Team 7 Project in Computational Neuroscience: Computational Modeling of the Memory Recall Capacity of Neural Networks and the Effects of Random Neural Failure
Robert Aguilar, Harold Chen, Rajani Deshpande, Nicole Katchur, Minsoo Khang, Kenneth Li, Aleta Murphy, Sudheesha Perera, Shreyas Shirodkar, Niklas Sjöquist, Amulya Yalamanchili, Susanna Yu
Advisor: Minjoon Kouh
Assistant: Aaron Loether
ABSTRACT
A number of neurodegenerative diseases, such as Alzheimer’s Disease, are known to involve the decay or death of neurons and synapses, resulting in loss of memory. We built a computational model of auto-associative memory (Hopfield network) and explored the capacity of the artificial neural network to store and recall patterns. We investigated the effects of random synaptic failures on the memory capacity of the computational model. Our results show that the number of neurons in the simulated network was directly proportional to the number of patterns the model was able to recall. Furthermore, our research findings reveal that the interconnectedness of a neural network is as, if not more, critical to memory recall as the sheer size of the network in number of neurons.