Recurrent spiking neural networks as models of the entorhinal–hippocampal system for path integration: Grid cells and beyond

Abstract

Grid cells in the mammalian medial entorhinal cortex (MEC) play a pivotal role in coding spatial information and integrating self-motion, functioning as a spatial metric through multiscale periodic representations. To investigate the properties and functions of these grid codes, mechanistic models employ continuous attractor neural networks (CANNs) with hand-tuned connectivity and dynamics, while normative models use recurrent neural networks (RNNs) for path integration where hexagonal grid patterns emerge spontaneously. In this work, we develop recurrent spiking neural networks (RSNNs) with biologically realistic structures for the path integration task, generating more biologically plausible representations resembling those in the entorhinal–hippocampal system. Leveraging various spiking neuron models including a novel adaptive neuron model, the RSNNs achieve accurate and generalizable path integration performance comparable to prior normative models. Besides, the RSNNs exhibit the inherent formation of multimodular hexagonal grid patterns with more biologically plausible grid scale ratios, as well as toroidal topology and low-dimensional neural dynamics consistent with biological observations. Through experiments and analyses, the path-integrating RSNNs offer new insights into the fundamental mechanisms underlying mammals’ exceptional navigation abilities, paving the way for future research in biologically inspired navigation systems.