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Brittleness in model selection analysis of single neuron firing rates

View ORCID ProfileChandramouli Chandrasekaran, Joana Soldado-Magraner, View ORCID ProfileDiogo Peixoto, William T. Newsome, View ORCID ProfileKrishna V. Shenoy, Maneesh Sahani
doi: https://doi.org/10.1101/430710
Chandramouli Chandrasekaran
1Department of Electrical Engineering, Stanford University, CA, USA
5Howard Hughes Medical Institute, Stanford University, CA, USA
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  • ORCID record for Chandramouli Chandrasekaran
  • For correspondence: mouli@stanford.edu shenoy@stanford.edu maneesh@gatsby.ucl.ac.uk
Joana Soldado-Magraner
2Gatsby Computational Neuroscience Unit, University College London, UK
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Diogo Peixoto
3Department of Neurobiology, Stanford University, CA, USA
4Champalimaud Neuroscience Institute, Lisbon, Portugal
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William T. Newsome
3Department of Neurobiology, Stanford University, CA, USA
5Howard Hughes Medical Institute, Stanford University, CA, USA
7Stanford Neurosciences Institute, Stanford University, CA, USA
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Krishna V. Shenoy
1Department of Electrical Engineering, Stanford University, CA, USA
3Department of Neurobiology, Stanford University, CA, USA
5Howard Hughes Medical Institute, Stanford University, CA, USA
6Department of Bioengineering, Stanford University, CA, USA
7Stanford Neurosciences Institute, Stanford University, CA, USA
8Bio-X program, Stanford University, CA, USA
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  • For correspondence: mouli@stanford.edu shenoy@stanford.edu maneesh@gatsby.ucl.ac.uk
Maneesh Sahani
2Gatsby Computational Neuroscience Unit, University College London, UK
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  • For correspondence: mouli@stanford.edu shenoy@stanford.edu maneesh@gatsby.ucl.ac.uk
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Abstract

Models of complex heterogeneous systems like the brain are inescapably incomplete, and thus always falsified with enough data. As neural data grow in volume and complexity, absolute measures of adequacy are being replaced by model selection methods that rank the relative accuracy of competing theories. Selection still depends on incomplete mathematical instantiations, but the implicit expectation is that ranking is robust to their details. Here we highlight a contrary finding of “brittleness,” where data matching one theory conceptually are ranked closer to an instance of another. In particular, selection between recent models of decision making is conceptually misleading when data are simulated with minor distributional mismatch, with mixed secondary signals, or with non-stationary parameters; and decision-related responses in macaque cortex show features suggesting that these effects may impact empirical results. We conclude with recommendations to mitigate such brittleness when using model selection to study neural signals.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 29, 2018.
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Brittleness in model selection analysis of single neuron firing rates
Chandramouli Chandrasekaran, Joana Soldado-Magraner, Diogo Peixoto, William T. Newsome, Krishna V. Shenoy, Maneesh Sahani
bioRxiv 430710; doi: https://doi.org/10.1101/430710
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Brittleness in model selection analysis of single neuron firing rates
Chandramouli Chandrasekaran, Joana Soldado-Magraner, Diogo Peixoto, William T. Newsome, Krishna V. Shenoy, Maneesh Sahani
bioRxiv 430710; doi: https://doi.org/10.1101/430710

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