Intermediate intrinsic diversity enhances neural population coding

Shreejoy J. Tripathy, Krishnan Padmanabhan, Richard C. Gerkin, Nathaniel N. Urban

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.

Original languageEnglish (US)
Pages (from-to)8248-8253
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume110
Issue number20
DOIs
StatePublished - May 14 2013

Keywords

  • Generalized linear models
  • Intrinsic biophysics
  • Ion channels
  • Neural variability
  • Stimulus coding

ASJC Scopus subject areas

  • General

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