Title: Is Information in the Brain Represented in Continuous or Discrete Form?,
Abstract: The question of continuous-versus-discrete information representation in the
brain is a fundamental yet unresolved physiological question. Historically,
most analyses assume a continuous representation without considering the
alternative possibility of a discrete representation. Our work explores the
plausibility of both representations, and answers the question from a
communications engineering perspective. Drawing on the well-established
Shannon's communications theory, we posit that information in the brain is
represented in a discrete form. Using a computer simulation, we show that
information cannot be communicated reliably between neurons using a continuous
representation, due to the presence of noise; neural information has to be in a
discrete form. In addition, we designed 3 (human) behavioral experiments on
probability estimation and analyzed the data using a novel discrete (quantized)
model of probability. Under a discrete model of probability, two distinct
probabilities (say, 0.57 and 0.58) are treated indifferently. We found that
data from all participants were better fit to discrete models than continuous
ones. Furthermore, we re-analyzed the data from a published (human) behavioral
study on intertemporal choice using a novel discrete (quantized) model of
intertemporal choice. Under such a model, two distinct time delays (say, 16
days and 17 days) are treated indifferently. We found corroborating results,
showing that data from all participants were better fit to discrete models than
continuous ones. In summary, all results reported here support our discrete
hypothesis of information representation in the brain, which signifies a major
demarcation from the current understanding of the brain's physiology.