Multi-Scale Codes in the Nervous System: The Problem of Noise Correlations and the Ambiguity of Periodic Scales

TL;DR

This paper demonstrates that multi-scale grid codes in the nervous system can achieve exponential precision in representing position even with noise and correlations, and analyzes how neural dependencies affect this coding.

Contribution

It shows that neuronal grid codes can be read out with exponential precision on short timescales and examines the impact of noise correlations on coding accuracy.

Findings

Neuronal grid codes scale exponentially in precision with neuron number N.

Noise correlations can reach 0.8 when grid parameters align.

Correlations reduce resolution but do not affect exponential scaling.

Abstract

Encoding information about continuous variables using noisy computational units is a challenge; nonetheless, asymptotic theory shows that combining multiple periodic scales for coding can be highly precise despite the corrupting influence of noise (Mathis et al., Phys. Rev. Lett. 2012). Indeed, cortex seems to use such stochastic multi-scale periodic `grid codes' to represent position accurately. We show here how these codes can be read out without taking the asymptotic limit; even on short time scales, the precision of neuronal grid codes scales exponentially in the number N of neurons. Does this finding also hold for neurons that are not statistically independent? To assess the extent to which biological grid codes are subject to statistical dependencies, we analyze the noise correlations between pairs of grid code neurons in behaving rodents. We find that if the grids of the two neurons align and have the same length scale, the noise correlations between the neurons can reach 0.8. For increasing mismatches between the grids of the two neurons, the noise correlations fall rapidly. Incorporating such correlations into a population coding model reveals that the correlations lessen the resolution, but the exponential scaling of resolution with N is unaffected.