Biologically Plausible Synaptic Plasticity Model for Rapid Neuronal Tuning

Abstract

Okinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyIn the intricate workings of the brain, neurons play a fundamental role in detecting meaningful patterns amidst a constant stream of information. However, the rapidity with which neurons accomplish this task often goes unnoticed in computational models, leading to a gap in understanding crucial mechanistic features observed in biological neurons. To bridge this gap, I introduce a class of neural models equipped with a biologically-inspired synaptic plasticity rule. The aim of this thesis research is to shed light on the brain’s ability to rapidly learn and discern statistically salient patterns. My approach leverages the dynamic interplay between neural activity and synaptic plasticity, where somatic spikes propagate back to dendrites, facilitating self-supervised detection and learning of presynaptic neuron communities impinging on dendrites. I showcase the efficacy of these models in various tasks, including pattern recognition and spatial navigation, where they establish swift associations between behavior and environmental cues. Moreover, in exploring multi-compartmental neural architectures, I extend the synaptic plasticity rule to elucidate the initiation and development of local dendritic spikes, offering insights into neural processing mechanisms. My modeling work underscores the importance of pre-existing neural assemblies in robust pattern learning within recurrent networks. By illuminating the self-supervision function of backpropagating action potentials and the role of pre-existing neural assemblies, my findings contribute to a deeper comprehension of brain cognitive function and its implications for artificial intelligence and neuroscience.doctoral thesi