Matthew Rogge, ’17

Project: Analysis of Spike Timing Dependent Neural Networks for More Efficient Starting State Learning
Duration: Summer 2014
Funding: Bucknell University Program for Undergraduate Research

ABSTRACT

Artifical Neural Networks (ANN) have been a popular machine learning method for decades. They aim to simulate the behavior of the neurons in the biological brain. One particular type of ANN that is an especially accurate representation of biological neurons is the Spike-Timing Dependent ANN. These ANNs differ from traditional back propagation ANNs in that they rely on the timing and frequency of signals, rather than their strength, to learn and process information. This type of ANN of often ignored for many reasons, mostly due to the computational complexity of learning using these models. On substantial challenge lies in the difficulty of determining the initial configuration of the network. The time required to train the network is also a formidable challenge. My research seeks to eliminate one of these hurdles by deriving an efficient algorithm that can determine the proper starting configuration for the ANN.

ACHIEVEMENTS

• Poster: 2014 Sigma Xi Summer Student Research Symposium, July 24, Bucknell University, Lewisburg, PA

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