Implications of neural networks for how we think about brain function

Robinson, David A.

doi:10.1017/s0140525x00072563

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

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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Due to the complexity and variability of the brain, pure bottom-up analysis of neural data faces potential challenges of interpretation.
“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 Robinson, 1992

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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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Implications of neural networks for how we think about brain function

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