Learning with a network of Competing Synapses
Authors: Bhat A. A., Mahajan G., & Mehta A.
Journal: PLoS ONE
Tags: plasticity, synapses, learning, neural, memory
Link: URL
Abstract:
Competition between synapses arises in correlation-based plasticity. We propose a game-theory-inspired model in which synapses switch between weak and strong states, driven by local competition and memory of past outcomes. The framework reproduces power-law forgetting and captures motor adaptation phenomena like savings and rebound via distinct timescales.
Methodology:
We model each synapse as a binary unit updated by parallel/sequential rules. Synaptic transition probabilities depend on neighbors' states and a memory parameter. We derive mean-field equations, identify phases via simulations, and compare system-level learning and forgetting curves to existing motor adaptation data.