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Keller GB, Sterzer P. Predictive Processing: A Circuit Approach to Psychosis. Annu Rev Neurosci 2024; 47:85-101. [PMID: 38424472 DOI: 10.1146/annurev-neuro-100223-121214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Predictive processing is a computational framework that aims to explain how the brain processes sensory information by making predictions about the environment and minimizing prediction errors. It can also be used to explain some of the key symptoms of psychotic disorders such as schizophrenia. In recent years, substantial advances have been made in our understanding of the neuronal circuitry that underlies predictive processing in cortex. In this review, we summarize these findings and how they might relate to psychosis and to observed cell type-specific effects of antipsychotic drugs. We argue that quantifying the effects of antipsychotic drugs on specific neuronal circuit elements is a promising approach to understanding not only the mechanism of action of antipsychotic drugs but also psychosis. Finally, we outline some of the key experiments that should be done. The aims of this review are to provide an overview of the current circuit-based approaches to psychosis and to encourage further research in this direction.
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Affiliation(s)
- Georg B Keller
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
- Faculty of Natural Science, University of Basel, Basel, Switzerland
| | - Philipp Sterzer
- Department of Psychiatry, University of Basel, Basel, Switzerland
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2
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Marvan T, Phillips WA. Cellular mechanisms of cooperative context-sensitive predictive inference. CURRENT RESEARCH IN NEUROBIOLOGY 2024; 6:100129. [PMID: 38665363 PMCID: PMC11043869 DOI: 10.1016/j.crneur.2024.100129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 02/14/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
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.
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Affiliation(s)
- Tomáš Marvan
- Institute of Philosophy, Czech Academy of Sciences (CAS), Czech Republic
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3
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Suzuki M, Pennartz CMA, Aru J. How deep is the brain? The shallow brain hypothesis. Nat Rev Neurosci 2023; 24:778-791. [PMID: 37891398 DOI: 10.1038/s41583-023-00756-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today's dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.
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Affiliation(s)
- Mototaka Suzuki
- Department of Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Cyriel M A Pennartz
- Department of Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
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Tscshantz A, Millidge B, Seth AK, Buckley CL. Hybrid predictive coding: Inferring, fast and slow. PLoS Comput Biol 2023; 19:e1011280. [PMID: 37531366 PMCID: PMC10395865 DOI: 10.1371/journal.pcbi.1011280] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 06/20/2023] [Indexed: 08/04/2023] Open
Abstract
Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising "prediction errors"-the differences between predicted and observed data. Implicit in this proposal is the idea that successful perception requires multiple cycles of neural activity. This is at odds with evidence that several aspects of visual perception-including complex forms of object recognition-arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity. Here, we propose that the feedforward sweep can be understood as performing amortized inference (applying a learned function that maps directly from data to beliefs) and recurrent processing can be understood as performing iterative inference (sequentially updating neural activity in order to improve the accuracy of beliefs). We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner by describing both in terms of a dual optimization of a single objective function. We show that the resulting scheme can be implemented in a biologically plausible neural architecture that approximates Bayesian inference utilising local Hebbian update rules. We demonstrate that our hybrid predictive coding model combines the benefits of both amortized and iterative inference-obtaining rapid and computationally cheap perceptual inference for familiar data while maintaining the context-sensitivity, precision, and sample efficiency of iterative inference schemes. Moreover, we show how our model is inherently sensitive to its uncertainty and adaptively balances iterative and amortized inference to obtain accurate beliefs using minimum computational expense. Hybrid predictive coding offers a new perspective on the functional relevance of the feedforward and recurrent activity observed during visual perception and offers novel insights into distinct aspects of visual phenomenology.
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Affiliation(s)
- Alexander Tscshantz
- Sussex AI Group, Department of Informatics, University of Sussex, Brighton, United Kingdom
- VERSES Research Lab, Los Angeles, California, United States of America
- Sussex Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
| | - Beren Millidge
- Sussex AI Group, Department of Informatics, University of Sussex, Brighton, United Kingdom
- VERSES Research Lab, Los Angeles, California, United States of America
- Brain Networks Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Anil K. Seth
- Sussex AI Group, Department of Informatics, University of Sussex, Brighton, United Kingdom
- Sussex Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
| | - Christopher L. Buckley
- Sussex AI Group, Department of Informatics, University of Sussex, Brighton, United Kingdom
- VERSES Research Lab, Los Angeles, California, United States of America
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Vasilevskaya A, Widmer FC, Keller GB, Jordan R. Locomotion-induced gain of visual responses cannot explain visuomotor mismatch responses in layer 2/3 of primary visual cortex. Cell Rep 2023; 42:112096. [PMID: 36821437 PMCID: PMC9945359 DOI: 10.1016/j.celrep.2023.112096] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/27/2022] [Accepted: 01/26/2023] [Indexed: 02/24/2023] Open
Abstract
The aim of this work is to provide a comment on a recent paper by Muzzu and Saleem (2021), which claims that visuomotor mismatch responses in mouse visual cortex can be explained by a locomotion-induced gain of visual halt responses. Our primary concern is that without directly comparing these responses with mismatch responses, the claim that one response can explain the other appears difficult to uphold, more so because previous work finds that a uniform locomotion-induced gain cannot explain mismatch responses. To support these arguments, we analyze layer 2/3 calcium imaging datasets and show that coupling between visual flow and locomotion greatly enhances mismatch responses in an experience-dependent manner compared with halts in non-coupled visual flow. This is consistent with mismatch responses representing visuomotor prediction errors. Thus, we conclude that while feature selectivity might contribute to mismatch responses in mouse visual cortex, it cannot explain these responses.
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Affiliation(s)
- Anna Vasilevskaya
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland; Faculty of Science, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland
| | - Felix C Widmer
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland; Faculty of Science, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland
| | - Georg B Keller
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland; Faculty of Science, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland
| | - Rebecca Jordan
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.
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6
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Dual counterstream architecture may support separation between vision and predictions. Conscious Cogn 2022; 103:103375. [DOI: 10.1016/j.concog.2022.103375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/03/2021] [Accepted: 06/28/2022] [Indexed: 11/24/2022]
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Lee KM, Ferreira-Santos F, Satpute AB. Predictive processing models and affective neuroscience. Neurosci Biobehav Rev 2021; 131:211-228. [PMID: 34517035 PMCID: PMC9074371 DOI: 10.1016/j.neubiorev.2021.09.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 02/10/2021] [Accepted: 09/07/2021] [Indexed: 01/17/2023]
Abstract
The neural bases of affective experience remain elusive. Early neuroscience models of affect searched for specific brain regions that uniquely carried out the computations that underlie dimensions of valence and arousal. However, a growing body of work has failed to identify these circuits. Research turned to multivariate analyses, but these strategies, too, have made limited progress. Predictive processing models offer exciting new directions to address this problem. Here, we use predictive processing models as a lens to critique prevailing functional neuroimaging research practices in affective neuroscience. Our review highlights how much work relies on rigid assumptions that are inconsistent with a predictive processing approach. We outline the central aspects of a predictive processing model and draw out their implications for research in affective and cognitive neuroscience. Predictive models motivate a reformulation of "reverse inference" in cognitive neuroscience, and placing a greater emphasis on external validity in experimental design.
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Affiliation(s)
- Kent M Lee
- Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02118, USA.
| | - Fernando Ferreira-Santos
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Education Sciences, University of Porto, Portugal
| | - Ajay B Satpute
- Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02118, USA
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Rorot W. Bayesian theories of consciousness: a review in search for a minimal unifying model. Neurosci Conscious 2021; 2021:niab038. [PMID: 34650816 PMCID: PMC8512254 DOI: 10.1093/nc/niab038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 09/10/2021] [Accepted: 09/22/2021] [Indexed: 11/30/2022] Open
Abstract
The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience-a Minimal Unifying Model of consciousness.
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Affiliation(s)
- Wiktor Rorot
- Faculty of Philosophy and Faculty of Psychology, University of Warsaw, ul. Krakowskie Przedmieście 3, 00-927, Stawki 5/7, Warsaw 00-183, Poland
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Hertäg L, Sprekeler H. Learning prediction error neurons in a canonical interneuron circuit. eLife 2020; 9:e57541. [PMID: 32820723 PMCID: PMC7442488 DOI: 10.7554/elife.57541] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 07/28/2020] [Indexed: 11/13/2022] Open
Abstract
Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.
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Affiliation(s)
- Loreen Hertäg
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer Science, Berlin Institute of TechnologyBerlinGermany
- Bernstein Center for Computational NeuroscienceBerlinGermany
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Institute of Software Engineering and Theoretical Computer Science, Berlin Institute of TechnologyBerlinGermany
- Bernstein Center for Computational NeuroscienceBerlinGermany
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Abstract
This paper examines the applicability of predictive coding as an explanatory model for perception. This is carried out from two perspectives. First, the central assumptions of the model are re-examined in light of the neuroscientific evidence for the structure and functioning of key brain areas involved in perception. The inferential processes involved in predictive coding are then investigated in the context of ambiguous stimuli. This showed that while predictive coding may provide an accurate explanation for our perceptual experiences in some cases, there are also several instances where the picture is not as clear cut. Following on from this, particular emphasis is placed on ambiguous art in order to examine the psychological and cognitive implications of predictive coding in affective states. This not only sheds light on the impact of predictive coding for cognition and emotion, but also helps clarify the nature of ambiguous art.
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Affiliation(s)
- Jasper Wolf
- Arts and Sciences Department, UCL, Bloomsbury, London, United Kingdom.
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Walsh KS, McGovern DP, Clark A, O'Connell RG. Evaluating the neurophysiological evidence for predictive processing as a model of perception. Ann N Y Acad Sci 2020; 1464:242-268. [PMID: 32147856 PMCID: PMC7187369 DOI: 10.1111/nyas.14321] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 01/21/2020] [Accepted: 02/03/2020] [Indexed: 12/12/2022]
Abstract
For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long-standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.
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Affiliation(s)
- Kevin S. Walsh
- Trinity College Institute of Neuroscience and School of PsychologyTrinity College DublinDublinIreland
| | - David P. McGovern
- Trinity College Institute of Neuroscience and School of PsychologyTrinity College DublinDublinIreland
- School of PsychologyDublin City UniversityDublinIreland
| | - Andy Clark
- Department of PhilosophyUniversity of SussexBrightonUK
- Department of InformaticsUniversity of SussexBrightonUK
| | - Redmond G. O'Connell
- Trinity College Institute of Neuroscience and School of PsychologyTrinity College DublinDublinIreland
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