A signature of neural coding at human perceptual limits.

Paul M Bays

doi:10.1167/16.11.4

Abstract

Simple visual features, such as orientation, are thought to be represented in the spiking of visual neurons using population codes. I show that optimal decoding of such activity predicts characteristic deviations from the normal distribution of errors at low gains. Examining human perception of orientation stimuli, I show that these predicted deviations are present at near-threshold levels of contrast. The findings may provide a neural-level explanation for the appearance of a threshold in perceptual awareness whereby stimuli are categorized as seen or unseen. As well as varying in error magnitude, perceptual judgments differ in certainty about what was observed. I demonstrate that variations in the total spiking activity of a neural population can account for the empirical relationship between subjective confidence and precision. These results establish population coding and decoding as the neural basis of perception and perceptual confidence.

Highlights

Population coding describes a method by which information can be encoded in, and recovered from, the combined activity of a pool of neurons (Georgopoulos et al, 1982; Pouget, Dayan, & Zemel, 2000; Salinas & Abbott, 1994; Seung & Sompolinsky, 1993; Vogels, 1990)
Estimation of orientation was modeled as maximum a posteriori (MAP) decoding over a fixed temporal window
The present results demonstrate a signature of population coding in the errors made by human observers in perception of near-threshold stimuli

Summary

Introduction

Population coding describes a method by which information can be encoded in, and recovered from, the combined activity of a pool of neurons (Georgopoulos et al, 1982; Pouget, Dayan, & Zemel, 2000; Salinas & Abbott, 1994; Seung & Sompolinsky, 1993; Vogels, 1990). Each neuron’s mean firing rate is described by an approximately bell-shaped tuning curve, with a maximum at the cell’s ’preferred’ orientation. This orientation varies from neuron to neuron, and the population as a whole encodes information about every possible orientation. I considered a variant of the population coding model in which all neurons have background (baseline) activity, η. In this case, the response of the ith neuron is given by: ca ri(θ, c) = fi(θ) σa + ca + η, (22). The model of detection is the same as above, except that the no-stimulus epoch contained spikes generated at the baseline rate η, while activity in the stimulus epoch was given by Eq (22). a b MAP c d

Results

Discussion

Conclusion