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  Robust Decision-Making: Non-Linear Responsiveness can Enhance Stimulus Information

Braun, J., Levina, A., & Giugliano, M. (2022). Robust Decision-Making: Non-Linear Responsiveness can Enhance Stimulus Information. Poster presented at FENS Forum 2022, Paris, France.

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Braun, J, Author
Levina, A1, Author           
Giugliano, M, Author
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1External Organizations, ou_persistent22              

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 Abstract: Sensory decision-making typically involves a choice between multiple alternatives and the evidence supporting each alternative may comprise some number of independent signals (e.g., from different receptive fields). To make an optimal choice, all signals supporting each alternative must be integrated before the results can be compared. In the brain, sensory signals are integrated and represented by downstream neural responses that perform a non-linear transformation of sensory input. Here we report that choice performance can be more accurate when it relies on integrated (averaged) neural responses rather than on integrated (averaged) noisy sensory signals, at odds with the common-sense expectation that any neural processing should degrade sensory information. The improvement is obtained when sensory signals s are drawn from heavy-tailed (non-Gaussian) distributions P(s) and when non-linear neural response functions Ψ(s) broadly match δs ln P(s). As proof-of-principle, we devised a decision-making toy-model in which a separate population of spiking neurons represented evidence for each alternative. Specifically, we simulated responses of integrate-and-fire neurons to uncorrelated synaptic inputs, with variance encoding an independent 'sensory signal'. In this toy framework, integrating neural responses rather than sensory signals more than doubled choice performance, depending on assumptions. The intuitive reason for this improvement is that compressive (saturating) responsiveness reduces the influence of outlier signals. Although not widely known in neuroscience, this effect is exploited routinely in engineering and statistics by preprocessing samples with nonlinear "influence functions" [e.g., Huber, Ronchetti (2009) Robust Statistics, John Wiley & Sons]. An intriguing question for further work is whether neural response functions in vivo match or adapt to heavy-tailed input distributions.

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 Dates: 2022-07
 Publication Status: Published online
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Title: FENS Forum 2022
Place of Event: Paris, France
Start-/End Date: 2022-07-09 - 2022-07-13

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Title: FENS Forum 2022
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: S01-159 Start / End Page: 497 Identifier: -