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Inferring hidden structure in multilayered neural circuits
A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-n...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124781/ https://www.ncbi.nlm.nih.gov/pubmed/30138312 http://dx.doi.org/10.1371/journal.pcbi.1006291 |
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author | Maheswaranathan, Niru Kastner, David B. Baccus, Stephen A. Ganguli, Surya |
author_facet | Maheswaranathan, Niru Kastner, David B. Baccus, Stephen A. Ganguli, Surya |
author_sort | Maheswaranathan, Niru |
collection | PubMed |
description | A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data. |
format | Online Article Text |
id | pubmed-6124781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-61247812018-09-18 Inferring hidden structure in multilayered neural circuits Maheswaranathan, Niru Kastner, David B. Baccus, Stephen A. Ganguli, Surya PLoS Comput Biol Research Article A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data. Public Library of Science 2018-08-23 /pmc/articles/PMC6124781/ /pubmed/30138312 http://dx.doi.org/10.1371/journal.pcbi.1006291 Text en © 2018 Maheswaranathan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Maheswaranathan, Niru Kastner, David B. Baccus, Stephen A. Ganguli, Surya Inferring hidden structure in multilayered neural circuits |
title | Inferring hidden structure in multilayered neural circuits |
title_full | Inferring hidden structure in multilayered neural circuits |
title_fullStr | Inferring hidden structure in multilayered neural circuits |
title_full_unstemmed | Inferring hidden structure in multilayered neural circuits |
title_short | Inferring hidden structure in multilayered neural circuits |
title_sort | inferring hidden structure in multilayered neural circuits |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124781/ https://www.ncbi.nlm.nih.gov/pubmed/30138312 http://dx.doi.org/10.1371/journal.pcbi.1006291 |
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