Advisor(s)

Joseph Ayers

Abstract

Unit central pattern generators (CPGs) were created using two different types of modeled neurons. Three basic central pattern-generating networks were created for each of two separate types of modeled neurons. First, UCSD analog electronic neurons (ENs) based on the three Hindmarsh-Rose differential equations, plus a fourth differential equation developed at UCSD, were used to build an endogenous pacemaker network, a reciprocal half center network, and a recurrent cyclic inhibitory network. These electronic neurons are a biologically realistic phenomenological model, but are computationally intensive and difficult to prototype with. The three CPG types were then also created using discrete-time map-based neurons (MBNs), which are computationally less intensive, and also less biologically accurate. Finally, networks were created using both artificial neuron types, showing the possibility for creating hybrid circuits for the control of robotics or neural prosthetics that can benefit from the biological realism of the ENs and the computational efficiency of the MBNS.

Date Accepted

5-1-2006

Publication Date

5-3-2006

Keywords

Behavioral Neuroscience, Neuroscience

Degree Grantor

Northeastern University

Rights Holder

Daniel Knudsen

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