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Israely S, Ninou H, Rajchert O, Elmaleh L, Harel R, Mawase F, Kadmon J, Prut Y. Cerebellar output shapes cortical preparatory activity during motor adaptation. Nat Commun 2025; 16:2574. [PMID: 40089504 PMCID: PMC11910607 DOI: 10.1038/s41467-025-57832-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Accepted: 03/05/2025] [Indexed: 03/17/2025] Open
Abstract
The cerebellum plays a key role in motor adaptation by driving trial-to-trial recalibration of movements based on previous errors. In primates, cortical correlates of adaptation are encoded already in the pre-movement motor plan, but these early cortical signals could be driven by a cerebellar-to-cortical information flow or evolve independently through intracortical mechanisms. To address this question, we trained female macaque monkeys to reach against a viscous force field (FF) while blocking cerebellar outflow. The cerebellar block led to impaired FF adaptation and a compensatory, re-aiming-like shift in motor cortical preparatory activity. In the null-field conditions, the cerebellar block altered neural preparatory activity by increasing task-representation dimensionality and impeding generalization. A computational model indicated that low-dimensional (cerebellar-like) feedback is sufficient to replicate these findings. We conclude that cerebellar signals carry task structure information that constrains the dimensionality of the cortical preparatory manifold and promotes generalization. In the absence of these signals, cortical mechanisms are harnessed to partially restore adaptation.
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Affiliation(s)
- Sharon Israely
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, Israel
| | - Hugo Ninou
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, Israel
- Département D'Etudes Cognitives, Ecole Normale Supérieure, Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, PSL University, Paris, France
- Laboratoire de Physique de l'Ecole Normale Superieure, Ecole Normale Supérieure, PSL University, Paris, France
| | - Ori Rajchert
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Lee Elmaleh
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, Israel
| | - Ran Harel
- Department of Neurosurgery, Sheba Medical Center, Tel Aviv, Israel
| | - Firas Mawase
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Jonathan Kadmon
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, Israel.
| | - Yifat Prut
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, Israel.
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Koren V, Blanco Malerba S, Schwalger T, Panzeri S. Efficient coding in biophysically realistic excitatory-inhibitory spiking networks. eLife 2025; 13:RP99545. [PMID: 40053385 PMCID: PMC11888603 DOI: 10.7554/elife.99545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2025] Open
Abstract
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.
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Affiliation(s)
- Veronika Koren
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-EppendorfHamburgGermany
- Institute of Mathematics, Technische Universität BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
| | - Simone Blanco Malerba
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-EppendorfHamburgGermany
| | - Tilo Schwalger
- Institute of Mathematics, Technische Universität BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-EppendorfHamburgGermany
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Israely S, Ninou H, Rajchert O, Elmaleh L, Harel R, Mawase F, Kadmon J, Prut Y. Cerebellar output shapes cortical preparatory activity during motor adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.07.12.603354. [PMID: 40060411 PMCID: PMC11888169 DOI: 10.1101/2024.07.12.603354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/15/2025]
Abstract
The cerebellum plays a key role in motor adaptation by driving trial-to-trial recalibration of movements based on previous errors. In primates, cortical correlates of adaptation are encoded already in the pre-movement motor plan, but these early cortical signals could be driven by a cerebellar-to-cortical information flow or evolve independently through intracortical mechanisms. To address this question, we trained female macaque monkeys to reach against a viscous force field (FF) while blocking cerebellar outflow. The cerebellar block led to impaired FF adaptation and a compensatory, re-aiming-like shift in motor cortical preparatory activity. In the null-field conditions, the cerebellar block altered neural preparatory activity by increasing task-representation dimensionality and impeding generalization. A computational model indicated that low-dimensional (cerebellar-like) feedback is sufficient to replicate these findings. We conclude that cerebellar signals carry task structure information that constrains the dimensionality of the cortical preparatory manifold and promotes generalization. In the absence of these signals, cortical mechanisms are harnessed to partially restore adaptation.
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Affiliation(s)
- Sharon Israely
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, 91904-01, Israel
| | - Hugo Ninou
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, 91904-01, Israel
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
- Laboratoire de Physique de l’Ecole Normale Superieure, Ecole Normale Supérieure, PSL University, Paris, France
| | - Ori Rajchert
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Lee Elmaleh
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, 91904-01, Israel
| | - Ran Harel
- Department of Neurosurgery, Sheba Medical Center, 5262000 Tel Aviv, Israel
| | - Firas Mawase
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Jonathan Kadmon
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, 91904-01, Israel
| | - Yifat Prut
- The Edmond and Lily Safra Center For Brain Sciences, The Hebrew University, Jerusalem, 91904-01, Israel
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Safavi S, Chalk M, Logothetis NK, Levina A. Signatures of criticality in efficient coding networks. Proc Natl Acad Sci U S A 2024; 121:e2302730121. [PMID: 39352933 PMCID: PMC11474077 DOI: 10.1073/pnas.2302730121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/24/2024] [Indexed: 10/04/2024] Open
Abstract
The critical brain hypothesis states that the brain can benefit from operating close to a second-order phase transition. While it has been shown that several computational aspects of sensory processing (e.g., sensitivity to input) can be optimal in this regime, it is still unclear whether these computational benefits of criticality can be leveraged by neural systems performing behaviorally relevant computations. To address this question, we investigate signatures of criticality in networks optimized to perform efficient coding. We consider a spike-coding network of leaky integrate-and-fire neurons with synaptic transmission delays. Previously, it was shown that the performance of such networks varies nonmonotonically with the noise amplitude. Interestingly, we find that in the vicinity of the optimal noise level for efficient coding, the network dynamics exhibit some signatures of criticality, namely, scale-free dynamics of the spiking and the presence of crackling noise relation. Our work suggests that two influential, and previously disparate theories of neural processing optimization (efficient coding and criticality) may be intimately related.
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Affiliation(s)
- Shervin Safavi
- Computational Neuroscience, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden01307, Germany
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen72076, Germany
| | - Matthew Chalk
- Institut de la Vision, INSERM, CNRS, Sorbonne Université, Paris75014, France
| | - Nikos K. Logothetis
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen72076, Germany
- International Center for Primate Brain Research, Shanghai201602, China
| | - Anna Levina
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen72076, Germany
- Department of Computer Science, University of Tübingen, Tübingen72076, Germany
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen72076, Germany
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D'Agostino S, Moro F, Torchet T, Demirağ Y, Grenouillet L, Castellani N, Indiveri G, Vianello E, Payvand M. DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays. Nat Commun 2024; 15:3446. [PMID: 38658524 PMCID: PMC11043378 DOI: 10.1038/s41467-024-47764-w] [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: 12/14/2023] [Accepted: 04/11/2024] [Indexed: 04/26/2024] Open
Abstract
An increasing number of studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the neocortex for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection. This is accomplished thanks to coincidence detection (CD) mechanisms enabled by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for achieving spatio-temporal pattern recognition with pure feed-forward neural networks, without the need of resorting to recurrent architectures. In this work, we present "DenRAM", the first realization of a feed-forward spiking neural network with dendritic compartments, implemented using analog electronic circuits integrated into a 130 nm technology node and coupled with Resistive Random Access Memory (RRAM) technology. DenRAM's dendritic circuits use RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and by exploiting their heterogeneity we experimentally demonstrate DenRAM's ability to replicate synaptic delay profiles, and to efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, demonstrating DenRAM's resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, warrants high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.
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Affiliation(s)
- Simone D'Agostino
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- CEA-Leti, Université Grenoble Alpes, Grenoble, France
| | - Filippo Moro
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- CEA-Leti, Université Grenoble Alpes, Grenoble, France
| | - Tristan Torchet
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yiğit Demirağ
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
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Dezhina Z, Smallwood J, Xu T, Turkheimer FE, Moran RJ, Friston KJ, Leech R, Fagerholm ED. Establishing brain states in neuroimaging data. PLoS Comput Biol 2023; 19:e1011571. [PMID: 37844124 PMCID: PMC10602380 DOI: 10.1371/journal.pcbi.1011571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/26/2023] [Accepted: 10/04/2023] [Indexed: 10/18/2023] Open
Abstract
The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience-from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the 'state' of a system-i.e., a specification of the system's future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets.
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Affiliation(s)
- Zalina Dezhina
- Department of Neuroimaging, King’s College London, United Kingdom
| | | | - Ting Xu
- Child Mind Institute, New York, United States of America
| | | | - Rosalyn J. Moran
- Department of Neuroimaging, King’s College London, United Kingdom
| | | | - Robert Leech
- Department of Neuroimaging, King’s College London, United Kingdom
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