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Could a Neuroscientist Understand a Microprocessor?
Open accessAbstract
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. Author Summary: Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used na•vely, fall short of producing a meaningful understanding.
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In the citing paper
“The sobering result was that performing interventionist neuroscience on the processor could not explain how the processor worked.”
From Neuroscience Needs Behavior: Correcting a Reductionist Bias (2017) · cites this paper as Jonas and Kording, 2017
In the citing paper
“The result was that none of these techniques came close to reverse engineering the standard stored-program computer architecture”
From Neuroscience Needs Behavior: Correcting a Reductionist Bias (2017) · cites this paper as Jonas and Kording, 2017
In the citing paper
“the recent study by Jonas and Kording (Jonas and Kording, 2017) we referred to earlier provides an empirical demonstration of the fundamental difference between intervening and recording versus understanding how information flows through processing steps.”
From Neuroscience Needs Behavior: Correcting a Reductionist Bias (2017) · cites this paper as Jonas and Kording, 2017
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“The behavioral data analyzed consisted of 10 s of spontaneous activity with no player actually playing the game”
From Neuroscience Needs Behavior: Correcting a Reductionist Bias (2017) · cites this paper as Jonas and Kording, 2017
In the citing paper
“Due to the complexity and variability of the brain, pure “bottom up” analysis of neural data faces potential challenges of interpretation (Robinson, 1992; Jonas and Kording, 2016).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Jonas and Kording, 2016
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Metadata
- Authors
- Jonas, E., & Paul Kording, K.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Jonas, Eric
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2016-05-26
- Volume
- 13
- Issue
- 1
- Pages
- e1005268-e1005268
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC5230747
- OpenAlex
- W2951107471
- Cited by
- 329
- References
- 83
- Citations by year
- 2026: 20 · 2025: 41 · 2024: 29 · 2023: 53 · 2022: 37 · 2021: 31 · 2020: 29 · 2019: 23 · 2018: 33 · 2017: 24 · 2016: 8
- FWCI
- 16.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (SN10040 DE-SC0012463, 1139158)
- U.S. Department of Energy (DESC0012463)
- Kavli Foundation
- Cisco Systems
- Amazon Web Services
- Thomas and Stacey Siebel Foundation
- NetApp
- VMware
- National Institutes of Health
- Directorate for Computer and Information Science and Engineering (CCF-1139158)
- Defense Advanced Research Projects Agency (FA8750, XData Award FA8750-12-2-0331, FA8750-12-2-0331)
- Samsung
- National Institute of Mental Health (R01MH103910)
- National Eye Institute (R01EY021579)
- National Institute of Neurological Disorders and Stroke (R01NS074044)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Keywords
- microprocessors, multimodal dataset, nonlinear dynamical systems, time series analysis, structure discovery
- MeSH terms
- Algorithms, Computer Simulation, Microcomputers, Models, Neurological, Neurosciences, Databases, Factual, Neural Networks, Computer, Video Games, Computational Biology, Connectome
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