Abstract
In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters – excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli.
Highlights
A simulation of neural network consists of model neurons that interact via network parameters; The model is a set of differential equations that describes the behaviors of neurons and synapses, captured by years of electro physiological studies (Nicholls et al, 2012)
Simple models consume less computer resources, at the cost of loosing details and biological plausibility; there is a delicate balance between computational feasibility and biological plausibility, there are many reviews and books that compare different models (Izhikevich, 2004; Herz et al, 2006; Sterratt et al, 2011) and neural simulators (Brette et al, 2007)
They help us to find a suitable model of a single neuron, set its parameters, and get reliable results in a reasonable time
Summary
A simulation of neural network consists of model neurons that interact via network parameters; The model is a set of differential equations that describes the behaviors of neurons and synapses, captured by years of electro physiological studies (Nicholls et al, 2012). Simple models consume less computer resources, at the cost of loosing details and biological plausibility; there is a delicate balance between computational feasibility and biological plausibility, there are many reviews and books that compare different models (Izhikevich, 2004; Herz et al, 2006; Sterratt et al, 2011) and neural simulators (Brette et al, 2007) They help us to find a suitable model of a single neuron, set its parameters, and get reliable results in a reasonable time.
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