Sterling, P. Allostasis: a model of predictive regulation. Physiol. Behav. 106, 5–15 (2012).
Sterling, P. & Laughlin, S. Principles of Neural Design (MIT Press, 2015).
Niven, J. E. & Laughlin, S. B. Energy limitation as a selective pressure on the evolution of sensory systems. J. Exp. Biol. 211, 1792–1804 (2008).
Pontzer, H. Energy expenditure in humans and other primates: a new synthesis. Annu. Rev. Anthropol. 44, 169–187 (2015).
White, O., Babič, J., Trenado, C., Johannsen, L. & Goswami, N. The promise of stochastic resonance in falls prevention. Front. Physiol. 9, 1865 (2019).
Adar, O., Shakargy, J. D. & Ilan, Y. The constrained disorder principle: beyond biological allostasis. Biology 14, 339 (2025).
Mendez-Balbuena, I. et al. Improved sensorimotor performance via stochastic resonance. J. Neurosci. 32, 12612–12618 (2012).
Krauss, P., Tziridis, K., Schilling, A. & Schulze, H. Cross-modal stochastic resonance as a universal principle to enhance sensory processing. Front. Neurosci. 12, 12:578 (2018).
Nobusako, S. et al. Stochastic resonance improves visuomotor temporal integration in healthy young adults. PLoS ONE 13, e0209382 (2018).
Vázquez-Rodríguez, B. et al. Stochastic resonance at criticality in a network model of the human cortex. Sci. Rep. 7, 13020 (2017).
Ghosh, A., Rho, Y., McIntosh, A. R., Kötter, R. & Jirsa, V. K. Noise during rest enables the exploration of the brain’s dynamic repertoire. PLoS Comput. Biol. 4, e1000196 (2008).
Attneave, F. Some informational aspects of visual perception. Psychol. Rev. 61, 183–193 (1954).
Barlow, H. B. in Sensory Communication (ed. Rosenblith, W. A.) Ch. 13 (MIT Press, 1961).
Shannon, C. & Weaver, W. The Mathematical Theory of Communication (Univ. Illinois Press, 1964).
Badre, D., Bhandari, A., Keglovits, H. & Kikumoto, A. The dimensionality of neural representations for control. Curr. Opin. Behav. Sci. 38, 20–28 (2021).
Bates, C. J. & Jacobs, R. A. Efficient data compression in perception and perceptual memory. Psychol. Rev. 127, 891–917 (2020).
Bernardi, S. et al. The geometry of abstraction in hippocampus and prefrontal cortex. Cell 183, 954–967.e21 (2020).
Guell, X., Schmahmann, J. D., Gabrieli, J. D. & Ghosh, S. S. Functional gradients of the cerebellum. eLife 7, e36652 (2018).
Kharabian Masouleh, S., Plachti, A., Hoffstaedter, F., Eickhoff, S. & Genon, S. Characterizing the gradients of structural covariance in the human hippocampus. NeuroImage 218, 116972 (2020).
Mack, M. L., Preston, A. R. & Love, B. C. Ventromedial prefrontal cortex compression during concept learning. Nat. Commun. 11, 46 (2020).
Przeździk, I., Faber, M., Fernández, G., Beckmann, C. F. & Haak, K. V. The functional organisation of the hippocampus along its long axis is gradual and predicts recollection. Cortex 119, 324–335 (2019).
Reber, T. P. et al. Representation of abstract semantic knowledge in populations of human single neurons in the medial temporal lobe. PLoS Biol. 17, e3000290 (2019).
Shaffer, C., Barrett, L. F. & Quigley, K. S. Signal processing in the vagus nerve: hypotheses based on new genetic and anatomical evidence. Biol. Psychol. 182, 108626 (2023).
Straub, I. et al. Gradients in the mammalian cerebellar cortex enable Fourier-like transformation and improve storing capacity. eLife 9, e51771 (2020).
Barrett, L. F. The theory of constructed emotion: an active inference account of interoception and categorization. Soc. Cognit. Affect. Neurosci. 12, 1–23 (2017).
Chanes, L. & Barrett, L. F. Redefining the role of limbic areas in cortical processing. Trends Cognit. Sci. 20, 96–106 (2016).
Finlay, B. L. & Uchiyama, R. Developmental mechanisms channeling cortical evolution. Trends Neurosci. 38, 69–76 (2015).
Öngür, D. & Price, J. L. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb. Cortex 10, 206–219 (2000).
Kleckner, I. R. et al. Evidence for a large-scale brain system supporting allostasis and interoception in humans. Nat. Hum. Behav. 1, 0069 (2017).
Girn, M., Setton, R., Turner, G. R. & Spreng, R. N. The “limbic network,” comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: evidence from multi-echo fMRI. Netw. Neurosci. 8, 860–882 (2024).
Zhang, J. et al. Cortical and subcortical mapping of the human allostatic–interoceptive system using 7 Tesla fMRI. Nat. Neurosci. 28, 2380–2391 (2025).
Theriault, J. E. et al. It’s not the thought that counts: allostasis at the core of brain function. Neuron 113, 4107–4133 (2025).
Glasser, M. F., Goyal, M. S., Preuss, T. M., Raichle, M. E. & Van Essen, D. C. Trends and properties of human cerebral cortex: correlations with cortical myelin content. NeuroImage 93, 165–175 (2014).
Hilgetag, C. C. & Goulas, A. ‘Hierarchy’ in the organization of brain networks. Phil. Trans. R. Soc. B 375, 20190319 (2020).
Zhang, J. et al. Topography impacts topology: anatomically central areas exhibit a “high-level connector” profile in the human cortex. Cereb. Cortex 30, 1357–1365 (2020).
van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).
Barbas, H. General cortical and special prefrontal connections: principles from structure to function. Annu. Rev. Neurosci. 38, 269–289 (2015).
John, Y. J., Zikopoulos, B., García-Cabezas, M. Á & Barbas, H. The cortical spectrum: a robust structural continuum in primate cerebral cortex revealed by histological staining and magnetic resonance imaging. Front. Neuroanat. 16, 897237 (2022).
Benna, M. K. & Fusi, S. Place cells may simply be memory cells: memory compression leads to spatial tuning and history dependence. Proc. Natl Acad. Sci. USA 118, e2018422118 (2021).
Gluck, M. A. & Myers, C. E. Hippocampal mediation of stimulus representation: a computational theory. Hippocampus 3, 491–516 (1993).
Barbas, H. & Rempel-Clower, N. Cortical structure predicts the pattern of corticocortical connections. Cereb. Cortex 7, 635–646 (1997).
Brincat, S. L., Siegel, M., Von Nicolai, C. & Miller, E. K. Gradual progression from sensory to task-related processing in cerebral cortex. Proc. Natl Acad. Sci. USA 115, E7202–E7211 (2018).
Siegle, J. H. et al. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 592, 86–92 (2021).
Bernhardt, B. C., Smallwood, J., Keilholz, S. & Margulies, D. S. Gradients in brain organization. NeuroImage 251, 118987 (2022).
Haueis, P. Multiscale modeling of cortical gradients: the role of mesoscale circuits for linking macro- and microscale gradients of cortical organization and hierarchical information processing. NeuroImage 232, 117846 (2021).
Paquola, C. et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 17, e3000284 (2019).
Zhou, D. et al. Compression supports low-dimensional representations of behavior across neural circuits. Preprint at bioRxiv https://doi.org/10.1101/2022.11.29.518415 (2022).
Katsumi, Y. et al. Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus. Commun. Biol. 6, 401 (2023).
Raut, R. V., Snyder, A. Z. & Raichle, M. E. Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proc. Natl Acad. Sci. USA 117, 20890–20897 (2020).
Shafiei, G. et al. Topographic gradients of intrinsic dynamics across neocortex. eLife 9, e62116 (2020).
Wang, X.-J. Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat. Rev. Neurosci. 21, 169–178 (2020).
MacIver, M. A. & Finlay, B. L. The neuroecology of the water-to-land transition and the evolution of the vertebrate brain. Phil. Trans. R. Soc. B 377, 20200523 (2022).
Binder, J. R., Desai, R. H., Graves, W. W. & Conant, L. L. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb. Cortex 19, 2767–2796 (2009).
Azzalini, D., Rebollo, I. & Tallon-Baudry, C. Visceral signals shape brain dynamics and cognition. Trends Cognit. Sci. 23, 488–509 (2019).
Engelen, T., Solcà, M. & Tallon-Baudry, C. Interoceptive rhythms in the brain. Nat. Neurosci. 26, 1670–1684 (2023).
Tort, A. B. L., Brankačk, J. & Draguhn, A. Respiration-entrained brain rhythms are global but often overlooked. Trends Neurosci. 41, 186–197 (2018).
Braga, R. M., Sharp, D. J., Leeson, C., Wise, R. J. S. & Leech, R. Echoes of the brain within default mode, association, and heteromodal cortices. J. Neurosci. 33, 14031–14039 (2013).
Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H. & Johnson, K. A. Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. J. Neurosci. 32, 10649–10661 (2012).
Szinte, M. & Knapen, T. Visual organization of the default network. Cereb. Cortex 30, 3518–3527 (2020).
Wei, W. et al. A function-based mapping of sensory integration along the cortical hierarchy. Commun. Biol. 7, 1–14 (2024).
Rizzolatti, G. et al. in Principles of Neural Science (eds. Kandel, E. R. et al.) 412–425 (McGraw-Hill, 2013).
Vogt, B. A. in Cingulate Neurobiology and Disease (ed. Vogt, B. A.) 65–94 (Oxford Univ. Press, 2009).
Barnaveli, I., Viganò, S., Reznik, D., Haggard, P. & Doeller, C. F. Hippocampal-entorhinal cognitive maps and cortical motor system represent action plans and their outcomes. Nat. Commun. 16, 4139 (2025).
Lathe, R., Singadia, S., Jordan, C. & Riedel, G. The interoceptive hippocampus: mouse brain endocrine receptor expression highlights a dentate gyrus (DG)–cornu ammonis (CA) challenge-sufficiency axis. PLoS ONE 15, e0227575 (2020).
Barsalou, L. W. Grounded cognition: past, present, and future. Top. Cognit. Sci. 2, 716–724 (2010).
Kalaska, J. et al. in Principles of Neural Science (eds. Kandel, E. R. et al.) Ch. 37 (McGraw-Hill, 2013).
Barrett, L. F. & Finlay, B. L. Concepts, goals and the control of survival-related behaviors. Curr. Opin. Behav. Sci. 24, 172–179 (2018).
Hickok, G. The Myth of Mirror Neurons: The Real Neuroscience of Communication and Cognition 292 (W. W. Norton, 2014).
Graziano, M. S. A. in Shared Representations (eds Obhi, S. S. & Cross, E. S.) 38–58 (Cambridge Univ. Press, 2016).
Seger, C. A. & Miller, E. K. Category learning in the brain. Annu. Rev. Neurosci. 33, 203–219 (2010).
Giffin, C., Wilkenfeld, D. & Lombrozo, T. The explanatory effect of a label: explanations with named categories are more satisfying. Cognition 168, 357–369 (2017).
Vouloumanos, A. & Waxman, S. R. Listen up! Speech is for thinking during infancy. Trends Cognit. Sci. 18, 642–646 (2014).
Waxman, S. R. & Gelman, S. A. in The Making of Human Concepts (eds Mareschal, D., Quinn, P. C. & Lea, S. E. G.) 99–130 (Oxford Univ. Press, 2010).
Waxman, S. R. & Markow, D. B. Words as invitations to form categories: evidence from 12- to 13-month-old infants. Cognit. Psychol. 29, 257–302 (1995).
Booth, A. E. & Waxman, S. Object names and object functions serve as cues to categories for infants. Dev. Psychol. 38, 948–957 (2002).
Graham, S. A., Kilbreath, C. S. & Welder, A. N. Thirteen-month-olds rely on shared labels and shape similarity for inductive inferences. Child Dev. 75, 409–427 (2004).
Nazzi, T. & Gopnik, A. Linguistic and cognitive abilities in infancy: when does language become a tool for categorization? Cognition 80, B11–B20 (2001).
Welder, A. N. & Graham, S. A. The influence of shape similarity and shared labels on infants’ inductive inferences about nonobvious object properties. Child Dev. 72, 1653–1673 (2001).
Chung, S. & Abbott, L. F. Neural population geometry: an approach for understanding biological and artificial neural networks. Curr. Opin. Neurobiol. 70, 137–144 (2021).
Kerrén, C., Reznik, D., Doeller, C. F. & Griffiths, B. J. Exploring the role of dimensionality transformation in episodic memory. Trends Cognit. Sci. 29, 614–626 (2025).
Barrett, L. F. & Theriault, J. in Handbook of Social Psychology 6th Edition (eds Gilbert, D., Fiske, S., Finkel, E. & Mendes, W.) https://doi.org/10.70400/BPQW3358 (Situational Press, 2025).
Lombrozo, T. Explanation and categorization: how ‘why?’ informs ‘what?’. Cognition 110, 248–253 (2009).
Muhle-Karbe, P. S. et al. Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex. Neuron 111, 3885–3899.e6 (2023).
Estes, W. K. Classification and Cognition (Oxford Univ. Press, 1994).
Medin, D. L. & Schaffer, M. M. Context theory of classification learning. Psychol. Rev. 85, 207–238 (1978).
Smith, E. E. in Foundations of Cognitive Science (ed. Posner, M.) 501–526 (MIT Press, 1989).
Rosch, E. & Mervis, C. B. Family resemblances: studies in the internal structure of categories. Cognit. Psychol. 7, 573–605 (1975).
Ashby, F. G. & Valentin, V. V. in Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience (ed. Wixted, J. T.) https://doi.org/10.1002/9781119170174.epcn508 (Wiley, 2018).
Nosofsky, R. M. Choice, similarity, and the context theory of classification. J. Exp. Psychol. 10, 104–114 (1984).
Rockland, K. S. Notes on visual cortical feedback and feedforward connections. Front. Syst. Neurosci. 16, 784310 (2022).
Markov, N. T. et al. Weight consistency specifies regularities of macaque cortical networks. Cereb. Cortex 21, 1254–1272 (2011).
Sherman, S. M. & Guillery, R. W. The role of the thalamus in the flow of information to the cortex. Phil. Trans. R. Soc. Lond. B 357, 1695–1708 (2002).
Sporns, O. Networks of the Brain (MIT Press, 2011).
Keller, A. J., Roth, M. M. & Scanziani, M. Feedback generates a second receptive field in neurons of the visual cortex. Nature 582, 545–549 (2020).
Aru, J. et al. Untangling cross-frequency coupling in neuroscience. Curr. Opin. Neurobiol. 31, 51–61 (2015).
Boyd, A. M., Kato, H. K., Komiyama, T. & Isaacson, J. S. Broadcasting of cortical activity to the olfactory bulb. Cell Rep. 10, 1032–1039 (2015).
Warwick, R. A. et al. Top-down modulation of the retinal code via histaminergic neurons of the hypothalamus. Sci. Adv. 10, eadk4062 (2024).
Schröder, S. et al. Arousal modulates retinal output. Neuron 107, 487–495.e9 (2020).
Halassa, M. M. & Sherman, S. M. Thalamocortical circuit motifs: a general framework. Neuron 103, 762–770 (2019).
Sherman, S. M. & Guillery, R. W. Exploring the Thalamus and its Role in Cortical Function 253–286 (MIT Press, 2006).
Beitz, A. J. The organization of afferent projections to the midbrain periaqueductal gray of the rat. Neuroscience 7, 133–159 (1982).
Uhlrich, D. J., Cucchiaro, J. B. & Sherman, S. M. The projection of individual axons from the parabrachial region of the brain stem to the dorsal lateral geniculate nucleus in the cat. J. Neurosci. 8, 4565–4575 (1988).
Card, J. P. & Moore, R. Y. Organization of lateral geniculate-hypothalamic connections in the rat. J. Comp. Neurol. 284, 135–147 (1989).
Fillinger, C., Yalcin, I., Barrot, M. & Veinante, P. Efferents of anterior cingulate areas 24a and 24b and midcingulate areas 24a′ and 24b′ in the mouse. Brain Struct. Funct. 223, 1747–1778 (2018).
Zhang, J. et al. Cortical and subcortical mapping of the allostatic-interoceptive system in the human brain using 7 Tesla fMRI. Nat. Neurosci. 28, 2380–2391 (2025).
Müller, E. J. et al. Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients. NeuroImage 222, 117224 (2020).
Phillips, J. M. et al. Primate thalamic nuclei select abstract rules and shape prefrontal dynamics. Neuron 113, 2014–2027.e12 (2025).
Shine, J. M. et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat. Neurosci. 22, 289–296 (2019).
Halassa, M. M. & Saalmann, Y. B. in The Cerebral Cortex and Thalamus (eds Usrey, W. M. & Sherman, S. M.) Ch. 46 (Oxford Univ. Press, 2024).
Shine, J. M. The thalamus integrates the macrosystems of the brain to facilitate complex, adaptive brain network dynamics. Prog. Neurobiol. 199, 101951 (2021).
Fusi, S., Miller, E. K. & Rigotti, M. Why neurons mix: high dimensionality for higher cognition. Curr. Opin. Neurobiol. 37, 66–74 (2016).
Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).
Tye, K. M. et al. Mixed selectivity: cellular computations for complexity. Neuron 112, 2289–2303 (2024).
Olshausen, B. A. & Field, D. J. in 23 Problems in Systems Neuroscience (eds van Hemmen, J. L. & Sejnowski, T. J.) 182–212 (Oxford Univ. Press, 2006).
Albright, T. D. & Stoner, G. R. Contextual influences on visual processing. Annu. Rev. Neurosci. 25, 339–379 (2002).
Basole, A., White, L. E. & Fitzpatrick, D. Mapping multiple features in the population response of visual cortex. Nature 423, 986–990 (2003).
David, S. V., Vinje, W. E. & Gallant, J. L. Natural stimulus statistics alter the receptive field structure of V1 neurons. J. Neurosci. 24, 6991–7006 (2004).
Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).
Parker, P. R. L., Brown, M. A., Smear, M. C. & Niell, C. M. Movement-related signals in sensory areas: roles in natural behavior. Trends Neurosci. 43, 581–595 (2020).
Spillmann, L., Dresp-Langley, B. & Tseng, C. Beyond the classical receptive field: the effect of contextual stimuli. J. Vis. 15, 7 (2015).
Bressler, S. L. & McIntosh, A. R. in Handbook of Brain Connectivity (eds Jirsa, V. K. & McIntosh, A.) 403–419 (Springer, 2007).
Gjorgjieva, J., Drion, G. & Marder, E. Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance. Curr. Opin. Neurobiol. 37, 44–52 (2016).
Heald, J. B., Wolpert, D. M. & Lengyel, M. The computational and neural bases of context-dependent learning. Annu. Rev. Neurosci. 46, 233–258 (2023).
Saxena, S. & Cunningham, J. P. Towards the neural population doctrine. Curr. Opin. Neurobiol. 55, 103–111 (2019).
Willems, R. M. & Peelen, M. V. How context changes the neural basis of perception and language. iScience 24, 102392 (2021).
Denève, S. & Machens, C. K. Efficient codes and balanced networks. Nat. Neurosci. 19, 375–382 (2016).
Sillito, A. M. The contribution of inhibitory mechanisms to the receptive field properties of neurones in the striate cortex of the cat. J. Physiol. 250, 305–329 (1975).
Balasubramanian, V. Heterogeneity and efficiency in the brain. Proc. IEEE 103, 1346–1358 (2015).
Favila, S. E., Samide, R., Sweigart, S. C. & Kuhl, B. A. Parietal representations of stimulus features are amplified during memory retrieval and flexibly aligned with top-down goals. J. Neurosci. 38, 7809–7821 (2018).
Lifanov-Carr, J. et al. Reconstructing spatiotemporal trajectories of visual object memories in the human brain. eNeuro 11, ENEURO.0091–24.2024 (2024).
Linde-Domingo, J., Treder, M. S., Kerrén, C. & Wimber, M. Evidence that neural information flow is reversed between object perception and object reconstruction from memory. Nat. Commun. 10, 179 (2019).
Pinotsis, D. A., Buschman, T. J. & Miller, E. K. Working memory load modulates neuronal coupling. Cereb. Cortex 29, 1670–1681 (2019).
Lifanov, J., Linde-Domingo, J. & Wimber, M. Feature-specific reaction times reveal a semanticisation of memories over time and with repeated remembering. Nat. Commun. 12, 3177 (2021).
Hutchinson, J. B. & Barrett, L. F. The power of predictions: an emerging paradigm for psychological research. Curr. Dir. Psychol. Sci. 28, 280–291 (2019).
Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 1–24 (2013).
Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010).
Gabhart, K. M., Xiong, Y. S. & Bastos, A. M. Predictive coding: a more cognitive process than we thought? Trends Cognit. Sci. 29, 627–640 (2025).
Keller, G. B. & Mrsic-Flogel, T. D. Predictive processing: a canonical cortical computation. Neuron 100, 424–435 (2018).
Rao, R. P. N. A sensory–motor theory of the neocortex. Nat. Neurosci. 27, 1221–1235 (2024).
Bastos, A. M., Lundqvist, M., Waite, A. S., Kopell, N. & Miller, E. K. Layer and rhythm specificity for predictive routing. Proc. Natl Acad. Sci. USA 117, 31459–31469 (2020).
Koren, V. & Denève, S. Computational account of spontaneous activity as a signature of predictive coding. PLoS Comput. Biol. 13, e1005355 (2017).
Luczak, A., McNaughton, B. L. & Kubo, Y. Neurons learn by predicting future activity. Nat. Mach. Intell. 4, 62–72 (2022).
Pinotsis, D. A., Loonis, R., Bastos, A. M., Miller, E. K. & Friston, K. J. Bayesian modelling of induced responses and neuronal rhythms. Brain Topogr. 32, 569–582 (2019).
Pinotsis, D. A. et al. Linking canonical microcircuits and neuronal activity: dynamic causal modelling of laminar recordings. Neuroimage 146, 355–366 (2017).
Xiong, Y. S. et al. Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity. Proc. Natl Acad. Sci. USA 121, e2315160121 (2024).
Straka, H., Simmers, J. & Chagnaud, B. P. A new perspective on predictive motor signaling. Curr. Biol. 28, R232–R243 (2018).
Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429 (2015).
Joyce, M. K. P. & Barbas, H. Cortical connections position primate area 25 as a keystone for interoception, emotion, and memory. J. Neurosci. 38, 1677–1698 (2018).
Wolpert, D. M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329 (1998).
Pinotsis, D. A., Siegel, M. & Miller, E. K. Sensory processing and categorization in cortical and deep neural networks. Neuroimage 202, 116118 (2019).
Hoffman, P., McClelland, J. L. & Lambon Ralph, M. A. Concepts, control, and context: a connectionist account of normal and disordered semantic cognition. Psychol. Rev. 125, 293 (2018).
Schyns, P. G., Goldstone, R. L. & Thibaut, J. P. The development of features in object concepts. Behav. Brain Sci. 21, 1–17 (1998).
Wilson-Mendenhall, C. D., Barrett, L. F. & Barsalou, L. W. Variety in emotional life: within-category typicality of emotional experiences is associated with neural activity in large-scale brain networks. Soc. Cogn. Affect. Neurosci. 10, 62–71 (2015).
Yee, E. & Thompson-Schill, S. L. Putting concepts into context. Psychon. Bull. Rev. 23, 1015–1027 (2016).
Barsalou, L. W. in Building Categories in Interaction: Linguistic Resources at Work (eds. Mauri, C., Fiorentini, I. & Goria, E.) 35–72 (John Benjamins, 2021).
Casasanto, D. & Lupyan, G. in The Conceptual Mind: New Directions in the Study of Concepts (eds Margolis, E. & Laurence, S.) 543–566 (MIT Press, 2015).
Coraci, D. A unified model of ad hoc concepts in conceptual spaces. Minds Mach. 32, 289–309 (2022).
Voorspoels, W., Storms, G. & Vanpaemel, W. Idealness and similarity in goal-derived categories: a computational examination. Mem. Cogn. 41, 312–327 (2013).
Posner, M. I. & Keele, S. W. On the genesis of abstract ideas. J. Exp. Psychol. 77, 353–363 (1968).
Edelman, G. M. & Gally, J. A. Degeneracy and complexity in biological systems. Proc. Natl Acad. Sci. USA 98, 13763–13768 (2001).
Marder, E. & Taylor, A. L. Multiple models to capture the variability in biological neurons and networks. Nat. Neurosci. 14, 133–138 (2011).
Brincat, S. L. & Miller, E. K. Prefrontal cortex networks shift from external to internal modes during learning. J. Neurosci. 36, 9739–9754 (2016).
Freedman, D. J. & Assad, J. A. Experience-dependent representation of visual categories in parietal cortex. Nature 443, 85–88 (2006).
Freedman, D. J., Riesenhuber, M., Poggio, T. & Miller, E. K. Categorical representation of visual stimuli in the primate prefrontal cortex. Science 291, 312–316 (2001).
Wallis, J. D., Anderson, K. C. & Miller, E. K. Single neurons in prefrontal cortex encode abstract rules. Nature 411, 953–956 (2001).
Antzoulatos, E. G. & Miller, E. K. Differences between neural activity in prefrontal cortex and striatum during learning of novel, abstract categories. Neuron 71, 243–249 (2011).
Wutz, A., Loonis, R., Roy, J. E., Donoghue, J. A. & Miller, E. K. Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron 97, 716–726.e8 (2018).
Zhang, X.-Y. et al. Adaptive stretching of representations across brain regions and deep learning model layers. Nat. Commun. 16, 10302 (2025).
Yaron, A. et al. Auditory cortex neurons that encode negative prediction errors respond to omissions of sounds in a predictable sequence. PLoS Biol. 23, e3003242 (2025).
Hoy, C. W. et al. Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex. Nat. Commun. 14, 8520 (2023).
Braga, A. & Schönwiesner, M. Neural substrates and models of omission responses and predictive processes. Front. Neural Circuits 16, 799581 (2022).
Adams, R. A., Bauer, M., Pinotsis, D. & Friston, K. J. Dynamic causal modelling of eye movements during pursuit: confirming precision-encoding in V1 using MEG. Neuroimage 132, 175–189 (2016).
Feldman, H. & Friston, K. J. Attention, uncertainty, and free-energy. Front. Hum. Neurosci. 4, 215 (2010).
Parr, T. & Friston, K. J. Attention or salience? Curr. Opin. Psychol. 29, 1–5 (2019).
Deneve, S. Bayesian spiking neurons I: inference. Neural Comput. 20, 91–117 (2008).
Yan, C., de Lange, F. P. & Richter, D. Conceptual associations generate sensory predictions. J. Neurosci. 43, 3733–3742 (2023).
Lundqvist, M. et al. Working memory control dynamics follow principles of spatial computing. Nat. Commun. 14, 1429 (2023).
Miller, E. K., Lundqvist, M. & Bastos, A. M. Working memory 2.0. Neuron 100, 463–475 (2018).
Recanatesi, S. et al. Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nat. Commun. 12, 1417 (2021).
Pinotsis, D. A., Fridman, G. & Miller, E. K. Cytoelectric coupling: electric fields sculpt neural activity and “tune” the brain’s infrastructure. Prog. Neurobiol. 226, 102465 (2023).
Buzsáki, G. & Draguhn, A. Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004).
Jadi, M. P. & Sejnowski, T. J. Cortical oscillations arise from contextual interactions that regulate sparse coding. Proc. Natl Acad. Sci. USA 111, 6780–6785 (2014).
Buzsáki, G. & Vöröslakos, M. Brain rhythms have come of age. Neuron 111, 922–926 (2023).
Fröhlich, F. & McCormick, D. A. Endogenous electric fields may guide neocortical network activity. Neuron 67, 129–143 (2010).
Anastassiou, C. A., Perin, R., Markram, H. & Koch, C. Ephaptic coupling of cortical neurons. Nat. Neurosci. 14, 217–223 (2011).
Lundqvist, M., Miller, E. K., Nordmark, J., Liljefors, J. & Herman, P. Beta: bursts of cognition. Trends Cognit. Sci. 28, 662–676 (2024).
Pinotsis, D. A. & Miller, E. K. Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity. Commun. Biol. 3, 707 (2020).
Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L. & Miller, E. K. Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nat. Commun. 9, 394 (2018).
Pinotsis, D. A. & Miller, E. K. Beyond dimension reduction: stable electric fields emerge from and allow representational drift. NeuroImage 253, 119058 (2022).
Bartos, M., Vida, I. & Jonas, P. Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nat. Rev. Neurosci. 8, 45–56 (2007).
Buzsáki, G. & Wang, X.-J. Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012).
Miao, C., Cao, Q., Moser, M.-B. & Moser, E. I. Parvalbumin and somatostatin interneurons control different space-coding networks in the medial entorhinal cortex. Cell 171, 507–521.e17 (2017).
van den Heuvel, M. P. et al. Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting-state functional connectivity. Hum. Brain Mapp. 37, 3103–3113 (2016).
Barrett, L. F. et al. The theory of constructed emotion: more than a feeling. Perspect. Psychol. Sci. 20, 392–420 (2025).
Mayr, E. What Makes Biology Unique?: Considerations on the Autonomy of a Scientific Discipline (Cambridge Univ. Press, 2004).
Picard, M., Kempes, C., Pontzer, H., Behnke, A. & Shaulson, E. D. Energy constraints on human health. Preprint at https://doi.org/10.31219/osf.io/ne3qg_v1 (2025).
McEwen, B. S. Stress, adaptation, and disease: allostasis and allostatic load. Ann. N. Y. Acad. Sci. 840, 33–44 (1998).
Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382–2395 (2014).
de Lange, S. C. et al. Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders. Nat. Hum. Behav. 3, 988–998 (2019).
Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305 (2015).
Sprooten, E. et al. Addressing reverse inference in psychiatric neuroimaging: meta-analyses of task-related brain activation in common mental disorders. Hum. Brain Mapp. 38, 1846–1864 (2017).
Stam, C. J. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw. Neurosci. 8, 1–23 (2024).
Bolt, T. et al. Autonomic physiological coupling of the global fMRI signal. Nat. Neurosci. 28, 1327–1335 (2025).
Bolt, T. et al. A parsimonious description of global functional brain organization in three spatiotemporal patterns. Nat. Neurosci. 25, 1093–1103 (2022).
Gu, Y. et al. Brain activity fluctuations propagate as waves traversing the cortical hierarchy. Cereb. Cortex 31, 3986–4005 (2021).
Raut, R. V. et al. Global waves synchronize the brain’s functional systems with fluctuating arousal. Sci. Adv. 7, eabf2709 (2021).
Luczak, A., Bartho, P. & Harris, K. D. Gating of sensory input by spontaneous cortical activity. J. Neurosci. 33, 1684–1695 (2013).
Parr, T., Pezzulo, G. & Friston, K. J. Active Inference: The Free Energy Principle in Mind, Brain, and Behavior (MIT Press, 2022).
Theriault, J. E. et al. A functional account of stimulation-based aerobic glycolysis and its role in interpreting BOLD signal intensity increases in neuroimaging experiments. Neurosci. Biobehav. Rev. 153, 105373 (2023).
Chalk, M., Marre, O. & Tkačik, G. Toward a unified theory of efficient, predictive, and sparse coding. Proc. Natl Acad. Sci. USA 115, 186–191 (2018).
Hechler, A., De Lange, F. P. & Riedl, V. The energy metabolic footprint of predictive processing in the human brain. Preprint at bioRxiv https://doi.org/10.1101/2023.12.08.570804 (2023).
Manookin, M. B. & Rieke, F. Two sides of the same coin: efficient and predictive neural coding. Annu. Rev. Vis. Sci. 9, 293–311 (2023).
Sengupta, B., Stemmler, M. B. & Friston, K. J. Information and efficiency in the nervous system — a synthesis. PLoS Comput. Biol. 9, e1003157 (2013).
Ali, A., Ahmad, N., de Groot, E., Johannes van Gerven, M. A. & Kietzmann, T. C. Predictive coding is a consequence of energy efficiency in recurrent neural networks. Patterns 3, 100639 (2022).
Quigley, K. S., Kanoski, S., Grill, W. M., Barrett, L. F. & Tsakiris, M. Functions of interoception: from energy regulation to experience of the self. Trends Neurosci. 44, 29–38 (2021).
Bastos, A. M. et al. Canonical microcircuits for predictive coding. Neuron 76, 695–711 (2012).
Shipp, S., Adams, R. A. & Friston, K. J. Reflections on agranular architecture: predictive coding in the motor cortex. Trends Neurosci. 36, 706–716 (2013).
Pezzulo, G., Zorzi, M. & Corbetta, M. The secret life of predictive brains: what’s spontaneous activity for? Trends Cognit. Sci. 25, 730–743 (2021).
Dimakou, A., Pezzulo, G., Zangrossi, A. & Corbetta, M. The predictive nature of spontaneous brain activity across scales and species. Neuron 113, 1310–1332 (2025).
Corcoran, A. W., Pezzulo, G. & Hohwy, J. From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition. Biol. Philos. 35, 32 (2020).
Levinthal, D. J. & Strick, P. L. The motor cortex communicates with the kidney. J. Neurosci. 32, 6726–6731 (2012).
Levinthal, D. J. & Strick, P. L. Multiple areas of the cerebral cortex influence the stomach. Proc. Natl Acad. Sci. USA 117, 13078–13083 (2020).
Alagapan, S. et al. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 622, 130–138 (2023).
Fujimoto, S. et al. Deep brain stimulation induces white matter remodeling and functional changes to brain-wide networks. Brain Stimul. 18, 242–243 (2025).
Lochmann, T. & Deneve, S. Neural processing as causal inference. Curr. Opin. Neurobiol. 21, 774–781 (2011).
Laland, K., Matthews, B. & Feldman, M. W. An introduction to niche construction theory. Evol. Ecol. 30, 191–202 (2016).
Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity (Princeton Univ. Press, 2012).
Mareschal, D., Quinn, P. C. & Lea, S. E. G. (eds) The Making of Human Concepts (Oxford Univ. Press, 2010).
Freddolino, P. L. & Tavazoie, S. Beyond homeostasis: a predictive-dynamic framework for understanding cellular behavior. Annu. Rev. Cell Dev. Biol. 28, 363–384 (2012).
Westlin, C. et al. Improving the study of brain–behavior relationships by revisiting basic assumptions. Trends Cognit. Sci. 27, 246–257 (2023).
Sennesh, E. et al. Interoception as modeling, allostasis as control. Biol. Psychol. 167, 108242 (2022).
Blakemore, S. J., Goodbody, S. J. & Wolpert, D. M. Predicting the consequences of our own actions: the role of sensorimotor context estimation. J. Neurosci. 18, 7511–7518 (1998).
Berkes, P., Orbán, G., Lengyel, M. & Fiser, J. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331, 83–87 (2011).
Barsalou, L. W. Grounded cognition. Annu. Rev. Psychol. 59, 617–645 (2008).
McMains, S. & Kastner, S. Interactions of top-down and bottom-up mechanisms in human visual cortex. J. Neurosci. 31, 587–597 (2011).
Gershman, S. J., Blei, D. M. & Niv, Y. Context, learning, and extinction. Psychol. Rev. 117, 197–209 (2010).
Frankland, S. M. & Greene, J. D. Concepts and compositionality: in search of the brain’s language of thought. Annu. Rev. Psychol. 71, 273–303 (2020).
Isomura, T., Shimazaki, H. & Friston, K. J. Canonical neural networks perform active inference. Commun. Biol. 5, 55 (2022).
Johansen, J. P. et al. Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation. Proc. Natl Acad. Sci. USA 111, E5584–E5592 (2014).
Barrett, L. F., Quigley, K. S. & Hamilton, P. An active inference theory of allostasis and interoception in depression. Philos. Trans. R. Soc. Lond. B 371, 20160011 (2016).
Shaffer, C., Westlin, C., Quigley, K. S., Whitfield-Gabrieli, S. & Barrett, L. F. Allostasis, action, and affect in depression: insights from the theory of constructed emotion. Annu. Rev. Clin. Psychol. 18, 553–580 (2022).
Barrett, L. F. How Emotions Are Made: The Secret Life of the Brain (Pan Macmillan, 2017).
Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).
Sherwood, C. C., Bauernfeind, A. L., Verendeev, A., Raghanti, M. A. & Hof, P. R. in Evolution of Nervous Systems 2nd edn (ed. Kaas, J. H.) 121–139 (Academic, 2017).
Kuzawa, C. W. et al. Metabolic costs and evolutionary implications of human brain development. Proc. Natl Acad. Sci. USA 111, 13010–13015 (2014).
Krienen, F. M., Yeo, B. T. T., Ge, T., Buckner, R. L. & Sherwood, C. C. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain. Proc. Natl Acad. Sci. USA 113, E469–E478 (2016).
Sherwood, C. C. & Gómez-Robles, A. Brain plasticity and human evolution. Annu. Rev. Anthropol. 46, 399–419 (2017).
Wei, Y. et al. Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nat. Commun. 10, 4839 (2019).
Barrett, L. F. Seven and a Half Lessons About the Brain (HarperCollins, 2020).
Gallivan, J. P., Bowman, N. A. R., Chapman, C. S., Wolpert, D. M. & Flanagan, J. R. The sequential encoding of competing action goals involves dynamic restructuring of motor plans in working memory. J. Neurophysiol. 115, 3113–3122 (2016).
Mesulam, M. M. From sensation to cognition. Brain 121, 1013–1052 (1998).
Jones, E. G. Synchrony in the interconnected circuitry of the thalamus and cerebral cortex. Ann. N. Y. Acad. Sci. 1157, 10–23 (2009).
Jones, E. G. The thalamic matrix and thalamocortical synchrony. Trends Neurosci. 24, 595–601 (2001).
Sherman, S. M. & Guillery, R. W. Distinct functions for direct and transthalamic corticocortical connections. J. Neurophysiol. 106, 1068–1077 (2011).
Sherman, S. M. Thalamus plays a central role in ongoing cortical functioning. Nat. Neurosci. 19, 533–541 (2016).
Usrey, W. M. & Sherman, S. M. Corticofugal circuits: communication lines from the cortex to the rest of the brain. J. Comp. Neurol. 527, 640–650 (2019).
Sherman, S. M. & Usrey, W. M. A reconsideration of the core and matrix classification of thalamocortical projections. J. Neurosci. 44, e0163242024 (2024).
Shine, J. M. et al. The low-dimensional neural architecture of cognitive complexity is related to activity in medial thalamic nuclei. Neuron 104, 849–855.e3 (2019).
Cappe, C., Morel, A., Barone, P. & Rouiller, E. M. The thalamocortical projection systems in primate: an anatomical support for multisensory and sensorimotor interplay. Cereb. Cortex 19, 2025–2037 (2009).
Lamme, V. A. F. & Roelfsema, P. R. The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23, 571–579 (2000).
Zeki, S. Multiple asynchronous stimulus- and task-dependent hierarchies (STDH) within the visual brain’s parallel processing systems. Eur. J. Neurosci. 44, 2515–2527 (2016).
Zeki, S. “Multiplexing” cells of the visual cortex and the timing enigma of the binding problem. Eur. J. Neurosci. 52, 4684–4694 (2020).
Demirtaş, M. et al. Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron 101, 1181–1194.e13 (2019).
Tognoli, E. & Kelso, J. A. S. The metastable brain. Neuron 81, 35–48 (2014).
García-Cabezas, M. Á, Zikopoulos, B. & Barbas, H. The structural model: a theory linking connections, plasticity, pathology, development and evolution of the cerebral cortex. Brain Struct. Funct. 224, 985–1008 (2019).
Markov, N. T. et al. Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. J. Comp. Neurol. 522, 225–259 (2013).
Lin, H.-M. et al. Reconstruction of intratelencephalic neurons in the mouse secondary motor cortex reveals the diverse projection patterns of single neurons. Front. Neuroanat. 12, 86 (2018).
Parent, M. & Parent, A. Single-axon tracing study of corticostriatal projections arising from primary motor cortex in primates. J. Comp. Neurol. 496, 202–213 (2006).
Rockland, K. S. & Drash, G. W. Collateralized divergent feedback connections that target multiple cortical areas. J. Comp. Neurol. 373, 529–548 (1996).
Weisenhorn, D. M. V., Ilung, R. B. & Spatz, W. B. Morphology and connections of neurons in area 17 projecting to the extrastriate areas mt and 19DM and to the superior colliculus in the monkey Callithrix jacchus. J. Comp. Neurol. 362, 233–255 (1995).
Zhang, S. et al. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665 (2014).
Leinweber, M., Ward, D. R., Sobczak, J. M., Attinger, A. & Keller, G. B. A sensorimotor circuit in mouse cortex for visual flow predictions. Neuron 95, 1420–1432.e5 (2017).
Keck, T. et al. Synaptic scaling and homeostatic plasticity in the mouse visual cortex in vivo. Neuron 80, 327–334 (2013).
Grill-Spector, K. & Weiner, K. S. The functional architecture of the ventral temporal cortex and its role in categorization. Nat. Rev. Neurosci. 15, 536–548 (2014).
Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–151 (2013).
Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016).
Larkum, M. E. Are dendrites conceptually useful? Neuroscience 489, 4–14 (2022).
Peysakhovich, B. et al. Primate superior colliculus is causally engaged in abstract higher-order cognition. Nat. Neurosci. 27, 1999–2008 (2024).
Fisher, A. & Rao, R. P. N. Recursive neural programs: a differentiable framework for learning compositional part-whole hierarchies and image grammars. Proc. Natl Acad. Sci. USA Nexus 2, pgad337 (2023).