The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Principles of Neural Design
Abstract
Two distinguished neuroscientists distil general principles from more than a century of scientific study, “reverse engineering” the brain to understand its design. Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading neuroscientists, strive to fill this gap, outlining a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently. Setting out to “reverse engineer” the brain—disassembling it to understand it—Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm. They examine bigger brains and the advantages of “anticipatory regulation”; identify constraints on neural design and the need to “nanofy”; and demonstrate the routes to efficiency in an integrated molecular system, phototransduction. They show that the principles of neural design at finer scales and lower levels apply at larger scales and higher levels; describe neural wiring efficiency; and discuss learning as a principle of biological design that includes “save only what is needed.” Sterling and Laughlin avoid speculation about how the brain might work and endeavor to make sense of what is already known. Their distinctive contr…
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In the citing paper
“All animals run an internal model of their world for the purpose of allostasis”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Sterling and Laughlin, 2015
In the citing paper
“Allostasis is not a condition of the body, but a process for how the brain regulates the body according to costs and benefits”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Sterling and Laughlin, 2015
In the citing paper
“Even single-celled organisms that lack a brain learn, remember, make predictions, and forage in service to allostasis”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Sterling and Laughlin, 2015
In the citing paper
“All brains accomplish the same core task (Sterling and Laughlin, 2015): to efficiently ensure resources for physiological systems within an animal’s body (i.e. its internal milieu) so that an animal can grow, survive and reproduce.”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Sterling and Laughlin, 2015
In the citing paper
“‘efficiency’ requires the ability to anticipate the body’s needs and satisfy them before they arise”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Sterling and Laughlin, 2015
In the citing paper
“A brain can be thought of as running an internal model that controls central pattern generators in the service of allostasis (for more on pattern generators, see Burrows, 1996; Sterling and Laughlin, 2015; Swanson, 2000).”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Sterling and Laughlin, 2015
Citations
Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268
LeDoux, J. E. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.155 · Cited by 8,444
Eye movements in reading and information processing: 20 years of research
Rayner, K. (1998) · Psychological Bulletin · doi:10.1037/0033-2909.124.3.372 · Cited by 7,979
The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme
Gould, S. J., & Lewontin, R. C. (1979) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1979.0086 · Cited by 7,770
Navigation-related structural change in the hippocampi of taxi drivers
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., & Frith, C. D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.070039597 · Cited by 3,225
Daugman, J. G. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.001160 · Cited by 3,219
The contrast sensitivity of retinal ganglion cells of the cat
Enroth-Cugell, C., & Robson, J. G. (1966) · The Journal of Physiology · doi:10.1113/jphysiol.1966.sp008107 · Cited by 2,625
Invariant visual representation by single neurons in the human brain
Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005) · Nature · doi:10.1038/nature03687 · Cited by 1,988
Cited by
Sorted by citations
Items 401–419 of 419
Big Data and Deep Learning Models
Hoffmann, D. S. (2022) · Principia an international journal of epistemology · doi:10.5007/1808-1711.2022.e84419
“Considering the biological side of this topic, a good starting point for the philosopher interested in this line of research is, in my opinion, the book by Sterling & Laughlin (2015).”
BackgroundCommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
Geenjaar, E., Lewis, N., Kashyap, A., Miller, R. L., & Calhoun, V. (2022) · arXiv.org · doi:10.48550/arxiv.2210.03667
“Although mechanistically different, due to metabolic and volume constraints, macroscale communication presumably exhibits strategies similar to sparse coding to efficiently transfer information between neural populations Bullmore & Sporns (2012); Sterling & Laughlin (2015).”
BackgroundCapsule Networks as Generative Models
Kiefer, A. B., Millidge, B., Tschantz, A., & Buckley, C. L. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-28719-0_14
Birkoben, T., & Kohlstedt, H. (2022) · doi:10.48550/arxiv.2204.12774
“Studying the neural design of biological species with only a few hundred or thousands neurons is a fruitful ansatz to develop novel computing primitives (Brenner, 1974; Kaiser and Varier, 2011; Sterling and Laughlin, 2015; Bosch et al., 2017; Dupre and Yuste, 2017; Lovas and Yuste, 2021; Giez et al., 2021).”
BackgroundInfluentialAxon Arbor Trade-off Between Wiring Cost, Delay, and Synchronization in Neuronal Networks
Liu, Q., Kurniawan, C., Xu, C., Jagtap, S., Deng, X., Lou, K., Soh, Y. S., & Nakahira, Y. (2021) · Conference on Information Sciences and Systems · doi:10.1109/ciss50987.2021.9400271
“However, there is a large variation in the cross-sectional area of axons, which leads to heterogeneity in conduction velocity [56], [57].”
BackgroundOptimal templates for signal extraction by noisy ideal detectors and human observers
Neri, P. (2020) · Journal of Computational Neuroscience · doi:10.1007/s10827-020-00768-z
“…be intrinsic limitations to the range over which the shape of the transducer can be encoded by our perceptual system: neural substrates are band-limited (Sterling and Laughlin 2015), and this should be reflected by psychological constructs such as the scale over which template amplitude is defined.”
BackgroundPredictive Processing: Avoiding the elephant in the roomPreprint
Klein, C. (2020) · doi:10.31234/osf.io/vdswk
“Brains are tightly constrained by space, speed, and energetic demands [10].”
BackgroundShahid, S., Kerskens, C., Burrows, M., & Witney, A. (2020) · bioRxiv · doi:10.1101/2020.01.17.910851
Bladder pressure encoding by near-independent fibre subpopulations – implications for decoding
Lubba, C., Ouyang, Z., Jones, N. S., Bruns, T., & Schultz, S. (2019) · bioRxiv · doi:10.1101/826297
“These thin fibres, however, do not fire at high frequencies, imposing a limit on the information rate per fibre [69].”
BackgroundLaws for Glia Organization Conserved Across MammalsPreprint
Pinto‐Duarte, A., Bogue, K., B, T. J. S. A., & Srinivasan, S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/449421 · Cited by 1
“It was a necessary first step, as it helped identify general principles of neural function (Finlay and Darlington, 1995a; Stevens, 2009; Sterling and Laughlin, 2015), and paved the way for more complex models of how individual cell types within circuits contribute to function (Hodge et al.”
BackgroundInformation content of downwelling skylight for non-imaging visual systemsPreprint
Thiermann, R., Sweeney, A., & Murugan, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/408989 · Cited by 1
Commentary: Cultural recycling of neural substrates during language evolution and development
Trettenbrein, P. C. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01507
“However, everything currently known about the encoding of information in single neurons and cell assemblies (Quiroga et al., 2005; Sterling and Laughlin, 2015), as well as the fundamental principles on which much simpler nervous systems realize cognitive functions (Gallistel, 2009), points toward…”
BackgroundInfluentialHuebner, B. (2023) · Res Philosophica · doi:10.11612/resphil.2262
An Integrative View on Entropic Measures Applied to the Nervous SystemPreprint
Mateos, D. M., & Velázquez, J. L. P. (2020) · Preprints.org · doi:10.20944/preprints202004.0402.v1
Intron and Small RNA Localization in Mammalian Neurons
Saini, H. (2019) · doi:10.13028/srmk-pk14
“The minimum diameter of neuronal projections is thought to be constrained by the need to fit a molecular transport system and allow bidirectional movement of cargo [Sterling and Laughlin, 2015].”
BackgroundExperience, Prediction, Surviving
Schulkin, J. (2019) · Oliver Wendell Holmes Jr., Pragmatism and Neuroscience · doi:10.1007/978-3-030-23100-2_3
Neuroscientific Considerations and the Law
Schulkin, J. A. (2019) · Springer eBooks · doi:10.1007/978-3-030-23100-2_10
Sparse algorithms for decoding and identification of neural circuits
Ukani, N. H. (2018) · doi:10.7916/d83x9qjj
“The photoreceptors, in turn, receive lateral feedback from the amacrine cells as well as feedback from LMCs such as L2 neurons [101, 121, 136].”
BackgroundNeurovascular Coupling, the BOLD Signal and MRI
Gabbiani, F., & Cox, S. (2017) · doi:10.1016/b978-0-12-801895-8.00015-4
Metadata
- Authors
- Sterling, P., & Laughlin, S.
- Affiliations
- Australian National University (AU)
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2015-05-22
- OpenAlex
- W4301042878
- Cited by
- 482
- References
- 630
- Citations by year
- 2026: 40 · 2025: 58 · 2024: 61 · 2023: 59 · 2022: 51 · 2021: 40 · 2020: 50 · 2019: 32 · 2018: 33 · 2017: 33 · 2016: 20 · 2015: 5
- FWCI
- 17.23
- 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 › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- brain evolution, computational neuroscience, anticipatory regulation, phototransduction, neural efficiency
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