Computational Hebbian Synapses and Self-Organizing Neural Maps

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This website presents the work from my final project in the Computational Section of BioNB 2220, Spring 2009.


This project investigates the efficacy of two variations of a Hebbian learning algorithm. Current research shows that the strengthening of synapses of pre-synaptic neurons on post-synaptic neurons is a function of depolarization of the post-synaptic cell membrane. Thus, coincidence detection and subsequent associativity and specificity requires no spiking activity of the post-synaptic neuron. In this project, I created a neural model that allows self-organization of neurons using a Hebbian learning algorithm. The extent of self-organization was quantified under two different Hebbian learning algorithms: one that required no spiking of the post-synaptic neurons (using a so-called activity independent output variable), and one that required spiking (using a so-called activity dependent output variable). With repeated simulations, it is shown that the extent of self-organization is significantly greater when the learning model does not require post-synaptic cell spiking to induce learning.

Files for my project can be downloaded below:

Matt Conlon
BioNB2220 Computational Section
Final Project
Presented April 28, 2009