Continuous Attractor Networks for Laplace Neural Manifolds

TL;DR

This paper introduces a neural circuit using continuous attractor dynamics to represent Laplace transforms of temporal functions, enabling robust modeling of temporal predictions in cognitive systems.

Contribution

It presents a novel neural circuit model that encodes Laplace transforms and their inverses for temporal prediction, with robustness to noise and applications in cognitive modeling.

Findings

The circuit can estimate Laplace transforms of delta functions in time.

It models learned temporal associations with fixed delays.

The network maintains exponential change in states over time.

Abstract

Many cognitive models, including those for predicting the time of future events, can be mapped onto a particular form of neural representation in which activity across a population of neurons is restricted to manifolds that specify the Laplace transform of functions of continuous variables. These populations coding Laplace transform are associated with another population that inverts the transform, approximating the original function. This paper presents a neural circuit that uses continuous attractor dynamics to represent the Laplace transform of a delta function evolving in time. One population places an edge at any location along a 1-D array of neurons; another population places a bump at a location corresponding to the edge. Together these two populations can estimate a Laplace transform of delta functions in time along with an approximate inverse transform. Building the circuit so the edge moves at an appropriate speed enables the network to represent events as a function of log time. Choosing the connections appropriately within the edge network make the network states map onto Laplace transform with exponential change as a function of time. In this paper we model a learned temporal association in which one stimulus predicts another at some fixed delay $T$. Shortly after $t=0$ the first stimulus recedes into the past. The Laplace Neural Manifold representing the past maintains the Laplace transform $\exp(-st)$. Another Laplace Neural Manifold represents the predicted future. At $t=0$, the second stimulus is represented a time $T$ in the future. At each moment between 0 and $T$, firing over the Laplace transform predicting the future changes as $\exp[-s(T-t)]$. Despite exponential growth in firing, the circuit is robust to noise, making it a practical means to implement Laplace Neural Manifolds in populations of neurons for a variety of cognitive models.