Competition on Presynaptic Resources Enhances the Discrimination of Interfering Memories

Evidence suggests that hippocampal adult neurogenesis is critical for discriminating considerably interfering memories. During adult neurogenesis, synaptic competition modifies the weights of synaptic connections non-locally across neurons, thus providing a different form of unsupervised learning from Hebb’s local plasticity rule. However, how synaptic competition achieves separating similar memories largely remains unknown. Here, we aim to link synaptic competition with such pattern separation. In synaptic competition, adult-born neurons are integrated into the existing neuronal pool by competing with mature neurons for synaptic connections from the entorhinal cortex. We show that synaptic competition and neuronal maturation play distinct roles in separating interfering memory patterns. Furthermore, we demonstrate that a feed-forward neural network trained by a competition-based learning rule can outperform a multi-layer perceptron trained by the backpropagation algorithm when only a small number of samples are available. Our results unveil the functional implications and potential applications of synaptic competition in neural computation. © The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.