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Cited by 107Neural dynamics and brain functionNeural Networks and ApplicationsMemory and Neural Mechanisms

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Implications of neural networks for how we think about brain function

Robinson, David A.·1992·Behavioral and Brain Sciences
DOI 10.1017/s0140525x00072563
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Abstract

Abstract Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization.

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Metadata

Authors
Robinson, D. A.
Affiliations
Johns Hopkins University (US)
Corresponding author
Robinson, David A.
Venue
Behavioral and Brain Sciences
Publisher
Cambridge University Press (CUP)
Published
1992-12
Volume
15
Issue
4
Pages
644-655
ISSN
  • 0140-525X (ISSN-L)
  • 1469-1825
DOI
10.1017/s0140525x00072563
OpenAlex
W2476507505
Cited by
107
References
1,393
Citations by year
2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 6 · 2018: 2 · 2017: 1 · 2016: 5 · 2015: 2 · 2014: 1 · 2013: 1 · 2012: 3
FWCI
2.10
Type
journal article
Language
en
Open access
closed
Licence
www.cambridge.org/core/terms (unspecified, 6743 d)
Locations
Behavioral and Brain Sciences (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Keywords
artificial neural networks, brain function, single-unit recording, interpretability, hidden units, oculomotor control, task complexity, reductionism, levels of organization, computational neuroscience

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