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The Nature of Shared Cortical Variability.

I-Chun Lin1, Michael Okun1, Matteo Carandini2

  • 1UCL Institute of Neurology, University College London, London WC1N 3BG, UK; UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK; UCL Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6DE, UK.

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Neuronal population activity in the visual cortex shows correlated variability. A model reveals two main factors, multiplicative gain and additive offset, explain this shared variability and impact population coding.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neuronal responses in the sensory cortex exhibit significant variability.
  • This variability is often correlated across populations of neurons.
  • Understanding these correlations is key to deciphering neural population coding.

Purpose of the Study:

  • To investigate the sources of shared variability among simultaneously recorded neurons in the visual cortex.
  • To develop and test a model explaining how population variability reflects shared factors.
  • To assess the impact of these shared variability factors on population coding.

Main Methods:

  • Simultaneous recording of neuronal activity from multiple neurons in the visual cortex.
  • Development of a computational model incorporating multiplicative gain and additive offset.
  • Analysis of spike count variability and pairwise correlations.
  • Evaluation of the model's ability to reproduce observed neuronal activity patterns.

Main Results:

  • A simple model with multiplicative gain and additive offset effectively captured neuronal spike count variability.
  • The model reproduced the stimulus- and tuning-dependent pairwise correlations between neurons.
  • The relative contributions of additive and multiplicative factors varied dynamically.
  • These factors significantly influenced the fidelity of population coding.

Conclusions:

  • Shared variability in sensory cortical neuronal populations can be largely attributed to multiplicative and additive gain modulation.
  • These two factors provide a parsimonious explanation for correlated neuronal variability.
  • Understanding these shared variability mechanisms is crucial for comprehending neural information processing.