Inhibitory Neurons` Diversity in Biological Neural Networks

The control and activation system in any biological system is consisted from many neural cells (also named neurons).


The control and activation system in any biological system is consisted from many neural cells (also named neurons). These cells are connected together as a complex network that conducts electrical currents in the form of charged Ions. We can divide the neurons in the nervous system into two main groups: Excitatory and Inhibitory neurons. Each of this groups can be divided into different types of Excitatory and Inhibitory cells characterized by (among other things) different electrical behavior. The Excitatory neurons make 70-80 % of each neural network while the Inhibitory make the 20-30% remaining. According to the synaptic weights model, stimulating an Excitatory neuron encourages activation of other cells connected to it, while a stimulation of an Inhibitory neuron suppresses neural activity.



A lot of neural network models neglect the difference between Inhibitory and Excitatory neurons and the diversity of neuron types within each group.
In this project we will create a neural network using a computer model introduced by Eugene M. Izhikevitch.
The purpose of the project is to characterize the neural network and the influence of the different types of neurons on those characteristics.

Understanding the behavior of neural networks has many benefits. Among them:

  • Guessing the structure and components of a neural systems in a biological system being researched
  • Developing artificial neural networks for different uses
  • Understanding how tissues and organs operate: such as the brain
  • Developing neural network based computers capable of complex computations


Basic Approach




  • The Model presented by Izhikevitch provides a good simulation environment for single nerve cells as well as for large neural networks
  • We have authenticated that Inhibitory and Excitatory neurons indeed suppress and encourage, accordingly, neural activity
  • We have defined parameters by which one can analyze and characterize a neural network by its` electrical response
  • We have characterized neural networks by several characteristics: Threshold phenomena and the shape of dependence between the defined parameters
  • We have characterized the neural network in the phase plane determined by the average activity of excitatory and inhibitory neurons

Now we can compare different networks using these parameters and characteristics

  • In large scale networks there is little dependence of the network behavior on the type cells from which it is built
  • The excitability of the network elements changes the phase plane