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

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