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 .
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 .
D
physiological
R
alternative possibility of a discrete representation
discrete alternative
R
representations, and answers
, answering
A
systems
R
Drawing on the well-established
Using
D
a
R
Using a computer simulation, we show
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
R
reliably between neurons
and represented reliably in the brain
R
, due to the presence of noise; neural information
- it
R
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
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