Brain Modeling of Inhibition / Excitation Firing through Spiking Neurons

We propose to develop several learning mechanisms which obey the following philosophy: each connection to be

modified by some learning rule is changed only due to locally available information which is provided by the timing of

spikes produced by the two corresponding neurons. This strategy can be seen as a formulation of the Hebbian rule in the

temporal domain and is motivated by recent neurobiological findings.

We will explore various ways for computing and learning with networks of leaky integrate-and-fire type using

temporal coding. We will use both supervised and unsupervised learning rules, which are Hebbian in the sense that the

strength of a synapse is modified if a pre- and a postsynaptic spike arrive at the synapse within a certain learning window.

Recent neurobiological findings have confirmed such a dependency and have shown that the sign and strength of the change

depends on the timing of the two spikes.

In the relatively unexplored area of neurotransmitter research (from a computing perspective), we will design an

architecture and experimentally analyze simulation results referring to available biological research and established