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Could a Neuroscientist Understand a Microprocessor?

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exis...

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Autores principales: Jonas, Eric, Kording, Konrad Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5230747/
https://www.ncbi.nlm.nih.gov/pubmed/28081141
http://dx.doi.org/10.1371/journal.pcbi.1005268
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author Jonas, Eric
Kording, Konrad Paul
author_facet Jonas, Eric
Kording, Konrad Paul
author_sort Jonas, Eric
collection PubMed
description There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.
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spelling pubmed-52307472017-01-31 Could a Neuroscientist Understand a Microprocessor? Jonas, Eric Kording, Konrad Paul PLoS Comput Biol Research Article There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods. Public Library of Science 2017-01-12 /pmc/articles/PMC5230747/ /pubmed/28081141 http://dx.doi.org/10.1371/journal.pcbi.1005268 Text en © 2017 Jonas, Kording 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
Jonas, Eric
Kording, Konrad Paul
Could a Neuroscientist Understand a Microprocessor?
title Could a Neuroscientist Understand a Microprocessor?
title_full Could a Neuroscientist Understand a Microprocessor?
title_fullStr Could a Neuroscientist Understand a Microprocessor?
title_full_unstemmed Could a Neuroscientist Understand a Microprocessor?
title_short Could a Neuroscientist Understand a Microprocessor?
title_sort could a neuroscientist understand a microprocessor?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5230747/
https://www.ncbi.nlm.nih.gov/pubmed/28081141
http://dx.doi.org/10.1371/journal.pcbi.1005268
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