Could a neuroscientist understand a microprocessor?
Abstract
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. Furthermore, we argue for scientists using complex non-linear dynamical systems with known groundmore »
- Authors:
-
[1];
- Univ. of California, Berkeley, CA (United States)
- Northwestern Univ. and Rehabilitation Institute of Chicago, Chicago, IL (United States)
- Univ. College London (United Kingdom)
- Publication Date:
- Research Org.:
- Univ. of California, Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1367151
- Grant/Contract Number:
- SC0012463
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online); Journal Volume: 13; Journal Issue: 1; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING
Citation Formats
Jonas, Eric, Kording, Konrad Paul, and Diedrichsen, Jorn. Could a neuroscientist understand a microprocessor?. United States: N. p., 2017.
Web. doi:10.1371/journal.pcbi.1005268.
Jonas, Eric, Kording, Konrad Paul, & Diedrichsen, Jorn. Could a neuroscientist understand a microprocessor?. United States. https://doi.org/10.1371/journal.pcbi.1005268
Jonas, Eric, Kording, Konrad Paul, and Diedrichsen, Jorn. Thu .
"Could a neuroscientist understand a microprocessor?". United States. https://doi.org/10.1371/journal.pcbi.1005268. https://www.osti.gov/servlets/purl/1367151.
@article{osti_1367151,
title = {Could a neuroscientist understand a microprocessor?},
author = {Jonas, Eric and Kording, Konrad Paul and Diedrichsen, Jorn},
abstractNote = {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. Furthermore, 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.},
doi = {10.1371/journal.pcbi.1005268},
journal = {PLoS Computational Biology (Online)},
number = 1,
volume = 13,
place = {United States},
year = {2017},
month = {1}
}
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