|PROJECTS AT PSPL FINISHED IN 2007|
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
Now we can compare different networks using these parameters and characteristics.