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Storm JF, Klink PC, Aru J, Senn W, Goebel R, Pigorini A, Avanzini P, Vanduffel W, Roelfsema PR, Massimini M, Larkum ME, Pennartz CMA. An integrative, multiscale view on neural theories of consciousness. Neuron 2024; 112:1531-1552. [PMID: 38447578 DOI: 10.1016/j.neuron.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 03/08/2024]
Abstract
How is conscious experience related to material brain processes? A variety of theories aiming to answer this age-old question have emerged from the recent surge in consciousness research, and some are now hotly debated. Although most researchers have so far focused on the development and validation of their preferred theory in relative isolation, this article, written by a group of scientists representing different theories, takes an alternative approach. Noting that various theories often try to explain different aspects or mechanistic levels of consciousness, we argue that the theories do not necessarily contradict each other. Instead, several of them may converge on fundamental neuronal mechanisms and be partly compatible and complementary, so that multiple theories can simultaneously contribute to our understanding. Here, we consider unifying, integration-oriented approaches that have so far been largely neglected, seeking to combine valuable elements from various theories.
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Affiliation(s)
- Johan F Storm
- The Brain Signaling Group, Division of Physiology, IMB, Faculty of Medicine, University of Oslo, Domus Medica, Sognsvannsveien 9, Blindern, 0317 Oslo, Norway.
| | - P Christiaan Klink
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, 3584 CS Utrecht, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris 75012, France
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Walter Senn
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan 20122, Italy
| | - Pietro Avanzini
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, 43125 Parma, Italy
| | - Wim Vanduffel
- Department of Neurosciences, Laboratory of Neuro and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
| | - Pieter R Roelfsema
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut de la Vision, Paris 75012, France; Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands; Department of Neurosurgery, Academisch Medisch Centrum, Postbus 22660, 1100 DD Amsterdam, the Netherlands
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan 20157, Italy; Istituto di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan 20122, Italy; Azrieli Program in Brain, Mind and Consciousness, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5G 1M1, Canada
| | - Matthew E Larkum
- Institute of Biology, Humboldt University Berlin, Berlin, Germany; Neurocure Center for Excellence, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cyriel M A Pennartz
- Swammerdam Institute for Life Sciences, Center for Neuroscience, Faculty of Science, University of Amsterdam, Sciencepark 904, Amsterdam 1098 XH, the Netherlands; Research Priority Program Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
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Lackey EP, Moreira L, Norton A, Hemelt ME, Osorno T, Nguyen TM, Macosko EZ, Lee WCA, Hull CA, Regehr WG. Specialized connectivity of molecular layer interneuron subtypes leads to disinhibition and synchronous inhibition of cerebellar Purkinje cells. Neuron 2024:S0896-6273(24)00248-4. [PMID: 38692278 DOI: 10.1016/j.neuron.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/12/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024]
Abstract
Molecular layer interneurons (MLIs) account for approximately 80% of the inhibitory interneurons in the cerebellar cortex and are vital to cerebellar processing. MLIs are thought to primarily inhibit Purkinje cells (PCs) and suppress the plasticity of synapses onto PCs. MLIs also inhibit, and are electrically coupled to, other MLIs, but the functional significance of these connections is not known. Here, we find that two recently recognized MLI subtypes, MLI1 and MLI2, have a highly specialized connectivity that allows them to serve distinct functional roles. MLI1s primarily inhibit PCs, are electrically coupled to each other, fire synchronously with other MLI1s on the millisecond timescale in vivo, and synchronously pause PC firing. MLI2s are not electrically coupled, primarily inhibit MLI1s and disinhibit PCs, and are well suited to gating cerebellar-dependent behavior and learning. The synchronous firing of electrically coupled MLI1s and disinhibition provided by MLI2s require a major re-evaluation of cerebellar processing.
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Affiliation(s)
| | - Luis Moreira
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Aliya Norton
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Marie E Hemelt
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA
| | - Tomas Osorno
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Tri M Nguyen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Evan Z Macosko
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Stanley Center for Psychiatric Research, Cambridge, MA, USA
| | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA; Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Court A Hull
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA
| | - Wade G Regehr
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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De Filippo R, Schmitz D. Synthetic surprise as the foundation of the psychedelic experience. Neurosci Biobehav Rev 2024; 157:105538. [PMID: 38220035 PMCID: PMC10839673 DOI: 10.1016/j.neubiorev.2024.105538] [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] [Received: 09/18/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/16/2024]
Abstract
Psychedelic agents, such as LSD and psilocybin, induce marked alterations in consciousness via activation of the 5-HT2A receptor (5-HT2ARs). We hypothesize that psychedelics enforce a state of synthetic surprise through the biased activation of the 5-HTRs system. This idea is informed by recent insights into the role of 5-HT in signaling surprise. The effects on consciousness, explained by the cognitive penetrability of perception, can be described within the predictive coding framework where surprise corresponds to prediction error, the mismatch between predictions and actual sensory input. Crucially, the precision afforded to the prediction error determines its effect on priors, enabling a dynamic interaction between top-down expectations and incoming sensory data. By integrating recent findings on predictive coding circuitry and 5-HT2ARs transcriptomic data, we propose a biological implementation with emphasis on the role of inhibitory interneurons. Implications arise for the clinical use of psychedelics, which may rely primarily on their inherent capacity to induce surprise in order to disrupt maladaptive patterns.
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Affiliation(s)
- Roberto De Filippo
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, Neuroscience Research Center, 10117 Berlin, Germany.
| | - Dietmar Schmitz
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, Neuroscience Research Center, 10117 Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, Einstein Center for Neuroscience, 10117 Berlin, Germany; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, NeuroCure Cluster of Excellence, 10117 Berlin, Germany; Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience, Philippstr. 13, 10115 Berlin, Germany
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Brucklacher M, Bohté SM, Mejias JF, Pennartz CMA. Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception. Front Comput Neurosci 2023; 17:1207361. [PMID: 37818157 PMCID: PMC10561268 DOI: 10.3389/fncom.2023.1207361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/31/2023] [Indexed: 10/12/2023] Open
Abstract
The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: perceived objects must be mapped to high-level representations invariantly of the precise viewing conditions, and a generative model must be learned that allows, for instance, to fill in occluded information guided by visual experience. Here, we show how a multilayered predictive coding network can learn to recognize objects from the bottom up and to generate specific representations via a top-down pathway through a single learning rule: the local minimization of prediction errors. Trained on sequences of continuously transformed objects, neurons in the highest network area become tuned to object identity invariant of precise position, comparable to inferotemporal neurons in macaques. Drawing on this, the dynamic properties of invariant object representations reproduce experimentally observed hierarchies of timescales from low to high levels of the ventral processing stream. The predicted faster decorrelation of error-neuron activity compared to representation neurons is of relevance for the experimental search for neural correlates of prediction errors. Lastly, the generative capacity of the network is confirmed by reconstructing specific object images, robust to partial occlusion of the inputs. By learning invariance from temporal continuity within a generative model, the approach generalizes the predictive coding framework to dynamic inputs in a more biologically plausible way than self-supervised networks with non-local error-backpropagation. This was achieved simply by shifting the training paradigm to dynamic inputs, with little change in architecture and learning rule from static input-reconstructing Hebbian predictive coding networks.
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Affiliation(s)
- Matthias Brucklacher
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Sander M Bohté
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
- Machine Learning Group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands
| | - Jorge F Mejias
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Cyriel M A Pennartz
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
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