Is Information in the Brain Represented in Continuous or Discrete Form?

TL;DR

This paper argues that brain information is represented discretely rather than continuously, using communication theory to analyze neural coding and challenging conventional assumptions.

Contribution

It introduces a communication systems perspective to demonstrate that neural information representation must be discrete, providing a new theoretical foundation for neural coding.

Findings

Discrete neural coding is necessary for reliable information transmission in the brain.

Continuous representation cannot achieve reliable communication according to Shannon's theory.

Application of discrete coding to electrophysiology data supports the hypothesis.

Abstract

The question of continuous-versus-discrete information representation in the brain is a fundamental yet unresolved question. Historically, most analyses assume a continuous representation without considering the discrete alternative. Our work explores the plausibility of both, answering the question from a communications systems engineering perspective. Using Shannon's communications theory, we posit that information in the brain is represented in discrete form. We address this hypothesis using 2 approaches. First, we identify the fundamental communication requirements of the brain. Second, we estimate the symbol error probability and channel capacity for a continuous information representation. Our work concludes that information cannot be communicated and represented reliably in the brain using a continuous representation - it has to be in a discrete form. This is a major demarcation from conventional and current wisdom. We apply this discrete result to the 4 major neural coding hypotheses, and illustrate the use of discrete ISI neural coding in analyzing electrophysiology experimental data. We further posit and illustrate a plausible direct link between Weber's Law and discrete neural coding. We end by outlining a number of key research questions on discrete neural coding.