Encoding certainty in bump attractors

TL;DR

This paper extends bump attractor models to encode stimulus certainty through amplitude, creating a two-dimensional attractor that includes position and amplitude, which enhances robustness to noise and improves memory representation.

Contribution

It introduces a novel bump attractor model that encodes stimulus certainty via amplitude, requiring precise synaptic balance and expanding the stability of persistent activity.

Findings

Bumps encode both position and certainty in a 2D attractor.

Larger bump amplitudes are more noise-robust.

Precise inhibition tuning is necessary for amplitude encoding.

Abstract

Persistent activity in neuronal populations has been shown to represent the spatial position of remembered stimuli. Networks that support bump attractors are often used to model such persistent activity. Such models usually exhibit translational symmetry. Thus activity bumps are neutrally stable, and perturbations in position do not decay away. We extend previous work on bump attractors by constructing model networks capable of encoding the certainty or salience of a stimulus stored in memory. Such networks support bumps that are not only neutrally stable to perturbations in position, but also perturbations in amplitude. Possible bump solutions then lie on a two-dimensional attractor, determined by a continuum of positions and amplitudes. Such an attractor requires precisely balancing the strength of recurrent synaptic connections. The amplitude of activity bumps represents certainty, and is determined by the initial input to the system. Moreover, bumps with larger amplitudes are more robust to noise, and over time provide a more faithful representation of the stored stimulus. In networks with separate excitatory and inhibitory populations, generating bumps with a continuum of possible amplitudes, requires tuning the strength of inhibition to precisely cancel background excitation.