Implementing and investigating a biologically realistic neuron model
Name
Taivo Pungas
Abstract
Calcium-based single neuron models have been shown to elicit different modes of synaptic plasticity. In the present study one such model was implemented and its learning behaviour studied.
Behaviour of the implemented neuron agreed qualitatively with prior work in all regards except selectivity to correlation in input. The neuron was found to implement a linear filter responding linearly to partial presentations of learned patterns. Simulating probabilistic neurotransmitter release had an expected effect of de-correlating input and was found to improve the efficiency of information transfer. In the regimes explored, the neuron was found to be incapable of performing principal component analysis. The insensitivity of results to changes in parameters was mostly untested.
The neuron did not exhibit more advanced information processing capabilities in the tests conducted. However, the implemented neuron model is capable of meaningful information processing and forms a good basis for further research.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Raul Vicente, Jaan Aru
Defence year
2015