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Neural variability, or lack thereof.

Timothée Masquelier1

  • 1Unit for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain ; Laboratory of Neurobiology of Adaptive Processes, UMR 7102, CNRS - University Pierre and Marie Curie Paris, France.

Frontiers in Computational Neuroscience
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PubMed
Summary
This summary is machine-generated.

Neuronal variability in the brain might be overestimated due to uncontrolled factors. Analyzing relative latencies suggests brain signal-to-noise ratios are higher than previously thought, indicating efficient neural processing.

Keywords:
neural codingneural variabilityredundancyreliabilitysignal-to-noise ratio

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Area of Science:

  • Neuroscience
  • Computational Neuroscience

Background:

  • Neuronal variability is a key factor in brain function, but its sources and extent are debated.
  • Previous studies often attribute variability to intrinsic noise, potentially overestimating its contribution.

Purpose of the Study:

  • To re-examine the extent of neuronal variability in the brain.
  • To investigate the impact of uncontrolled internal variables and reference timing on variability measurements.
  • To propose that signal-to-noise ratios in the brain may be higher than commonly assumed.

Main Methods:

  • Analysis of recent experimental findings on trial-to-trial variability.
  • Simultaneous recording of multiple neurons to analyze relative latencies.
  • Investigating correlations in variability across neurons and trials.

Main Results:

  • Trial-to-trial neuronal variability is often correlated across neurons and trials.
  • Variability increases for higher-order neurons and decreases with attention.
  • Relative latencies are insensitive to shared variability, offering a more accurate measure.
  • Intrinsic noise likely accounts for only a small portion of observed variability.

Conclusions:

  • Neuronal variability may be overestimated due to methodological limitations.
  • Signal-to-noise ratios in the brain are potentially much higher, suggesting efficient neural processing.
  • High signal-to-noise ratios support the existence of reactive, neuron- and energy-efficient neural systems.