Sensory feedback in a bump attractor model of path integration

TL;DR

This paper presents a computational model integrating sensory cues with path integration in a bump attractor framework, analyzing how external control signals can correct errors caused by heterogeneity and noise in spatial navigation.

Contribution

It introduces a novel scalar reduction of a bump attractor model incorporating heterogeneity and noise, and identifies optimal sensory control parameters for error correction.

Findings

Optimal control strength and decay rate depend on cue placement and noise characteristics.

Heterogeneity causes error accumulation in path integration, which can be mitigated by external cues.

The model provides a quantitative framework for sensory correction in spatial navigation.

Abstract

The mammalian spatial navigation system makes use of several different sensory information channels. This information is then converted into a neural code that represents the animal's current position in space by engaging place cell, grid cell, and head direction cell networks. In particular, sensory landmark (allothetic) cues can be utilized in concert with an animal's knowledge of its own velocity (idiothetic) cues to generate a more accurate representation of position than (idiothetic) path integration provides on its own (Battaglia et al, 2004). We develop a computational model that merges path integration with information from external sensory cues that provide a reliable representation of spatial position along an annular track. Starting with a continuous bump attractor model, we allow for the possibility of synaptic spatial heterogeneity that would break the translation symmetry of space. We use asymptotic analysis to reduce the bump attractor model to a single scalar equation whose potential represents the impact of heterogeneity. Such heterogeneity causes errors to build up when the network performs path integration, but these errors can be corrected by an external control signal representing the effects of sensory cues. We demonstrate that there is an optimal strength and decay rate of the control signal when cues are placed both periodically and randomly. A similar analysis is performed when errors in path integration arise from dynamic noise fluctuations. Again, there is an optimal strength and decay of discrete control that minimizes the path integration error.