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Neuronal variability and tuning are balanced to optimize naturalistic self-motion coding in primate vestibular pathways.

Diana E Mitchell | Annie Kwan | Jerome Carriot | Maurice J Chacron | Kathleen E Cullen
eLife | 2018

It is commonly assumed that the brain's neural coding strategies are adapted to the statistics of natural stimuli. Specifically, to maximize information transmission, a sensory neuron's tuning function should effectively oppose the decaying stimulus spectral power, such that the neural response is temporally decorrelated (i.e. 'whitened'). However, theory predicts that the structure of neuronal variability also plays an essential role in determining how coding is optimized. Here, we provide experimental evidence supporting this view by recording from neurons in early vestibular pathways during naturalistic self-motion. We found that central vestibular neurons displayed temporally whitened responses that could not be explained by their tuning alone. Rather, computational modeling and analysis revealed that neuronal variability and tuning were matched to effectively complement natural stimulus statistics, thereby achieving temporal decorrelation and optimizing information transmission. Taken together, our findings reveal a novel strategy by which neural variability contributes to optimized processing of naturalistic stimuli.

Pubmed ID: 30561328

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Associated grants

  • Agency: NIDCD NIH HHS, United States
    Id: R01 DC002390
  • Agency: NIH HHS, United States
    Id: DC2390
  • Agency: CIHR, Canada

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RRID:SCR_001622

Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.

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