The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
The Computational Brain
Abstract
Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. How do groups of neurons interact to enable the organism to see, decide, and move appropriately? What are the principles whereby networks of neurons represent and compute? These are the central questions probed by The Computational Brain. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework. Computer models constrained by neurobiological data can help reveal how—networks of neurons subserve perception and behavior—bow their physical interactions can yield global results in perception and behavior, and how their physical properties are used to code information and compute solutions. The Computational Brain focuses mainly on three domains: visual perception, learning and memory, and sensorimotor integration. Examples of recent computer models in these domains are discussed in detail, highlighting strengths and weaknesses, and extracting principles applicable to other domains. Churchland and Sejnowski show how both abstract models and neurobiologically realistic models can have useful roles in computational neuroscience, and they predict the coevolution of models and experiments at many levels of organization, from the neuron to the system. The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques. The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. Bradford Books imprint
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In the citing paper
“In response to incoming signals from a set of N pre-synaptic units, formal neurons exhibit sudden activation if the weighted sum of inputs surpasses a threshold [253,254].”
From Fundamental constraints to the logic of living systems (2024) · cites this paper as [253]
In the citing paper
“This kind of columnar organization is typical in biology [38],[39].”
From Towards a mathematical theory of cortical micro-circuits (2009) · cites this paper as [39]
Citations
Cites
Women, Fire, and Dangerous Things: What Categories Reveal about the Mind
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Winograd, T. A., & Flores, F. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90026-9 · Cited by 3,685
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On the proper treatment of connectionism
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Parallel Networks that Learn to Pronounce English Text
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Self-citation
Cited by
Sorted by citations
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Chen, G., Nakamura, Y., et al. (2014) · Science · doi:10.1126/science.1254642 · Cited by 4,270
Mischel, W., & Shoda, Y. (1995) · Psychological Review · doi:10.1037/0033-295x.102.2.246 · Cited by 3,889
Thalamocortical oscillations in the sleeping and aroused brain
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993) · Science · doi:10.1126/science.8235588 · Cited by 3,564
Networks of spiking neurons: The third generation of neural network models
Maass, W. (1997) · Neural Networks · doi:10.1016/s0893-6080(97)00011-7 · Cited by 3,711
Metadata
- Authors
- Churchland, P. S., & Sejnowski, T. J.
- Affiliations
- University of California San Diego (US)
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 1992-06-12
- OpenAlex
- W1589549454
- Cited by
- 1,757
- References
- 33
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 36 · 2023: 27 · 2022: 19 · 2021: 31 · 2020: 39 · 2019: 51 · 2018: 44 · 2017: 40 · 2016: 39 · 2015: 21 · 2014: 37 · 2013: 68 · 2012: 61
- FWCI
- 12.21
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- The MIT Press eBooks (publishedVersion)
- Project Muse (Johns Hopkins University) (submittedVersion)
- 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
- population coding, neural population activity, artificial neural networks, computational neuroscience, neural coding, single-unit recording, brain function, neural network model
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