Learning predictive signals within a local recurrent circuit

Abstract

The predictive coding hypothesis proposes that top-down predictions are compared with incoming bottom-up sensory information, with prediction errors signaling the discrepancies between these inputs. While this hypothesis explains the presence of prediction errors, recent experimental studies suggest that prediction error signals can emerge within a local circuit, that is, from bottom-up sensory input alone. In this paper, we test whether local circuits alone can generate predictive signals by training a recurrent spiking network using local plasticity rules. Our network model replicates prediction errors resembling various experimental results, such as a biphasic pattern of prediction errors and context-specific representation of error signals. Our findings shed light on how synaptic plasticity can shape prediction errors and enables the acquisition and updating of an internal model of sensory input within a recurrent neural network.