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Interrogating theoretical models of neural computation with deep inference

Sean R. Bittner, Agostina Palmigiano, Alex T. Piet, Chunyu A. Duan, Carlos D. Brody, Kenneth D. Miller, John P. Cunningham
doi: https://doi.org/10.1101/837567
Sean R. Bittner
1Department of Neuroscience, Columbia University
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Agostina Palmigiano
1Department of Neuroscience, Columbia University
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Alex T. Piet
2Princeton Neuroscience Institute
3Princeton University
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Chunyu A. Duan
4Institute of Neuroscience, Chinese Academy of Sciences
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Carlos D. Brody
2Princeton Neuroscience Institute
3Princeton University
5Howard Hughes Medical Institute
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Kenneth D. Miller
1Department of Neuroscience, Columbia University
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John P. Cunningham
6Department of Statistics, Columbia University
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  • For correspondence: jpc2181@columbia.edu
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1 Abstract

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon – whether behavioral or in terms of neural activity – and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choices of model parameters. Historically, the gold standard has been to analytically derive the relationship between model parameters and computational properties. However, this enterprise quickly becomes infeasible as biologically realistic constraints are included into the model increasing its complexity, often resulting in ad hoc approaches to understanding the relationship between model and computation. We bring recent machine learning techniques – the use of deep generative models for probabilistic inference – to bear on this problem, learning distributions of parameters that produce the specified properties of computation. Importantly, the techniques we introduce offer a principled means to understand the implications of model parameter choices on computational properties of interest. We motivate this methodology with a worked example analyzing sensitivity in the stomatogastric ganglion. We then use it to go beyond linear theory of neuron-type input-responsivity in a model of primary visual cortex, gain a mechanistic understanding of rapid task switching in superior colliculus models, and attribute error to connectivity properties in recurrent neural networks solving a simple mathematical task. More generally, this work suggests a departure from realism vs tractability considerations, towards the use of modern machine learning for sophisticated interrogation of biologically relevant models.

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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 4.0 International license.
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Posted November 18, 2019.
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Interrogating theoretical models of neural computation with deep inference
Sean R. Bittner, Agostina Palmigiano, Alex T. Piet, Chunyu A. Duan, Carlos D. Brody, Kenneth D. Miller, John P. Cunningham
bioRxiv 837567; doi: https://doi.org/10.1101/837567
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Interrogating theoretical models of neural computation with deep inference
Sean R. Bittner, Agostina Palmigiano, Alex T. Piet, Chunyu A. Duan, Carlos D. Brody, Kenneth D. Miller, John P. Cunningham
bioRxiv 837567; doi: https://doi.org/10.1101/837567

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