MS Defense: Shamit Patel on a Working Theory of the Learning Rule for Dendritic Integration

MS Thesis Defense

Towards Implementation of a Pattern Recognition System based on
a Working Theory of the Learning Rule for Dendritic Integration

Shamit Patel

4:00pm Monday 23 April 2012, ITE 346, UMBC

My goal is to develop a working theory of the learning rule for dendritic integration, and to then implement a pattern recognition system based on that learning algorithm so that the algorithm can be evaluated for its generalization ability. In this regard, this thesis presents an implementation of Jeff Hawkins and Dileep George's Hierarchical Temporal Memory (HTM) pattern recognition system that's based on an existing theory of the learning rule for dendritic integration – spike-timing-dependent synaptic plasticity (STDP). The integration of this learning rule is the novel contribution of this thesis. I found that the STDP HTM system achieved much higher probabilistic classification accuracy and better generalization ability than the non-STDP HTM system. Probabilistic classification accuracy is a way of measuring classification accuracy in which a testing pattern is classified correctly if its label appears in the group of labels output by the top-level node of the HTM network.

Committee: Professors Tim Oates (Chair), Yun Peng and Tim Finin

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