Neural decision boundaries for maximal information transmission

Tatyana Sharpee, William Bialek

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

We consider here how to separate multidimensional signals into two categories, such that the binary decision transmits the maximum possible information about those signals. Our motivation comes from the nervous system, wbere neurons process multidimensional signals into a binary sequence of responses (spikes). In a small noise limit, we derive a general equation for the decision boundary that locally relates its curvature to the probability distribution of inputs. We show that for Gaussian inputs the optimal boundaries are planar, but for non-Gaussian inputs the curvature is nonzero. As an example, we consider exponentially distributed inputs, which are known to approximate a variety of signals from natural environment.

Original languageEnglish (US)
Article numbere646
JournalPloS one
Volume2
Issue number7
DOIs
StatePublished - Jul 25 2007

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of 'Neural decision boundaries for maximal information transmission'. Together they form a unique fingerprint.

  • Cite this