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
The theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw154 · Cited by 1,426
“All brains accomplish the same core task (Sterling and Laughlin, 2015): to efficiently ensure resources for physiological systems within an animal’s body (i.”
InfluentialA Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster
Zheng, Z., Lauritzen, J. S., Perlman, E., Robinson, C. G., Nichols, M., Milkie, D., Torrens, O., Price, J., Fisher, C. B., Sharifi, N., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.019 · Cited by 1,050
“Maps of connectivity inform and constrain all models of how neuronal circuits transform information and subserve behavior (Braitenberg and Schüz, 1998; Marr, 1969; Sterling and Laughlin, 2015).”
BackgroundThe theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx060 · Cited by 1,009
“All brains accomplish the same core task (Sterling and Laughlin, 2015): to efficiently ensure resources for physiological systems within an animal’s body (i.”
InfluentialLieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892
Evidence for a large-scale brain system supporting allostasis and interoception in humans
Kleckner, I. R., Zhang, J., Touroutoglou, A., Chanes, L., Xia, C., Simmons, W. K., Quigley, K. S., Dickerson, B. C., & Feldman Barrett, L. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0069 · Cited by 641
“This process is called allostasis 20–22 .”
InfluentialHackel, L. M., Larson, G. M., Bowen, J. D., Ehrlich, G. A., Mann, T. C., Middlewood, B., Roberts, I. D., Eyink, J., Fetterolf, J. C., Gonzalez, F. J., et al. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x15002770 · Cited by 11
Large-scale neuromorphic computing systems
Furber, S. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/5/051001 · Cited by 565
“The full biological details of the neuron are dauntingly complex [ 3 ] , but widely accepted models abstract away most of the biological detail to yield a multiple-input single-output device where a range of different mathematical formulations can be used to describe the input-out transfer…”
BackgroundMethodInfluentialAn active inference theory of allostasis and interoception in depression
Barrett, L. F., Quigley, K. S., & Hamilton, P. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0011 · Cited by 554
“Several decades of research points to a different hypothesis: the human brain has a common computational architecture that, first and foremost, supports the human body as it moves, grows, survives and reproduces [1].”
BackgroundInfluentialAtzil, S., Gao, W., Fradkin, I., & Barrett, L. F. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0384-6 · Cited by 415
Zenke, F., & Vogels, T. P. (2021) · Neural Computation · doi:10.1162/neco_a_01367 · Cited by 288
Emotion words, emotion concepts, and emotional development in children: A constructionist hypothesis
Hoemann, K., Xu, F., & Barrett, L. F. (2019) · Developmental Psychology · doi:10.1037/dev0000686 · Cited by 354
“Moreover, the brain is thought to reinstate these patterns predictively rather than reactively, to actively infer the causes of sensory inputs from the body and the world (e.g., Friston, 2010; Sterling & Laughlin, 2015; Wolpert, Diedrichsen, & Flanagan, 2011).”
BackgroundImage sensing with multilayer nonlinear optical neural networks
Wang, T.-Y., Sohoni, M. M., Wright, L., Stein, M. M., Ma, S.-F., Onodera, T., Anderson, M. G., & McMahon, P. (2022) · Nature Photonics · doi:10.1038/s41566-023-01170-8 · Cited by 298
“…are directly bottlenecked by the number of pixels in the camera, including the energy and time costs of transducing images from the optical to digital electronic domain, of transporting them from the sensor to the post-processor, and performing high-dimensional digital post-processing 2,23 .”
BackgroundFunctions of interoception: From energy regulation to experience of the self
Quigley, K. S., Kanoski, S., Grill, W. M., Barrett, L. F., & Tsakiris, M. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.008 · Cited by 286
Allostasis: A brain-centered, predictive mode of physiological regulation
Schulkin, J., & Sterling, P. (2019) · Trends in Neurosciences · doi:10.1016/j.tins.2019.07.010 · Cited by 265
Self-citationRedefining the role of limbic areas in cortical processing
Chanes, L., & Barrett, L. F. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.11.005 · Cited by 259
Vértes, P. E., Rittman, T., Whitaker, K. J., Romero-García, R., Váša, F., Kitzbichler, M. G., Wagstyl, K., Fonagy, P., Dolan, R. J., Jones, P. B., et al. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0362 · Cited by 206
The power of predictions: An emerging paradigm for psychological research
Hutchinson, J. B., & Barrett, L. F. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419831992 · Cited by 216
“Recently, it has been proposed that many brains within the animal kingdom are structured to function via prediction and correction (Sterling & Laughlin, 2015).”
Viewpoints: Approaches to defining and investigating fear
Mobbs, D., Adolphs, R., Fanselow, M. S., Barrett, L. F., LeDoux, J. E., Ressler, K., & Tye, K. M. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0456-6 · Cited by 202
Historical pitfalls and new directions in the neuroscience of emotion
Barrett, L. F., & Satpute, A. B. (2019) · Neuroscience Letters · doi:10.1016/j.neulet.2017.07.045 · Cited by 180
Stöckl, C., & Maass, W. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00311-4 · Cited by 173
“Not only neurons with particular information-processing tasks in the smaller nervous systems of insects, but also neurons in the neocortex of mammals exhibit an astounding diversity of genetically encoded response properties 19–21 .”
BackgroundHaverkamp, A., Hansson, B. S., & Knaden, M. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00049 · Cited by 207
“…it is conceivable that the additional computation by local interneurons, which might be necessary to establish a combinatorial odor code, introduces metabolic costs that an insect aims to avoid whenever possible to resort to a computationally simpler labeled line (Sterling and Laughlin, 2015).”
BackgroundSurrogate gradients for analog neuromorphic computing
Cramer, B., Billaudelle, S., Kanya, S., Leibfried, A., Grübl, A., Karasenko, V., Pehle, C., Schreiber, K., Stradmann, Y., Weis, J., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109194119 · Cited by 146
“[4] A similar approach is taken for spikes by defining S̃j [t ](Sj [t ], Ṽj [t ])≡ Sj [t ] with associated derivatives”
MethodThe sense of should: A biologically-based framework for modeling social pressurePreprint
Theriault, J. E., Young, L., & Barrett, L. F. (2019) · Physics of Life Reviews · doi:10.31234/osf.io/x5rbs · Cited by 143
“A brain implements a predictive model to regulate an organism’s body in its environment ([54,236,278,281]; see Section 2.”
BackgroundInfluentialContext-aware experience sampling reveals the scale of variation in affective experience
Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69180-y · Cited by 124
Insect-inspired AI for autonomous robots
de Croon, G., Dupeyroux, J., Fuller, S. B., & Marshall, J. A. R. (2022) · Science Robotics · doi:10.1126/scirobotics.abl6334 · Cited by 138
Predictive Processing and the Representation Wars
Williams, D. (2017) · Minds and Machines · doi:10.1007/s11023-017-9441-6 · Cited by 153
Fully neuromorphic vision and control for autonomous drone flight
Paredes-Vallés, F., Hagenaars, J. J., Dupeyroux, J., Stroobants, S., Xu, Y., & de Croon, G. (2024) · Science Robotics · doi:10.1126/scirobotics.adi0591 · Cited by 123
“Coupling neuromorphic vision to neuromorphic processing promises low-energy and low-latency visual sensing and acting, as exhibited by agile animals such as flying insects [24].”
BackgroundMicro-connectomics: probing the organization of neuronal networks at the cellular scale
Schröter, M. S., Paulsen, O., & Bullmore, E. T. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.182 · Cited by 128
Neuromorphic artificial intelligence systems
Ivanov, D., Chezhegov, A., Kiselev, M. V., Grunin, A. A., & Larionov, D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.959626 · Cited by 155
“stores its state (memory represented by the membrane potential and the strength of synaptic connections), and a device that performs computations (Sterling and Laughlin, 2015).”
BackgroundPaying the brain's energy bill
Padamsey, Z., & Rochefort, N. L. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102668 · Cited by 114
Balasubramanian, V. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2107022118 · Cited by 113
Brain-Inspired Learning on Neuromorphic Substrates
Zenke, F., & Neftci, E. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3045625 · Cited by 107
“els discussed in the previous section. First, biological neurons possess internal dynamics on different timescales due to a plethora of bio-chemical processes that interact with the membrane dynamics [30, 65]. Because these dynamics play an important role for the approximations of the Jacobians Ht and Ft, which are th…”
BackgroundInfluentialNeuronal Migration and Lamination in the Vertebrate Retina
Amini, R., Rocha-Martins, M., & Norden, C. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00742 · Cited by 123
“To fulfill this function the retina has to be highly organized at the cellular and tissue level to allow the visual information to be sufficiently compressed, leading to fast transmission to the brain via the optic nerve (Nassi and Callaway, 2009; Sterling and Laughlin, 2015).”
BackgroundFunctional and anatomical specificity in a higher olfactory centre
Frechter, S., Bates, A. S., Tootoonian, S., Dolan, M.-J., Manton, J., Jamasb, A. R., Kohl, J., Bock, D., & Jefferis, G. (2019) · eLife · doi:10.7554/elife.44590 · Cited by 105
Energy‐efficient neural information processing in individual neurons and neuronal networks
Yu, L., & Yu, Y. (2017) · Journal of Neuroscience Research · doi:10.1002/jnr.24131 · Cited by 125
“This law is the revealed principle “send at the lowest rate” (Sterling and Laughlin, 2015).”
BackgroundThe neurobiology of interoception and affect
Feldman, M. J., Bliss-Moreau, E., & Lindquist, K. A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.009 · Cited by 101
“In contrast, thicker fi bers are less metabolically ef fi cient but can convey more information per unit of time and are less susceptible to channel noise [72,73].”
InfluentialElevated energy requirement of cone photoreceptors
Ingram, N. T., Fain, G., & Sampath, A. P. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001776117 · Cited by 115
“Cones release synaptic transmitter in darkness at the rate of a few hundred vesicles per second (31), requiring on the order of 10(4) ATPs per vesicle (5, 6) for a total of ∼2 to 3 × 10(6) ATPs/s in darkness, decreasing in the light.”
BackgroundAn energy costly architecture of neuromodulators for human brain evolution and cognition
Castrillón, G., Epp, S. M., Bose, A., Fraticelli, L., Hechler, A., Belenya, R., Ranft, A., Yakushev, I. Y., Utz, L., Sundar, L. K. S., et al. (2023) · Science Advances · doi:10.1126/sciadv.adi7632 · Cited by 102
Dendrocentric learning for synthetic intelligence
Boahen, K. A. (2022) · Nature · doi:10.1038/s41586-022-05340-6 · Cited by 107
Barrett, L. F. (2022) · American Psychologist · doi:10.1037/amp0001054 · Cited by 118
“…of the body, called allostasis (Sterling, 2012; for a discussion of some of the neuroscience hypotheses, see, e.g., Barrett, 2017b; Katsumi, Kamona et al. (2021); Katsumi, Theriault et al. (2022); Kleckner et al., 2017; Sterling & Laughlin, 2015; formodeling details, see Sennesh et al., 2022).”
BackgroundAllostasis, action and affect in depression: Insights from the theory of constructed emotion
Shaffer, C., Westlin, C., Quigley, K. S., Whitfield-Gabrieli, S., & Barrett, L. F. (2022) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-081219-115627 · Cited by 94
“…reviewed thus far is consistent with the hypothesis that the brain’s most important job is to efficiently coordinate and regulate the systems of its body as an animal moves and grows within an ever-changing and only partly predictable external world (e.g., Ashby 1960, Sterling & Laughlin 2015).”
BackgroundInfluentialOf what is “minimal cognition” the half-baked version?
Lyon, P. (2019) · Adaptive Behavior · doi:10.1177/1059712319871360 · Cited by 95
“The components are ‘‘irreducibly small’’ and protein circuits operate ‘‘near the thermodynamic limit of energy efficiency’’ (Sterling & Laughlin, 2015, p. 124), which is why they are used extensively in all organisms, even those with brains.”
BackgroundInfluentialThe frontiers of insect cognition
Perry, C. J., Barron, A. B., & Chıttka, L. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.05.011 · Cited by 131
“There are >150 identified types of neurons in just its visual periphery (the ganglia lamina, medulla and lobula) [62] and”
BackgroundDeconstructing arousal into wakeful, autonomic and affective varieties
Satpute, A. B., Kragel, P. A., Barrett, L. F., Wager, T. D., & Bianciardi, M. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2018.01.042 · Cited by 118
“One possibility is that functional interactions are described well by the concept of allostasis [1] in which activity in brainstem areas is modulated by predictions of future visceromotor fluctuations sent from limbic and paralimbic circuitry [23,127–131].”
BackgroundSynchrony and so much more: Diverse roles for electrical synapses in neural circuits
Connors, B. W. (2017) · Developmental Neurobiology · doi:10.1002/dneu.22493 · Cited by 121
“Cellular processes are inherently noisy—membrane potentials vacillate as, for example, ion channels flip stochastically between open and closed states, signaling molecules trigger downstream cascades, and vesicles fuse and liberate transmitter (White et al., 2000; Sterling and Laughlin, 2015).”
BackgroundEmotion Perception as Conceptual Synchrony
Gendron, M., & Barrett, L. F. (2018) · Emotion Review · doi:10.1177/1754073917705717 · Cited by 98
“Critically, an efficient brain anticipates the body’s needs and prepares the best actions to satisfy them before they arise (Sterling, 2012; Sterling & Laughlin, 2015).”
BackgroundConcepts, goals and the control of survival-related behaviors
Feldman Barrett, L., & Finlay, B. L. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.10.001 · Cited by 89
“Active inference, within this internal (forward) model, is thought to equip a brain to anticipate the needs of the body and attempt to meet those needs before they arise [52], referred to as allostasis [51], thereby allowing efficient control of purposeful motor actions in the service of a balanced energy budget.”
Do insects feel pain? A question at the intersection of animal behaviour, philosophy and robotics
Adamo, S. A. (2016) · Animal Behaviour · doi:10.1016/j.anbehav.2016.05.005 · Cited by 111
Allostasis as a core feature of hierarchical gradients in the human brain
Katsumi, Y., Theriault, J. E., Quigley, K. S., & Barrett, L. F. (2022) · Network Neuroscience · doi:10.1162/netn_a_00240 · Cited by 88
“…gradient in the cerebral cortex; e.g., Barbas, 2015; Hilgetag & Goulas, 2020; Margulies et al., 2016; Zhang et al., 2019), and (b) predictive regulation of the body’s energy resources, called allostasis (Schulkin & Sterling, 2019; Sennesh et al., 2021; Sterling, 2012; Sterling & Laughlin, 2015).”
BackgroundHomeostasis vs Allostasis: Implications for Brain Function and Mental Disorders
Sterling, P. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1043 · Cited by 88
Self-citationHoemann, K., & Barrett, L. F. (2018) · Cognition & Emotion · doi:10.1080/02699931.2018.1535428 · Cited by 130
“By accurately inferring causes, the brain can anticipate the needs of the body, and prepare to meet those needs before they arise (Sterling, 2012; Sterling & Laughlin, 2015).”
BackgroundJeanne, J. M., & Wilson, R. I. (2015) · Neuron · doi:10.1016/j.neuron.2015.10.018 · Cited by 94
“For example, vision begins with a large array of photoreceptors, and hearing begins with a large array of hair cells (Sterling and Laughlin, 2015).”
Grounding cognition: heterarchical control mechanisms in biology
Bechtel, W., & Bich, L. (2021) · Philosophical Transactions of the Royal Society of London. Biological Sciences · doi:10.1098/rstb.2019.0751 · Cited by 82
“This conforms to one of Sterling & Laughlin’s [115] principles of neural design: compute intracellularly and chemically whenever possible.”
MethodAdaptive coding for dynamic sensory inference
Młynarski, W. F., & Hermundstad, A. M. (2017) · bioRxiv · doi:10.1101/189506 · Cited by 82
A resource-rational theory of set size effects in human visual working memory
van den Berg, R., & Ma, W. J. (2018) · eLife · doi:10.7554/elife.34963 · Cited by 93
Gallistel, C. R. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.04.012 · Cited by 83
Bioinspired Materials for In Vivo Bioelectronic Neural Interfaces
Woods, G. A., Rommelfanger, N. J., & Hong, G. (2020) · Matter · doi:10.1016/j.matt.2020.08.002 · Cited by 89
Opportunities and Challenges for Psychiatry in the Connectomic Era
Fornito, A., Bullmore, E. T., & Zalesky, A. (2017) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.08.003 · Cited by 74
Varley, T. F., Sporns, O., Schaffelhofer, S., Scherberger, H., & Dann, B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207677120 · Cited by 84
Basic functional trade-offs in cognition: An integrative framework
Giudice, M. D., & Crespi, B. J. (2018) · Cognition · doi:10.1016/j.cognition.2018.06.008 · Cited by 86
“…broad sense it does not require the existence of a conscious designer: indeed, the crucial insight of Darwinian biology is that design and function can arise from the blind, undirected, and impersonal process of natural selection (Alderson & Doyle, 2010; Dennett, 2009; Sterling & Laughlin, 2015).”
InfluentialFrom symbols to icons: the return of resemblance in the cognitive neuroscience revolution
Williams, D., & Colling, L. (2018) · Synthese · doi:10.1007/s11229-017-1578-6 · Cited by 69
“In addition, purely reactive systems are extremely energetically inefficient (Sterling and Laughlin 2015): among other things, anticipatory dynamics enable brains to focus only on functionally significant surprises in sensory input, effectively ignoring much the incoming signal (see below).”
BackgroundInfluentialGrossman, P. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108739 · Cited by 82
“…out together with the circulation of the oxygenated blood through our bodies) — functioning of this phenomenon of RSA is here hypothesized to be central to the principle of allostasis, the capacity to adjust physiological function according to predicted energy demands (Sterling & Laughlin, 2015).”
Computational Foundations of Natural Intelligence
van Gerven, M. A. J. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00112 · Cited by 76
“For example, single-celled protozoans already show rather complex ingestive, defensive and reproductive behavior, which is regulated by molecular signaling (Swanson, 2012; Sterling and Laughlin, 2016).”
BackgroundMixed emotions in the predictive brain
Hoemann, K., Gendron, M., & Barrett, L. F. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.05.013 · Cited by 118
Inferring hidden structure in multilayered neural circuits
Maheswaranathan, N., Kastner, D. B., Baccus, S., & Ganguli, S. (2017) · bioRxiv · doi:10.1101/120956 · Cited by 67
“Another fundamental aspect of retinal processing is the sparse and precise firing patterns of retinal ganglion cells [52], presumably due to reasons of energy efficiency [51,53].”
BackgroundExplanation in Computational Neuroscience: Causal and Non-causal
Chirimuuta, M. (2016) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axw034 · Cited by 119
Harzsch, S., & Krieger, J. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.11.005 · Cited by 82
“Maintaining nervous tissue in a physiologically active state comes at the price of substantial energy demands (Niven, 2016; Sterling and Laughlin, 2015).”
InfluentialEvolutionarily conserved mechanisms for the selection and maintenance of behavioural activity
Fiore, V. G., Dolan, R. J., Strausfeld, N. J., & Hirth, F. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0053 · Cited by 66
“Decisions of the second kind involve more complex circuits that serve to detect external events, weigh their saliency and relevance, and decide when and how to act on them [1].”
BackgroundTsukamoto, Y., & Omi, N. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00104 · Cited by 64
“Bipolar cells may implement diffusional filtering by their dendrite positioning (Rao-Mirotznik et al., 1998; Sterling and Laughlin, 2015).”
BackgroundZendrikov, D., Solinas, S. M. G., & Indiveri, G. (2023) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ace64c · Cited by 67
“In section 4, we discuss the benefits of adopting the principles of neural design [43] that we demonstrated with experimental data, and in section 5 we present the concluding remarks.”
MethodBurleson, M. H., & Quigley, K. S. (2019) · Social Neuroscience · doi:10.1080/17470919.2019.1702095 · Cited by 70
“The TCE rests on the concept of allostasis, the process by which the brain predictively regulates and balances its energy needs in service of surviving and thriving (Sterling, 2004, 2012; Sterling & Laughlin, 2015).”
Tutorial: High-speed low-power neuromorphic systems based on magnetic Josephson junctions
Schneider, M. L., Donnelly, C. A., & Russek, S. E. (2018) · Journal of Applied Physics · doi:10.1063/1.5042425 · Cited by 62
Nave, K., Deane, G., Miller, M., & Clark, A. (2020) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1542 · Cited by 103
Hilgetag, C. C., Beul, S. F., van Albada, S. J., & Goulas, A. (2019) · Network Neuroscience · doi:10.1162/netn_a_00100 · Cited by 53
How biological vision succeeds in the physical world
Purves, D., Monson, B. B., Sundararajan, J., & Wojtach, W. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311309111 · Cited by 88
Predictive processing models and affective neuroscience
Lee, K. M., Ferreira‐Santos, F., & Satpute, A. B. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.09.009 · Cited by 53
Principles and open questions in functional brain network reconstruction
Korhonen, O., Zanin, M., & Papo, D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25462 · Cited by 59
An amplitude code transmits information at a visual synapse
James, B., Darnet, L., Moya‐Díaz, J., Seibel, S.-H., & Lagnado, L. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0403-6 · Cited by 59
Data and Power Efficient Intelligence with Neuromorphic Learning Machines
Neftci, E. (2018) · iScience · doi:10.1016/j.isci.2018.06.010 · Cited by 52
“They evolved with tight metabolic constraints and the need to adapt to new environments and changing bodies, sensors, and actuators, as a result of development, gradual degradation, and wear and tear (Sterling and Laughlin, 2015).”
BackgroundHow and why people synchronize: An integrated perspective
daSilva, E. B., & Wood, A. (2025) · Personality and Social Psychology Review · doi:10.1177/10888683241252036 · Cited by 56
“…is everywhere in our bodies and brains: The rhythms by which we live include the sleep-wake circadian cycle, a steady walking gait, the pacemaker cells that keep our hearts beating, the tempo of syllables in our speech, and the oscillation of brain waves (Sterling & Laughlin, 2015; Strogatz, 2004).”
The Theory of Constructed Emotion: More Than a Feeling
Barrett, L. F., Atzil, S., Bliss‐Moreau, E., Chanes, L., Gendron, M., Hoemann, K., Katsumi, Y., Kleckner, I. R., Lindquist, K. A., Quigley, K. S., et al. (2025) · Perspectives on Psychological Science · doi:10.1177/17456916251319045 · Cited by 67
“…differences in metabolic function between humans and our closest genetic relatives, common chimpanzees (e.g., Clarke & Sokoloff, 1999), and energy regulation, including allo-stasis, is crucial to understanding evolutionary change (e.g,. Lane, 2014; Pontzer, 2015; Sterling & Laughlin, 2015).”
Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair
Stöber, T. M., Batulin, D., Triesch, J., Narayanan, R., & Jedlička, P. (2023) · Communications Biology · doi:10.1038/s42003-023-04823-0 · Cited by 65
“…of the nervous system with respect to hyperexcit-ability is a high evolutionary price that we have to pay for the enormous computational capabilities of our energy-ef fi cient brains 292,293 , which seem to operate close to criticality that maximises information-processing 300 , but see also ref.”
An Introduction to Predictive Processing Models of Perception and Decision‐Making
Sprevak, M. D., & Smith, R. S. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12704 · Cited by 66
“The efficient coding hypothesis suggests that the brain aims to minimize this number of necessary bits (Simoncelli & Olshausen, 2001; Srinivasan, Laughlin, & Dubs, 1982; Sterling & Laughlin, 2015).”
BackgroundOn the research of time past: the hunt for the substrate of memory
Queenan, B. N., Ryan, T. J., Gazzaniga, M. S., & Gallistel, C. R. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13348 · Cited by 55
A ventral salience network in the macaque brain
Touroutoglou, A., Bliss‐Moreau, E., Zhang, J., Mantini, D., Vanduffel, W., Dickerson, B. C., & Barrett, L. F. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.029 · Cited by 54
“Allostasis, as the continually adjustment of the body’s internal milieu to promote survival and reproduction, is a fundamental feature of the mammalian nervous system (Sterling, 2012; Sterling and Laughlin, 2015).”
Nakahira, Y., Liu, Q., Sejnowski, T. J., & Doyle, J. C. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916367118 · Cited by 49
“number, resulting in different speed and accuracy in nerve signaling (2, 3).”
BackgroundInfluentialThe energetic basis for smooth human arm movements
Wong, J. D., Cluff, T., & Kuo, A. (2020) · bioRxiv · doi:10.1101/2020.12.28.424067 · Cited by 48
“And in the experimental realm, energy expenditure is regarded as a major factor in animal life and behavior (Alexander, 1996), even to the small scale of a single neural action potential (Sterling and Laughlin, 2017).”
BackgroundKelber, A. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.09.007 · Cited by 48
Intrinsic functional connectivity is organized as three interdependent gradients
Zhang, J., Abiose, O., Katsumi, Y., Touroutoglou, A., Dickerson, B. C., & Barrett, L. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-51793-7 · Cited by 47
Geometry and uncertainty in deep learning for computer vision
Kendall, A. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.35260 · Cited by 47
“Moreover, being able to build intelligent vision models may contribute to our understanding of the neuroscience behind visual intelligence (Sterling and Laughlin, 2015).”
BackgroundLearning from connectomics on the fly
Schlegel, P., Costa, M., & Jefferis, G. S. X. E. (2017) · Current Opinion in Insect Science · doi:10.1016/j.cois.2017.09.011 · Cited by 49
“Insect brains encode efficient neural solutions matched to the animal’s neuroethological context [4].”
BackgroundConsciousness as a Physical Process Caused by the Organization of Energy in the Brain
Pepperell, R. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02091 · Cited by 76
“The mechanisms that enable performance of these tasks can be seen at work in organisms with relatively simple nervous systems, such as the C. elegans worm (Sterling and Laughlin, 2017).”
BackgroundInfluentialConsciousness explained or consciousness redefined?
Adamo, S. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606942113 · Cited by 45
Fluctuation-driven initialization for spiking neural network training
Rossbroich, J., Gygax, J., & Zenke, F. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac97bb · Cited by 39
Encoding of environmental illumination by primate melanopsin neurons
Liu, A., Milner, E. S., Peng, Y.-R., Blume, H. A., Brown, M. C., Bryman, G. S., Emanuel, A. J., Morquette, P., Viet, N.-M., Sanes, J. R., et al. (2023) · Science · doi:10.1126/science.ade2024 · Cited by 44
Categorical perception in animal communication and decision-making
Green, P. A., Brandley, N. C., & Nowicki, S. (2020) · Behavioral Ecology · doi:10.1093/beheco/araa004 · Cited by 58
“Less obvious but potentially equally important costs are those required for developing and maintaining the neural substrate associated with the cognitive processing and memory necessary to compare potential mates (Sterling and Laughlin 2015).”
BackgroundSuperconducting optoelectronic single-photon synapses
Khan, S., Primavera, B. A., Chiles, J., McCaughan, A. N., Buckley, S. M., Tait, A. N., Lita, A. E., Biesecker, J. P., Fox, A. E., Olaya, D. I., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00840-9 · Cited by 49
Theriault, J. E., Shaffer, C., Dienel, G. A., Sander, C. Y. M., Hooker, J. M., Dickerson, B. C., Barrett, L. F., & Quigley, K. S. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105373 · Cited by 60
“1 μm thin to ~10 μm thick—have major implications for their ability to transmit information (Koch et al., 2006; Perge et al., 2009, 2012; Sengupta et al., 2013; Sterling and Laughlin, 2015).”
InfluentialA thousand brains: toward biologically constrained AI
Hole, K. J., & Ahmad, S. (2021) · SN Applied Sciences · doi:10.1007/s42452-021-04715-0 · Cited by 42
Categories and Their Role in the Science of Emotion
Barrett, L. F. (2017) · Psychological Inquiry · doi:10.1080/1047840x.2017.1261581 · Cited by 67
“R approach, as she describes it, is inconsistent with the anatomic, signal processing, and metabolic properties of the brain (for a review, see Barrett, 2017, in press-b; Barrett & Simmons, 2015; Chanes & Barrett, 2016; Sterling, 2012; Sterling & Laughlin, 2015).”
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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