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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All animals run an internal model of their world for the purpose of allostasis.
“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
Allostasis is a process for how the brain regulates the body according to costs and benefits, not a condition of the body.
“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
Even single-celled organisms that lack a brain learn, remember, make predictions, and forage in service to allostasis.
“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
All brains accomplish the same core task: to efficiently ensure resources for physiological systems within an animal's body so that the animal can grow, survive and reproduce.
“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
Efficiency requires the ability to anticipate the body's needs and satisfy them before they arise.
“‘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
A brain can be thought of as running an internal model that controls central pattern generators in the service of allostasis.
“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
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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 · see citing works
“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 · see citing works
“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 950 · see citing works
“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 · see citing works
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“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 10 · see citing works
Large-scale neuromorphic computing systems
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“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
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“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].”
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“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
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“…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
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Allostasis: A brain-centered, predictive mode of physiological regulation
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Self-citationRedefining the role of limbic areas in cortical processing
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The power of predictions: An emerging paradigm for psychological research
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“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
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Historical pitfalls and new directions in the neuroscience of emotion
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“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 .”
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“…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
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“[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
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“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 · see citing works
Insect-inspired AI for autonomous robots
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Fully neuromorphic vision and control for autonomous drone flight
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“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 122 · see citing works
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 139 · see citing works
“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 · see citing works
Balasubramanian, V. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2107022118 · Cited by 113 · see citing works
Brain-Inspired Learning on Neuromorphic Substrates
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“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 · see citing works
“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 · see citing works
Energy‐efficient neural information processing in individual neurons and neuronal networks
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“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 · see citing works
“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 · see citing works
“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 97 · see citing works
Dendrocentric learning for synthetic intelligence
Boahen, K. (2022) · Nature · doi:10.1038/s41586-022-05340-6 · Cited by 96 · see citing works
Barrett, L. F. (2022) · American Psychologist · doi:10.1037/amp0001054 · Cited by 100 · see citing works
“…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 · see citing works
“…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 90 · see citing works
“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 · see citing works
“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 107 · see citing works
“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 117 · see citing works
“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 86 · see citing works
“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 · see citing works
“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. (2016) · Animal Behaviour · doi:10.1016/j.anbehav.2016.05.005 · Cited by 89 · see citing works
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 · see citing works
“…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 · see citing works
Self-citationHoemann, K., & Barrett, L. F. (2018) · Cognition & Emotion · doi:10.1080/02699931.2018.1535428 · Cited by 111 · see citing works
“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 92 · see citing works
“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 · see citing works
“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 85 · see citing works
Gallistel, C. R. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.04.012 · Cited by 78 · see citing works
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 87 · see citing works
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 · see citing works
Varley, T., Sporns, O., Schaffelhofer, S., Scherberger, H., & Dann, B. (2021) · bioRxiv · doi:10.1073/pnas.2207677120 · Cited by 71 · see citing works
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 80 · see citing works
“…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 · see citing works
“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 73 · see citing works
“…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 72 · see citing works
“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 108 · see citing works
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. (2018) · British Journal for the Philosophy of Science · doi:10.1093/bjps/axw034 · Cited by 66 · see citing works
Harzsch, S., & Krieger, J. (2017) · Progress in neurobiology · doi:10.1016/j.pneurobio.2017.11.005 · Cited by 65 · see citing works
“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 · see citing works
“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 · see citing works
“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 63 · see citing works
“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 69 · see citing works
“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 60 · see citing works
Nave, K., Deane, G., Miller, M., & Clark, A. (2020) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1542 · Cited by 88 · see citing works
Hilgetag, C. C., Beul, S. F., van Albada, S. J., & Goulas, A. (2019) · Network Neuroscience · doi:10.1162/netn_a_00100 · Cited by 52 · see citing works
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 84 · see citing works
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 46 · see citing works
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 56 · see citing works
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 58 · see citing works
Data and Power Efficient Intelligence with Neuromorphic Learning Machines
Neftci, E. O. (2018) · iScience · doi:10.1016/j.isci.2018.06.010 · Cited by 50 · see citing works
“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 · see citing works
“…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 55 · see citing works
“…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 63 · see citing works
“…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 57 · see citing works
“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 51 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
Geometry and uncertainty in deep learning for computer vision
Kendall, A. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.35260 · Cited by 47 · see citing works
“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. (2017) · Current Opinion in Insect Science · doi:10.1016/j.cois.2017.09.011 · Cited by 47 · see citing works
“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 64 · see citing works
“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 · see citing works
Fluctuation-driven initialization for spiking neural network training
Rossbroich, J., Gygax, J., & Zenke, F. T. (2022) · Neuromorph. Comput. Eng. · doi:10.1088/2634-4386/ac97bb · Cited by 45 · see citing works
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 · see citing works
Categorical perception in animal communication and decision-making
Green, P., Brandley, N. C., & Nowicki, S. (2020) · Behavioral Ecology · doi:10.1093/beheco/araa004 · Cited by 44 · see citing works
“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., Chiles, J., McCaughan, A., Buckley, S., Tait, A., Lita, A., Biesecker, J., Fox, A., Olaya, D., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00840-9 · Cited by 43 · see citing works
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 55 · see citing works
“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 · see citing works
Categories and Their Role in the Science of Emotion
Barrett, L. F. (2017) · Psychological Inquiry · doi:10.1080/1047840x.2017.1261581 · Cited by 62 · see citing works
“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).”
- 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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