The Impact of Selective Plasticity Modulationon Simulated Long Term Memory

Abstract: Understanding the brain and its functions is achallenging undertaking. To facilitate this work, brain-inspiredtechnology may be used to examine cognitive phenomena to acertain extent, by replacing real biological brains with simulations.The aim of this project was to provide insights intohow different kinds of plasticity modulation affected long-termmemory recall through the use of a computational model. Aneural network was constructed based on the existing BayesianConfidence Propagation Neural Network (BCPNN) model andtrained with binary patterns representing memories acquiredover a lifetime. By varying network plasticity parameters forselected patterns and performing recall of “aging” memories,greater effects were observed in recall statistics for modulationearly in the lifetime in comparison with modulation of later ages.From the experiments conducted in this study it was possible toconclude that selective modulation of learning affected the longtermrecall of all memories in the simulation.