

In order for the neuron to do anything interesting, we need to present it with some externally "injected" current. This is defined by specifying a dictionary of the time interval for a certain voltage to be applied, and which neurons to apply it to. Voltages can stack on top of each other.

stim = [{'start':10,'stop':100,'mV':14,'neurons':[0]}]

###3. Run the Network

results = brain.simulate(experiment_name='Single Neuron exhibiting tonic spiking',T=100,dt=0.25,I_ext={'Charly the Neuron':stim}, save_data='../data/', properties_to_save=['v','u','psc','I_ext'])

###4. Look at data. Profit.

show_data(results)

##Recurrent Networks

• If you create a Population with more than one neuron in it, pyN by default recurrently connects the neurons.
• Note that in the absense of Hebbian Learning / Spike Adaptation, large networks will exhibit over-saturation of spiking behavior. If the entire network becomes active, it will become unrealistically "epileptic".

##Connecting Populations The brain is composed of many different types of neural populations with different properties.

###Construct two separate populations with different neural properties:

A = IzhikevichPopulation(name='thalamus', N=100, a=0.02, b=0.2, c=-65, d=6, v0=-70, u0=None)#tonic spiking
B = IzhikevichPopulation(name='cortex', N=50, a=0.02, b=0.25, c=-55, d=0.05, v0=-70, u0=None)#phasic bursting

###Add Populations to Network and then connect them

brain = Network(populations=[A,B])
brain.connect(pre='thalamus', post='cortex')
brain.connect(pre='cortex', post='thalamus',delay=2.25,std=0.25)#corticothalamic loop longer than thalamocortical loop

###Create stimuli and run the network.

stim = [{'start':10,'stop':200,'mV':20,'neurons':[0]},{'start':50,'stop':300,'mV':30,'neurons':[i for i in range(3)]}]
results = brain.simulate(experiment_name='Reentrant Thalamocortical Circuit',T=600,dt=0.25, integration_time=30, I_ext={'thalamus':stim}, save_data='../data/', properties_to_save=['v','u','psc','I_ext'])
show_data(results)

Note that if we wanted to stimulate the cortex as well, we merely pass in an additional attribute into the I_ext dictionary.

I_ext = {'thalamus':stim,'cortex':another_stim}

###Result:

##Spike-Timing-Dependent Plasticity

It has been proposed that STDP mechanisms act as a biologically plausible substrate to Hebbian Learning. STDP is active by default, but to disable it (for performance reasons, etc.), you can set the stdp=False flag when running the network:

brain.simulate(stdp=False)

Note that it takes a fairly large number of repeated spike pairings for STDP weights to take effect.

#Izhikevich Model Parameters:

Type	Example	a	b	c	d	v0	u0
Tonic Spiking		0.02	0.2	-65	6	-70
Phasic Spiking		0.02	0.25	-65	6	-64	u0
Tonic Bursting		0.02	0.25	-50	2	-70
Phasic Bursting		0.02	0.25	-55	0.05	-70
Mixed Mode		0.02	0.2	-55	6	-60
Spike Frequency Adaptation		0.01	0.2	-65	8	-70
Class 1 exc.		0.02	-0.1	-55	6	-60
Class 2 exc.	!	0.2	0.26	-65	0	-64
Spike Latency		0.02	0.2	-65	6	-70
Subthresh. osc.		0.05	0.26	-60	0	-62
Resonator		0.1	0.26	-60	-1	-62
Integrator		0.02	-0.1	-55	6	-60
Rebound Spike		0.03	0.25	-60	4	-64
Rebound Burst		0.03	0.25	-52	0	-64
Thresh. variability		0.03	0.25	-60	4	-64
Bistability		0.1	0.26	-60	0	-61
DAP		1	0.2	-60	-21	-70
Accomodation		0.02	1	-55	4	-65	-16
Inhibition induced spiking		0.02	-1	-60	8	-63.8
Inhibition induced bursting		-0.026	-1	-45	-	-63.8

#Adaptive Exponential Integrate-and-Fire Parameters:

#Performance:

• Although pyN is biologically realistic and matrix calculations are vectorized, it is still dreadfully slow due to numerical integration techniques used when convolving spike deltas with exponential decay currents.
• It takes ~10 minutes to simulate a single second of activity over 1000 Izhikevich neurons (recurrently connected to each other).

#Other Notes:

• Do NOT create a Population with the same name as another Population or it will be overwritten when adding it to a Network.

#License

pyN is licensed under BSD.