We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites and amplify that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology.

Faculty of Natural Sciences University of Stirling United Kingdom

Institute of Philosophy Czech Academy of Sciences Czech Republic

See more in PubMed

Adeel A., Adetomi A., Ahmed K., et al. Unlocking the potential of two-point cells for energy-efficient and resilient training of deep nets. IEEE Transac. Emerg. Topics in Comput. Intelli. 2023:1–11.

Adeel A., Franco M., Raza M., et al. 2022. Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing. DOI

Alilović J., Timmermans B., Reteig L.C., et al. No evidence that predictions and attention modulate the first feedforward sweep of cortical information processing. Cerebr. Cortex. 2019;29:2261–2278. PubMed PMC

Almeida V.N. The neural hierarchy of consciousness: a theoretical model and review on neurophysiology and NCCs. Neuropsychologia. 2022;169 PubMed

Anderson K.M., Collins M.A., Chin R., Ge T., Rosenberg M.D., Holmes A.J. Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk. Nat. Commun. 2020;11(1):1–15. PubMed PMC

Aru J., Suzuki M., Larkum M.E. Cellular mechanisms of conscious processing. Trends Cognit. Sci. 2020;24:814–825. PubMed

Aru J., Siclari F., Phillips W.A., Storm J.F. Apical drive—a cellular mechanism of dreaming? Neurosci. Biobehav. Rev. 2020;119:440–455. PubMed

Aru J., Bachmann T., Suzuki M., et al. Primer on the dendritic integration theory of consciousness. PsyArXic. 2023 doi: 10.31234/osf.io/vkdt2. DOI

Bachmann T., Suzuki M., Aru J. Dendritic integration theory: a thalamo-cortical theory of state and content of consciousness. Philosophy and the Mind Sci. 2020;1 doi: 10.33735/phimisci.2020.II.52. DOI

Barron H.C., Auksztulewicz R., Friston K. Prediction and memory: a predictive coding account. Prog. Neurobiol. 2020;192 PubMed PMC

Bastos A.M., Usrey W.M., Adams R.A., et al. Canonical microcircuits for predictive coding. Neuron. 2012;76:695–711. PubMed PMC

Beaulieu-Laroche L., Toloza E.H.S., van der Goes M.-S., et al. Enhanced dendritic compartmentalization in human cortical neurons. Cell. 2018;175:643–651.e14. PubMed PMC

Boldog E., Bakken T.E., Hodge R.D., et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 2018;21:1185–1195. PubMed PMC

Brouillet D., Friston K. Relative fluency (unfelt vs felt) in active inference. Conscious. Cognit. 2023;115 doi: 10.1016/j.concog.2023.103579. PubMed DOI

Capone C., Lupo C., Muratore P., Paolucci P.S. Beyond spiking networks: the computational advantages of dendritic amplification and input segregation. Proc. Natl. Acad. Sci. U.S.A. 2023;120(49) doi: 10.1073/pnas.22207431202023. PubMed DOI PMC

Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 2013;36:181–204. PubMed

Clark A. The many faces of precision. (Replies to commentaries on “Whatever next? Neural prediction, situated agents, and the future of cognitive science”) Front. Psychol. 2013;4:270. PubMed PMC

Clark A. Consciousness as generative entanglement. J. Philos. 2019;116:645–662.

Dowdle L., Ghose G., Moeller S., et al. Characterizing top-down microcircuitry of complex human behavior across different levels of the visual hierarchy. bioRxiv. 2023 doi: 10.1101/2022.12.03.518973. DOI

Fiorillo C.D. Towards a general theory of neural computation based on prediction by single neurons. PLoS One. 2008;3(10) doi: 10.1371/journal.pone.0003298. PubMed DOI PMC

Fiorillo C.D. A neurocentric approach to Bayesian inference. Nat. Rev. Neurosci. 2010;11(8):605. PubMed

Fiorillo C.D. Beyond Bayes: on the need for a unified and Jaynesian definition of probability and information within neuroscience. Information. 2012;3(2):175–203.

Graham B.P., Kay J.W., Phillips W.A. Transfer functions for burst firing probability in a model neocortical pyramidal cell. bioRxiv. 2024 doi: 10.1101/2024.01.16.575982. 01.16. DOI

Harnett M.T., Magee J.C., Williams S.R. Distribution and function of HCN channels in the apical dendritic tuft of neocortical pyramidal neurons. J. Neurosci. 2015;35:1024–1037. PubMed PMC

Harris K.D., Shepherd G.M.G. The neocortical circuit: themes and variations. Nat. Neurosci. 2015;18:170–181. PubMed PMC

Heeger D.J., Mackey W.E. Oscillatory recurrent gated neural integrator circuits (ORGaNICs), a unifying t heoretical framework for neural dynamics. Proc. Natl. Acad. Sci. USA. 2019;116(45):22783–22794. PubMed PMC

Heeger D.J., Zemlianova K.O. A recurrent circuit implements normalization, simulating the dynamics of V1 activity. Proc. Natl. Acad. Sci. USA. 2020;117(36):22494–22505. PubMed PMC

Hertz J., Krogh A., Palmer R.G. Addison-Wesley; Redwood City, CA: 1991. Introduction to the Theory of Neural Computation.

Hobson J.A., Hong C.C.-H., Friston K. Virtual reality and consciousness inference in dreaming. Front. Psychol. 2014;5:1133. PubMed PMC

Hohwy J. Oxford University Press; Oxford, New York: 2013. The Predictive Mind.

Huang Y., Rao R.P.N. Predictive coding. Wiley Interdiscipl. Rev. Cogn. Sci. 2011;2:580–593. PubMed

Jadi J.P., Behabadi B.F., Poleg-Polsky A., Schiller J., Mel B.W. An augmented two-layer model captures nonlinear analog spatial integration effects in pyramidal neuron dendrites. Proc. IEEE. 2014;102:782–798. PubMed PMC

Johnson R.R., Burkhalter A. A polysynaptic feedback circuit in rat visual cortex. J. Neurosci. 1997;17:7129–7140. PubMed PMC

Kalmbach B.E., Buchin A., Long B., Close J., Nandi A., Miller J.A., Bakken T.E., Hodge R.D., Chong P., de Frates R., Dai K. h-Channels contribute to divergent intrinsic membrane properties of supragranular pyramidal neurons in human versus mouse cerebral cortex. Neuron. 2018;100(5):1194–1208. doi: 10.1016/j.neuron.2018.10.012. PubMed DOI PMC

Kanai R., Komura Y., Shipp S., et al. Cerebral hierarchies: predictive processing, precision and the pulvinar. Phil. Trans. Biol. Sci. 2015;370 PubMed PMC

Karnani M.M., Agetsuma M., Yuste R. A blanket of inhibition: functional inferences from dense inhibitory connectivity. Curr. Opin. Neurobiol. 2014;26:96–102. PubMed PMC

Kay J.W., Phillips W.A. Coherent Infomax as a computational goal for neural systems. Bull. Math. Biol. 2011;73:344–372. PubMed

Kay J.W., Phillips W.A. Contextual modulation in mammalian neocortex is asymmetric. Symmetry. 2020;12:815.

Kay J.W., Schulz J.M., Phillips W.A. A comparison of partial information decompositions using data from real and simulated layer 5b pyramidal cells. Entropy. 2022;24:1021. PubMed PMC

Khan A.G., Hofer S.B. Contextual signals in visual cortex. Curr. Opin. Neurobiol. 2018;52:131–138. PubMed

Körding K.P., König P. Learning with two sites of synaptic integration. Netw. Comput. Neural Syst. 2000;11:25–39. PubMed

Kuhn T.S. University of Chicago Press; Chicago: 1962. The Structure of Scientific Revolutions.

Kuhn T.S. In: Criticism and the Growth of Knowledge. Lakatos I., Musgrave A., editors. Cambridge University Press; Cambridge: 1970. Reflections on my critics; pp. 231–278.

Lamme V.A.F. In: The Visual Neurosciences. Werner J.S., Chalupa L.M., editors. MIT Press; Cambridge, MA: 2004. Beyond the classical receptive field: contextual modulation of V1 responses; pp. 720–732.

Lamme V.A.F. Visual functions generating conscious seeing. Front. Psychol. 2020;11:83. PubMed PMC

Larkum M.E. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 2013;36 doi: 10.1016/j.tins.2012.11.006. PubMed DOI

Larkum M.E., Phillips W.A. Does arousal enhance apical amplification and disamplification? Behav. Brain Sci. 2016;39 PubMed

Larkum M.E., Zhu J.J., Sakmann B. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature. 1999;398:338–341. PubMed

Lee T.S., Mumford D. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. Opt Image Sci. Vis. 2003;20:1434–1448. PubMed

Linsker R. Local synaptic learning rules suffice to maximize mutual information in a linear network. Neural Comput. 1992;4:691–702.

Linson A., Parr T., Friston K.J. Active inference, stressors, and psychological trauma: a neuroethological model of (mal)adaptive explore-exploit dynamics in ecological context. Behav. Brain Res. 2020;380 doi: 10.1016/j.bbr.2019.112421. PubMed DOI PMC

Litwin P., Miłkowski M. Unification by fiat: arrested development of predictive processing. Cognit. Sci. 2020;44 PubMed PMC

Markov N.T., Kennedy H. The importance of being hierarchical. Curr. Opin. Neurobiol. 2013;23:187–194. PubMed

Marvan T., Polák M., Bachmann T., et al. Apical amplification – a cellular mechanism of conscious perception? Neurosci. Consciousness. 2021;2021 PubMed PMC

Mikulasch F., Rudelt L., Wibral M., Priesemann V. Where is the error? Hierarchical predictive coding through dendritic error computation. Trends Neurosci. 2023;46(1):45–59. PubMed

Muckli L., Petro L., Abbatecola C., Adeel A., Bergmann J., Deperrois N., Destexhe A., Kriegeskorte N., Levelt C.N., Maass W., Morgan A.T., Papale P., Pennartz C.M.A., Peters B., Petrovici M., Phillips W.A., Roelfsema P.R., Sachdev R.N.S., Seignette K., Self M.W., Smith F.W., Storm J.F., Svanera M., Vanduffel W., Senn W., Larkum M.E. Vol. 1. 2023. The Cortical Microcircuitry of Predictions and Context – a Multi-Scale Perspective. (Zenodo). DOI

Mumford D. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol. Cybern. 1992;66:241–251. PubMed

Naud R., Friedenberger Z., Toth K. Silences, spikes and bursts: three-part knot of the neural code. bioRxiv. 2023 doi: 10.48550/arXiv.2302.07206. arXiv:2302.07206v1 [q-bio.NC] PubMed DOI

Ouden C den, Zhou A., Mepani V., et al. Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs. bioRxiv. 2023 doi: 10.1101/2023.04.05.535778. PubMed DOI

Pastorelli E., Yegenoglu A., Kolodziej N., Wybo W., Simula F., Diaz S., Storm J.F., Paolucci P.S. Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes. bioRxiv. 2023 doi: 10.48550/arXiv.2311.06074. arXiv:2311.06074 [q-bio.NC] DOI

Penny W. Bayesian models of brain and behaviour. Int. Sch. Res. Notices. 2012;2012

Phillips W.A. Cognitive functions of intracellular mechanisms for contextual amplification. Brain Cognit. 2017;112:39–53. PubMed

Phillips W.A., Bachmann T., Storm J.F. Apical function in neocortical pyramidal cells: a common pathway by which general anesthetics can affect mental state. Front. Neural Circ. 2018;12 doi: 10.3389/fncir.2018.00050. PubMed DOI PMC

Phillips W.A., Clark A., Silverstein S.M. On the functions, mechanisms, and malfunctions of intracortical contextual modulation. Neurosci. Biobehav. Rev. 2015;52:1–20. PubMed

Phillips W.A. Oxford University Press; Oxford, New York: 2023. The Cooperative Neuron: Cellular Foundations of Mental Life.

Pujol C.F., Blundon E.G., Dykstra A.R. Laminar specificity of the auditory perceptual awareness negativity: a biophysical modeling study. PLoS Comput. Biol. 2023;19 PubMed PMC

Ramaswamy S., Markram H. Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron. Front. Cell. Neurosci. 2015;9 doi: 10.3389/fncel.2015.00233. PubMed DOI PMC

Rao R.P.N., Ballard D.H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 1999;2:79–87. PubMed

Rigoli F., Michely J., Friston K.J., et al. The role of the hippocampus in weighting expectations during inference under uncertainty. Cortex. 2019;115:1–14. PubMed PMC

Roth M.M., Dahmen J.C., Muir D.R., et al. Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex. Nat. Neurosci. 2016;19:299–307. PubMed PMC

Schmid D., Jarvers C., Neumann H. Canonical circuit computations for computer vision. Biol. Cybern. 2023 doi: 10.1007/s00422-023-00966-9. PubMed DOI PMC

Sarwat S.G., Moraitis T., Wright C.D., Bhaskaran H. Chalcogenide optomemristors for multi-factor neuromorphic computation. Nat. Commun. 2022;13:2247. doi: 10.1038/s41467-022-29870-9. PubMed DOI PMC

Schulz J.M., Kay J.W., Bischofberger J., et al. GABAB receptor-mediated regulation of dendro-somatic synergy in layer 5 pyramidal neurons. Front. Cell. Neurosci. 2021;15 PubMed PMC

Schuman B., Dellal S., Prönneke A., et al. Neocortical layer 1: an elegant solution to top-down and bottom-up integration. Annu. Rev. Neurosci. 2021;44:221–252. PubMed PMC

Seth A. Faber; London: 2021. Being You.

Shai A.S., Anastassiou C.A., Larkum M.E., Koch C. Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: coincidence detection through bursting. PLoS Comput. Biol. 2015;11(3) doi: 10.1371/journal.pcbi.1004090. PubMed DOI PMC

Shipp S. Neural elements for predictive coding. Front. Psychol. 2016;7:1792. doi: 10.3389/fpsyg.2016.01792.eCollection2016. PubMed DOI PMC

Shipp S. Computational components of visual predictive coding circuitry. Front. Neural Circ. 2024;17 doi: 10.3389/fncir.2023.1254009.eCollection2023. PubMed DOI PMC

Shipp S., Friston K. In: The Cerebral Cortex and Thalamus. Usrey W.M., Sherman S.M., editors. Oxford University Press; New York: 2023. Predictive coding: forward and backward connectivity; pp. 436–445.

Siegel M., Körding K.P., König P. Integrating top-down and bottom-up sensory processing by somato-dendritic interactions. J. Comput. Neurosci. 2000;8:161–173. PubMed

Solomon S.S., Tang H., Sussman E., et al. Limited evidence for sensory prediction error responses in visual cortex of macaques and humans. Cerebr. Cortex. 2021;31:3136–3152. PubMed PMC

Spratling M.W. Predictive coding as a model of biased competition in visual attention. Vis. Res. 2008;48:1391–1408. PubMed

Spratling M.W. A review of predictive coding algorithms. Brain Cognit. 2017;112:92–97. PubMed

Spratling M.W. Fitting predictive coding to the neurophysiological data. Brain Res. 2019;1720 PubMed

Sprevak M. PhilSci Archive; 2021. Predictive coding I: Introduction.http://philsci-archive.pitt.edu/id/eprint/19365

Sun Z., Firestone C. The dark room problem. Trends Cognit. Sci. 2020;24(5):346–348. PubMed

Takahashi N., Oertner T.G., Hegemann P., et al. Active cortical dendrites modulate perception. Science. 2016;354:1587–1590. PubMed

Tantirigama M.L.S., Zolnik T., Judkewitz B., Larkum M.E., Sachdev R.N.S. Perspective on the multiple pathways to changing brain states. Front. Syst. Neurosci. 2020;14:23. PubMed PMC

van Versendaal D., Levelt C.N. Inhibitory interneurons in visual cortical plasticity. Cell. Mol. Life Sci. 2016;73:3677–3691. PubMed PMC

Walsh K.S., McGovern D.P., Clark A., et al. Evaluating the neurophysiological evidence for predictive processing as a model of perception. Ann. N. Y. Acad. Sci. 2020;1464:242–268. PubMed PMC

Wang X.-J., Yang G.R. A disinhibitory circuit motif and flexible information routing in the brain. Curr. Opin. Neurobiol. 2018;49:75–83. PubMed PMC

Wibral M., Lizier J.T., Priesemann V. Bits from brains for biologically inspired computing. Front. Robot AI. 2015;2 doi: 10.3389/frobt.2015.00005. DOI

Granato A, Phillips WA, Schulz J, Suzuki M, Larkum ME. Sumbitted. Cellular Mechanisms of Neurodevelopmental Learning Disabilities. PubMed

Williams PL, Beer RD. Nonnegative decomposition of multivariate information. arXiv:1004.2515 [cs.IT]..

Williams S.R., Fletcher L.N. A dendritic substrate for the cholinergic control of neocortical output neurons. Neuron. 2019;101:486–499.e4. PubMed

Wittgenstein L. Blackwell; Oxford: 1953. Philosophical Investigations.

Xu N., Harnett M.T., Williams S.R., et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature. 2012;492:247–251. PubMed

Zhang S., Xu M., Kamigaki T., Hoang Do J.P., Chang W.C., Jenvay S., Miyamichi K., Luo L., Dan Y. Long-range and local circuits for top-down modulation of visual cortex processing. Science. 2014;345(6197):660–665. PubMed PMC