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

arXiv: https://arxiv.org/abs/1805.01631 · HTML: https://mlsanity.com/paper/1805.01631/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2018.

## Built on by (most cited first, 4 of 4)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training](https://mlsanity.com/paper/2303.13635/) (2023)
- [Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models](https://mlsanity.com/paper/2207.01916/) (2022)
- [Neural Network Compression for Reinforcement Learning Tasks](https://mlsanity.com/paper/2405.07748/) (2024)

## Abstract (arXiv)

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.