Home Department: Engineering and Mathematics
Mentor: Kwabena Boahen (Bioengineering and Electrical Engineering)

“Expanding the Silicon Brain: Creating a Super-linear Memory Capacity Network through the Utilization of Spike Sequences in Neuronal Networks”

This project aims to improve current computing through a novel approach inspired by neurobiology. Comparisons of the dominant (von Neumann) computing architecture to the human brain reveal the significant differences in the structure, power consumption, and processing capabilities between the two. This led to the quest for neurobiologically-inspired computing architecture: neuromorphic computing. Neuromorphic computing refers to a variety of brain-inspired devices and models that have highly connected synthetic neurons and synapses that can be used to model the activities of neuronal networks. This project aims to improve current neuromorphic computing through a novel approach inspired by neurobiology: utilizing spike sequences (i.e., discrete rather than continuous data) in neuronal networks. The project will first mathematically model the mechanisms of sequence-detecting neuronal networks. Then, the project will use simulation software (e.g., NEST) to generate activities of various sequences of spikes and compute the likelihood of temporally- coincident spike activity. The end goal of this project is to create a super-linear memory capacity network by drawing inspiration from neurobiology.