1
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Xia J, Jasper A, Kohn A, Miller KD. Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability. iScience 2024; 27:110512. [PMID: 39156642 PMCID: PMC11328009 DOI: 10.1016/j.isci.2024.110512] [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: 12/19/2023] [Revised: 04/01/2024] [Accepted: 07/12/2024] [Indexed: 08/20/2024] Open
Abstract
Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a multiplicative factor and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models and provide a parsimonious explanation for the stimulus dependence of correlated variability within and between visual areas.
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Affiliation(s)
- Ji Xia
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Anna Jasper
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenneth D. Miller
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027, USA
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2
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Lohse M, King AJ, Willmore BDB. Subcortical origin of nonlinear sound encoding in auditory cortex. Curr Biol 2024; 34:3405-3415.e5. [PMID: 39032492 DOI: 10.1016/j.cub.2024.06.057] [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: 02/02/2024] [Revised: 06/05/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024]
Abstract
A major challenge in neuroscience is to understand how neural representations of sensory information are transformed by the network of ascending and descending connections in each sensory system. By recording from neurons at several levels of the auditory pathway, we show that much of the nonlinear encoding of complex sounds in auditory cortex can be explained by transformations in the midbrain and thalamus. Modeling cortical neurons in terms of their inputs across these subcortical populations enables their responses to be predicted with unprecedented accuracy. By contrast, subcortical responses cannot be predicted from descending cortical inputs, indicating that ascending transformations are irreversible, resulting in increasingly lossy, higher-order representations across the auditory pathway. Rather, auditory cortex selectively modulates the nonlinear aspects of thalamic auditory responses and the functional coupling between subcortical neurons without affecting the linear encoding of sound. These findings reveal the fundamental role of subcortical transformations in shaping cortical responses.
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Affiliation(s)
- Michael Lohse
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London W1T 4JG, UK; Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK.
| | - Andrew J King
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK.
| | - Ben D B Willmore
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK.
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3
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Scott DN, Mukherjee A, Nassar MR, Halassa MM. Thalamocortical architectures for flexible cognition and efficient learning. Trends Cogn Sci 2024; 28:739-756. [PMID: 38886139 PMCID: PMC11305962 DOI: 10.1016/j.tics.2024.05.006] [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: 10/14/2023] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024]
Abstract
The brain exhibits a remarkable ability to learn and execute context-appropriate behaviors. How it achieves such flexibility, without sacrificing learning efficiency, is an important open question. Neuroscience, psychology, and engineering suggest that reusing and repurposing computations are part of the answer. Here, we review evidence that thalamocortical architectures may have evolved to facilitate these objectives of flexibility and efficiency by coordinating distributed computations. Recent work suggests that distributed prefrontal cortical networks compute with flexible codes, and that the mediodorsal thalamus provides regularization to promote efficient reuse. Thalamocortical interactions resemble hierarchical Bayesian computations, and their network implementation can be related to existing gating, synchronization, and hub theories of thalamic function. By reviewing recent findings and providing a novel synthesis, we highlight key research horizons integrating computation, cognition, and systems neuroscience.
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Affiliation(s)
- Daniel N Scott
- Department of Neuroscience, Brown University, Providence, RI, USA; Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| | - Arghya Mukherjee
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
| | - Matthew R Nassar
- Department of Neuroscience, Brown University, Providence, RI, USA; Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Michael M Halassa
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA; Department of Psychiatry, Tufts University School of Medicine, Boston, MA, USA.
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4
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Horrocks EAB, Rodrigues FR, Saleem AB. Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex. Nat Commun 2024; 15:6415. [PMID: 39080254 PMCID: PMC11289260 DOI: 10.1038/s41467-024-50563-y] [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: 12/11/2023] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.
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Affiliation(s)
- Edward A B Horrocks
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK.
| | - Fabio R Rodrigues
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK
| | - Aman B Saleem
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK.
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5
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Morales-Gregorio A, Kurth AC, Ito J, Kleinjohann A, Barthélemy FV, Brochier T, Grün S, van Albada SJ. Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region. Cell Rep 2024; 43:114371. [PMID: 38923458 DOI: 10.1016/j.celrep.2024.114371] [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: 09/29/2023] [Revised: 03/25/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
High-dimensional brain activity is often organized into lower-dimensional neural manifolds. However, the neural manifolds of the visual cortex remain understudied. Here, we study large-scale multi-electrode electrophysiological recordings of macaque (Macaca mulatta) areas V1, V4, and DP with a high spatiotemporal resolution. We find that the population activity of V1 contains two separate neural manifolds, which correlate strongly with eye closure (eyes open/closed) and have distinct dimensionalities. Moreover, we find strong top-down signals from V4 to V1, particularly to the foveal region of V1, which are significantly stronger during the eyes-open periods. Finally, in silico simulations of a balanced spiking neuron network qualitatively reproduce the experimental findings. Taken together, our analyses and simulations suggest that top-down signals modulate the population activity of V1. We postulate that the top-down modulation during the eyes-open periods prepares V1 for fast and efficient visual responses, resulting in a type of visual stand-by state.
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Affiliation(s)
- Aitor Morales-Gregorio
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany; Institute of Zoology, University of Cologne, Cologne, Germany.
| | - Anno C Kurth
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany; RWTH Aachen University, Aachen, Germany
| | - Junji Ito
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany
| | - Alexander Kleinjohann
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| | - Frédéric V Barthélemy
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany; Institut de Neurosciences de la Timone (INT), CNRS and Aix-Marseille Université, Marseille, France
| | - Thomas Brochier
- Institut de Neurosciences de la Timone (INT), CNRS and Aix-Marseille Université, Marseille, France
| | - Sonja Grün
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany; JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Sacha J van Albada
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany; Institute of Zoology, University of Cologne, Cologne, Germany
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6
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Tauste Campo A, Zainos A, Vázquez Y, Adell Segarra R, Álvarez M, Deco G, Díaz H, Parra S, Romo R, Rossi-Pool R. Thalamocortical interactions shape hierarchical neural variability during stimulus perception. iScience 2024; 27:110065. [PMID: 38993679 PMCID: PMC11237863 DOI: 10.1016/j.isci.2024.110065] [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: 03/25/2024] [Revised: 05/03/2024] [Accepted: 05/17/2024] [Indexed: 07/13/2024] Open
Abstract
The brain is organized hierarchically to process sensory signals. But, how do functional connections within and across areas contribute to this hierarchical order? We addressed this problem in the thalamocortical network, while monkeys detected vibrotactile stimulus. During this task, we quantified neural variability and directed functional connectivity in simultaneously recorded neurons sharing the cutaneous receptive field within and across VPL and areas 3b and 1. Before stimulus onset, VPL and area 3b exhibited similar fast dynamics while area 1 showed slower timescales. During the stimulus presence, inter-trial neural variability increased along the network VPL-3b-1 while VPL established two main feedforward pathways with areas 3b and 1 to process the stimulus. This lower variability of VPL and area 3b was found to regulate feedforward thalamocortical pathways. Instead, intra-cortical interactions were only anticipated by higher intrinsic timescales in area 1. Overall, our results provide evidence of hierarchical functional roles along the thalamocortical network.
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Affiliation(s)
- Adrià Tauste Campo
- Computational Biology and Complex Systems group, Department of Physics, Universitat Politècnica de Catalunya, Avinguda Dr. Marañón, 44-50, 08028 Barcelona, Catalonia, Spain
| | - Antonio Zainos
- Instituto de Fisiología Celular–Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Yuriria Vázquez
- Instituto de Fisiología Celular–Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Raul Adell Segarra
- Computational Biology and Complex Systems group, Department of Physics, Universitat Politècnica de Catalunya, Avinguda Dr. Marañón, 44-50, 08028 Barcelona, Catalonia, Spain
| | - Manuel Álvarez
- Instituto de Fisiología Celular–Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias I Fargas 25-27, 08005 Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Catalonia, Spain
| | - Héctor Díaz
- Instituto de Fisiología Celular–Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Sergio Parra
- Instituto de Fisiología Celular–Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | | | - Román Rossi-Pool
- Instituto de Fisiología Celular–Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
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7
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Spyropoulos G, Schneider M, van Kempen J, Gieselmann MA, Thiele A, Vinck M. Distinct feedforward and feedback pathways for cell-type specific attention effects. Neuron 2024; 112:2423-2434.e7. [PMID: 38759641 DOI: 10.1016/j.neuron.2024.04.020] [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: 04/19/2023] [Revised: 02/12/2024] [Accepted: 04/17/2024] [Indexed: 05/19/2024]
Abstract
Selective attention is thought to depend on enhanced firing activity in extrastriate areas. Theories suggest that this enhancement depends on selective inter-areal communication via gamma (30-80 Hz) phase-locking. To test this, we simultaneously recorded from different cell types and cortical layers of macaque V1 and V4. We find that while V1-V4 gamma phase-locking between local field potentials increases with attention, the V1 gamma rhythm does not engage V4 excitatory-neurons, but only fast-spiking interneurons in L4 of V4. By contrast, attention enhances V4 spike-rates in both excitatory and inhibitory cells, most strongly in L2/3. The rate increase in L2/3 of V4 precedes V1 in time. These findings suggest enhanced signal transmission with attention does not depend on inter-areal gamma phase-locking and show that the endogenous gamma rhythm has cell-type- and layer-specific effects on downstream target areas. Similar findings were made in the mouse visual system, based on opto-tagging of identified interneurons.
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Affiliation(s)
- Georgios Spyropoulos
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany
| | - Marius Schneider
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525 Nijmegen, the Netherlands
| | - Jochem van Kempen
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | | | - Alexander Thiele
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525 Nijmegen, the Netherlands.
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8
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Gupta D, Kopec CD, Bondy AG, Luo TZ, Elliott VA, Brody CD. A multi-region recurrent circuit for evidence accumulation in rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.602544. [PMID: 39026895 PMCID: PMC11257434 DOI: 10.1101/2024.07.08.602544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Decision-making based on noisy evidence requires accumulating evidence and categorizing it to form a choice. Here we evaluate a proposed feedforward and modular mapping of this process in rats: evidence accumulated in anterodorsal striatum (ADS) is categorized in prefrontal cortex (frontal orienting fields, FOF). Contrary to this, we show that both regions appear to be indistinguishable in their encoding/decoding of accumulator value and communicate this information bidirectionally. Consistent with a role for FOF in accumulation, silencing FOF to ADS projections impacted behavior throughout the accumulation period, even while nonselective FOF silencing did not. We synthesize these findings into a multi-region recurrent neural network trained with a novel approach. In-silico experiments reveal that multiple scales of recurrence in the cortico-striatal circuit rescue computation upon nonselective FOF perturbations. These results suggest that ADS and FOF accumulate evidence in a recurrent and distributed manner, yielding redundant representations and robustness to certain perturbations.
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Affiliation(s)
- Diksha Gupta
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA
- Present address: Sainsbury Wellcome Centre, University College London, London, UK
| | - Charles D. Kopec
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA
| | - Adrian G. Bondy
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA
| | - Thomas Z. Luo
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA
| | - Verity A. Elliott
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA
| | - Carlos D. Brody
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA
- Howard Hughes Medical Institute, Princeton University, Princeton NJ, USA
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9
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Hirabayashi T, Nagai Y, Hori Y, Hori Y, Oyama K, Mimura K, Miyakawa N, Iwaoki H, Inoue KI, Suhara T, Takada M, Higuchi M, Minamimoto T. Multiscale chemogenetic dissection of fronto-temporal top-down regulation for object memory in primates. Nat Commun 2024; 15:5369. [PMID: 38987235 PMCID: PMC11237144 DOI: 10.1038/s41467-024-49570-w] [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: 11/17/2023] [Accepted: 06/07/2024] [Indexed: 07/12/2024] Open
Abstract
Visual object memory is a fundamental element of various cognitive abilities, and the underlying neural mechanisms have been extensively examined especially in the anterior temporal cortex of primates. However, both macroscopic large-scale functional network in which this region is embedded and microscopic neuron-level dynamics of top-down regulation it receives for object memory remains elusive. Here, we identified the orbitofrontal node as a critical partner of the anterior temporal node for object memory by combining whole-brain functional imaging during rest and a short-term object memory task in male macaques. Focal chemogenetic silencing of the identified orbitofrontal node downregulated both the local orbitofrontal and remote anterior temporal nodes during the task, in association with deteriorated mnemonic, but not perceptual, performance. Furthermore, imaging-guided neuronal recordings in the same monkeys during the same task causally revealed that orbitofrontal top-down modulation enhanced stimulus-selective mnemonic signal in individual anterior temporal neurons while leaving bottom-up perceptual signal unchanged. Furthermore, similar activity difference was also observed between correct and mnemonic error trials before silencing, suggesting its behavioral relevance. These multifaceted but convergent results provide a multiscale causal understanding of dynamic top-down regulation of the anterior temporal cortex along the ventral fronto-temporal network underpinning short-term object memory in primates.
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Affiliation(s)
- Toshiyuki Hirabayashi
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan.
| | - Yuji Nagai
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Yuki Hori
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Yukiko Hori
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Kei Oyama
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Koki Mimura
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Naohisa Miyakawa
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Haruhiko Iwaoki
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Ken-Ichi Inoue
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi, 484-8506, Japan
| | - Tetsuya Suhara
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Masahiko Takada
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi, 484-8506, Japan
| | - Makoto Higuchi
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
| | - Takafumi Minamimoto
- Advanced Neuroimaging Center, National Institutes for Quantum Science and Technology, Chiba, 263-8555, Japan
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10
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Zhang Y, Zhang X, Lu X, Chen N. Attention spotlight in V1-based cortico-cortical interactions in human visual hierarchy. Sci Rep 2024; 14:13140. [PMID: 38849423 PMCID: PMC11161588 DOI: 10.1038/s41598-024-63817-y] [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: 09/07/2023] [Accepted: 06/03/2024] [Indexed: 06/09/2024] Open
Abstract
Attention is often viewed as a mental spotlight, which can be scaled like a zoom lens at specific spatial locations and features a center-surround gradient. Here, we demonstrate a neural signature of attention spotlight in signal transmission along the visual hierarchy. fMRI background connectivity analysis was performed between retinotopic V1 and downstream areas to characterize the spatial distribution of inter-areal interaction under two attentional states. We found that, compared to diffused attention, focal attention sharpened the spatial gradient in the strength of the background connectivity. Dynamic causal modeling analysis further revealed the effect of attention in both the feedback and feedforward connectivity between V1 and extrastriate cortex. In a context which induced a strong effect of crowding, the effect of attention in the background connectivity profile diminished. Our findings reveal a context-dependent attention prioritization in information transmission via modulating the recurrent processing across the early stages in human visual cortex.
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Affiliation(s)
- Yanyu Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xilin Zhang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, 510631, Guangdong, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, Guangdong, China
| | - Xincheng Lu
- Department of psychological and cognitive sciences, Tsinghua University, Beijing, China
| | - Nihong Chen
- Department of psychological and cognitive sciences, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, China.
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11
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Abstract
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or 'iconic' memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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Affiliation(s)
- Ivan Tomić
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
- Department of Psychology, Faculty of Humanities and Social Sciences, University of ZagrebZagrebCroatia
| | - Paul M Bays
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
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12
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Dijkstra N. Uncovering the Role of the Early Visual Cortex in Visual Mental Imagery. Vision (Basel) 2024; 8:29. [PMID: 38804350 PMCID: PMC11130976 DOI: 10.3390/vision8020029] [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: 02/26/2024] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/29/2024] Open
Abstract
The question of whether the early visual cortex (EVC) is involved in visual mental imagery remains a topic of debate. In this paper, I propose that the inconsistency in findings can be explained by the unique challenges associated with investigating EVC activity during imagery. During perception, the EVC processes low-level features, which means that activity is highly sensitive to variation in visual details. If the EVC has the same role during visual mental imagery, any change in the visual details of the mental image would lead to corresponding changes in EVC activity. Within this context, the question should not be whether the EVC is 'active' during imagery but how its activity relates to specific imagery properties. Studies using methods that are sensitive to variation in low-level features reveal that imagery can recruit the EVC in similar ways as perception. However, not all mental images contain a high level of visual details. Therefore, I end by considering a more nuanced view, which states that imagery can recruit the EVC, but that does not mean that it always does so.
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Affiliation(s)
- Nadine Dijkstra
- Department of Imaging Neuroscience, Institute of Neurology, University College London, London WC1E 6BT, UK
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13
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Johnston R, Smith MA. Brain-wide arousal signals are segregated from movement planning in the superior colliculus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591284. [PMID: 38746466 PMCID: PMC11092505 DOI: 10.1101/2024.04.26.591284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The superior colliculus (SC) is traditionally considered a brain region that functions as an interface between processing visual inputs and generating eye movement outputs. Although its role as a primary reflex center is thought to be conserved across vertebrate species, evidence suggests that the SC has evolved to support higher-order cognitive functions including spatial attention. When it comes to oculomotor areas such as the SC, it is critical that high precision fixation and eye movements are maintained even in the presence of signals related to ongoing changes in cognition and brain state, both of which have the potential to interfere with eye position encoding and movement generation. In this study, we recorded spiking responses of neuronal populations in the SC while monkeys performed a memory-guided saccade task and found that the activity of some of the neurons fluctuated over tens of minutes. By leveraging the statistical power afforded by high-dimensional neuronal recordings, we were able to identify a low-dimensional pattern of activity that was correlated with the subjects' arousal levels. Importantly, we found that the spiking responses of deep-layer SC neurons were less correlated with this brain-wide arousal signal, and that neural activity associated with changes in pupil size and saccade tuning did not overlap in population activity space with movement initiation signals. Taken together, these findings provide a framework for understanding how signals related to cognition and arousal can be embedded in the population activity of oculomotor structures without compromising the fidelity of the motor output.
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Affiliation(s)
- Richard Johnston
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, USA
| | - Matthew A. Smith
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, USA
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14
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Lewis CM, Wunderle T, Fries P. Top-down modulation of visual cortical stimulus encoding and gamma independent of firing rates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.589006. [PMID: 38645050 PMCID: PMC11030389 DOI: 10.1101/2024.04.11.589006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Neurons in primary visual cortex integrate sensory input with signals reflecting the animal's internal state to support flexible behavior. Internal variables, such as expectation, attention, or current goals, are imposed in a top-down manner via extensive feedback projections from higher-order areas. We optogenetically activated a high-order visual area, area 21a, in the lightly anesthetized cat (OptoTD), while recording from neuronal populations in V1. OptoTD induced strong, up to several fold, changes in gamma-band synchronization together with much smaller changes in firing rate, and the two effects showed no correlation. OptoTD effects showed specificity for the features of the simultaneously presented visual stimuli. OptoTD-induced changes in gamma synchronization, but not firing rates, were predictive of simultaneous changes in the amount of encoded stimulus information. Our findings suggest that one important role of top-down signals is to modulate synchronization and the information encoded by populations of sensory neurons.
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15
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Anisetti VR, Kandala A, Scellier B, Schwarz JM. Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks. Neural Comput 2024; 36:596-620. [PMID: 38457749 DOI: 10.1162/neco_a_01648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/20/2023] [Indexed: 03/10/2024]
Abstract
We introduce frequency propagation, a learning algorithm for nonlinear physical networks. In a resistive electrical circuit with variable resistors, an activation current is applied at a set of input nodes at one frequency and an error current is applied at a set of output nodes at another frequency. The voltage response of the circuit to these boundary currents is the superposition of an activation signal and an error signal whose coefficients can be read in different frequencies of the frequency domain. Each conductance is updated proportionally to the product of the two coefficients. The learning rule is local and proved to perform gradient descent on a loss function. We argue that frequency propagation is an instance of a multimechanism learning strategy for physical networks, be it resistive, elastic, or flow networks. Multimechanism learning strategies incorporate at least two physical quantities, potentially governed by independent physical mechanisms, to act as activation and error signals in the training process. Locally available information about these two signals is then used to update the trainable parameters to perform gradient descent. We demonstrate how earlier work implementing learning via chemical signaling in flow networks (Anisetti, Scellier, et al., 2023) also falls under the rubric of multimechanism learning.
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Affiliation(s)
| | - Ananth Kandala
- Department of Physics, University of Florida, Gainesville, FL 32611, U.S.A.
| | | | - J M Schwarz
- Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A
- Indian Creek Farm, Ithaca, NY 14850, U.S.A.
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16
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Contemori G, Oletto CM, Battaglini L, Bertamini M. On the relationship between foveal mask interference and mental imagery in peripheral object recognition. Proc Biol Sci 2024; 291:20232867. [PMID: 38471562 DOI: 10.1098/rspb.2023.2867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/02/2024] [Indexed: 03/14/2024] Open
Abstract
A delayed foveal mask affects perception of peripheral stimuli. The effect is determined by the timing of the mask and by the similarity with the peripheral stimulus. A congruent mask enhances performance, while an incongruent one impairs it. It is hypothesized that foveal masks disrupt a feedback mechanism reaching the foveal cortex. This mechanism could be part of a broader circuit associated with mental imagery, but this hypothesis has not as yet been tested. We investigated the link between mental imagery and foveal feedback. We tested the relationship between performance fluctuations caused by the foveal mask-measured in terms of discriminability (d') and criterion (C)-and the scores from two questionnaires designed to assess mental imagery vividness (VVIQ) and another exploring object imagery, spatial imagery and verbal cognitive styles (OSIVQ). Contrary to our hypotheses, no significant correlations were found between VVIQ and the mask's impact on d' and C. Neither the object nor spatial subscales of OSIVQ correlated with the mask's impact. In conclusion, our findings do not substantiate the existence of a link between foveal feedback and mental imagery. Further investigation is needed to determine whether mask interference might occur with more implicit measures of imagery.
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Affiliation(s)
- Giulio Contemori
- Department of General Psychology, University of Padova, Padova, Italy
| | | | - Luca Battaglini
- Department of General Psychology, University of Padova, Padova, Italy
| | - Marco Bertamini
- Department of General Psychology, University of Padova, Padova, Italy
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17
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Kanayama H, Tominaga T, Tominaga Y, Kato N, Yoshimura H. Action of GABAB receptor on local network oscillation in somatosensory cortex of oral part: focusing on NMDA receptor. J Physiol Sci 2024; 74:16. [PMID: 38475711 DOI: 10.1186/s12576-024-00911-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/01/2024] [Indexed: 03/14/2024]
Abstract
The balance of activity between glutamatergic and GABAergic networks is particularly important for oscillatory neural activities in the brain. Here, we investigated the roles of GABAB receptors in network oscillation in the oral somatosensory cortex (OSC), focusing on NMDA receptors. Neural oscillation at the frequency of 8-10 Hz was elicited in rat brain slices after caffeine application. Oscillations comprised a non-NMDA receptor-dependent initial phase and a later NMDA receptor-dependent oscillatory phase, with the oscillator located in the upper layer of the OSC. Baclofen was applied to investigate the actions of GABAB receptors. The later NMDA receptor-dependent oscillatory phase completely disappeared, but the initial phase did not. These results suggest that GABAB receptors mainly act on NMDA receptor, in which metabotropic actions of GABAB receptors may contribute to the attenuation of NMDA receptor activities. A regulatory system for network oscillation involving GABAB receptors may be present in the OSC.
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Affiliation(s)
- Hiroyuki Kanayama
- Department of Molecular Oral Physiology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto, Tokushima, 770-8504, Japan
- Department of Oral and Maxillofacial Surgery, National Hospital Organization Osaka National Hospital, Osaka, 540-0006, Japan
| | - Takashi Tominaga
- Institute of Neuroscience, Tokushima Bunri University, Shido, Kagawa, 769-2123, Japan
| | - Yoko Tominaga
- Institute of Neuroscience, Tokushima Bunri University, Shido, Kagawa, 769-2123, Japan
| | - Nobuo Kato
- Department of Physiology, Kanazawa Medical University, Uchinada-Cho, Ishikawa, 920-0293, Japan
| | - Hiroshi Yoshimura
- Department of Molecular Oral Physiology, Institute of Biomedical Sciences, Tokushima University Graduate School, 3-18-15 Kuramoto, Tokushima, 770-8504, Japan.
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18
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Lazar A, Klein L, Klon-Lipok J, Bányai M, Orbán G, Singer W. Paying attention to natural scenes in area V1. iScience 2024; 27:108816. [PMID: 38323011 PMCID: PMC10844823 DOI: 10.1016/j.isci.2024.108816] [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: 03/09/2023] [Revised: 07/18/2023] [Accepted: 01/02/2024] [Indexed: 02/08/2024] Open
Abstract
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. We hypothesized that attentional and contextual signals interact in V1 in a manner that primarily benefits the representation of natural stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes, but not by synthetic stimuli lacking higher-order statistical regularities. Population analysis revealed that neuronal responses converged to a low-dimensional subspace only for natural stimuli. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the natural-scene subspace, indicating an alignment between the attentional and natural stimulus variance. These results suggest that attentional and contextual signals interact in V1 in a manner optimized for natural vision.
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Affiliation(s)
- Andreea Lazar
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
| | - Liane Klein
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
| | - Johanna Klon-Lipok
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
| | - Mihály Bányai
- HUN-REN Wigner Research Center for Physics, Budapest, Hungary
| | - Gergő Orbán
- HUN-REN Wigner Research Center for Physics, Budapest, Hungary
| | - Wolf Singer
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
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19
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Han S, Helmchen F. Behavior-relevant top-down cross-modal predictions in mouse neocortex. Nat Neurosci 2024; 27:298-308. [PMID: 38177341 DOI: 10.1038/s41593-023-01534-x] [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: 03/07/2023] [Accepted: 11/27/2023] [Indexed: 01/06/2024]
Abstract
Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone-texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.
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Affiliation(s)
- Shuting Han
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
| | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland.
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland.
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20
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Weng G, Clark K, Akbarian A, Noudoost B, Nategh N. Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas. Front Comput Neurosci 2024; 18:1273053. [PMID: 38348287 PMCID: PMC10859875 DOI: 10.3389/fncom.2024.1273053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 01/09/2024] [Indexed: 02/15/2024] Open
Abstract
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors' contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
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Affiliation(s)
- Geyu Weng
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Kelsey Clark
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Amir Akbarian
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Neda Nategh
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States
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21
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Hedrick M, Thornton K. Reaction time for correct identification of vowels in consonant-vowel syllables and of vowel segments. JASA EXPRESS LETTERS 2024; 4:015205. [PMID: 38214609 DOI: 10.1121/10.0024334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024]
Abstract
Reaction times for correct vowel identification were measured to determine the effects of intertrial intervals, vowel, and cue type. Thirteen adults with normal hearing, aged 20-38 years old, participated. Stimuli included three naturally produced syllables (/ba/ /bi/ /bu/) presented whole or segmented to isolate the formant transition or static formant center. Participants identified the vowel presented via loudspeaker by mouse click. Results showed a significant effect of intertrial intervals, no significant effect of cue type, and a significant vowel effect-suggesting that feedback occurs, vowel identification may depend on cue duration, and vowel bias may stem from focal structure.
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Affiliation(s)
- Mark Hedrick
- Department of Audiology and Speech Pathology, The University of Tennessee Health Science Center, Knoxville, Tennessee 37996, USA
| | - Kristen Thornton
- Department of Hearing, Speech, and Language Sciences, Gallaudet University, Washington, DC 20002, ,
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22
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Tang Y, Gervais C, Moffitt R, Nareddula S, Zimmermann M, Nadew YY, Quinn CJ, Saldarriaga V, Edens P, Chubykin AA. Visual experience induces 4-8 Hz synchrony between V1 and higher-order visual areas. Cell Rep 2023; 42:113482. [PMID: 37999977 PMCID: PMC10790627 DOI: 10.1016/j.celrep.2023.113482] [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: 04/04/2023] [Revised: 09/20/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Visual perceptual experience induces persistent 4-8 Hz oscillations in the mouse primary visual cortex (V1), encoding visual familiarity. Recent studies suggest that higher-order visual areas (HVAs) are functionally specialized and segregated into information streams processing distinct visual features. However, whether visual memories are processed and stored within the distinct streams is not understood. We report here that V1 and lateromedial (LM), but not V1 and anterolateral, become more phase synchronized in 4-8 Hz after the entrainment of visual stimulus that maximally induces responses in LM. Directed information analysis reveals changes in the top-down functional connectivity between V1 and HVAs. Optogenetic inactivation of LM reduces post-stimulus oscillation peaks in V1 and impairs visual discrimination behavior. Our results demonstrate that 4-8 Hz familiarity-evoked oscillations are specific for the distinct visual features and are present in the corresponding HVAs, where they may be used for the inter-areal communication with V1 during memory-related behaviors.
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Affiliation(s)
- Yu Tang
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Catherine Gervais
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rylann Moffitt
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Sanghamitra Nareddula
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Michael Zimmermann
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Yididiya Y Nadew
- Department of Computer Sciences, Iowa State University, Ames, IA 50011, USA
| | | | - Violeta Saldarriaga
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Paige Edens
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Alexander A Chubykin
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA.
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23
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Rockland KS. Cellular and laminar architecture: A short history and commentary. J Comp Neurol 2023; 531:1926-1933. [PMID: 37941081 DOI: 10.1002/cne.25553] [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: 02/08/2023] [Revised: 08/11/2023] [Accepted: 10/13/2023] [Indexed: 11/10/2023]
Abstract
The feedforward/feedback classification, as originally stated in relation to early visual areas in the macaque monkey, has had a significant influence on ideas of laminar interactions, area reciprocity, and cortical hierarchical organization. In some contrast with this macroscale "laminar connectomics," a more cellular approach to cortical connections, as briefly surveyed here, points to a still underappreciated heterogeneity of neuronal subtypes and complex microcircuitries. From the perspective of heterogeneities, the question of how brain regions interact and influence each other quickly leads to discussions about concurrent hierarchical and nonhierarchical cortical features, brain organization as a multiscale system forming nested groups and hierarchies, connectomes annotated by multiple biological attributes, and interleaved and overlapping scales of organization.
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Affiliation(s)
- Kathleen S Rockland
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
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24
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Toosi T, Issa EB. Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment. ARXIV 2023:arXiv:2310.20599v1. [PMID: 37961740 PMCID: PMC10635293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
In natural vision, feedback connections support versatile visual inference capabilities such as making sense of the occluded or noisy bottom-up sensory information or mediating pure top-down processes such as imagination. However, the mechanisms by which the feedback pathway learns to give rise to these capabilities flexibly are not clear. We propose that top-down effects emerge through alignment between feedforward and feedback pathways, each optimizing its own objectives. To achieve this co-optimization, we introduce Feedback-Feedforward Alignment (FFA), a learning algorithm that leverages feedback and feedforward pathways as mutual credit assignment computational graphs, enabling alignment. In our study, we demonstrate the effectiveness of FFA in co-optimizing classification and reconstruction tasks on widely used MNIST and CIFAR10 datasets. Notably, the alignment mechanism in FFA endows feedback connections with emergent visual inference functions, including denoising, resolving occlusions, hallucination, and imagination. Moreover, FFA offers bio-plausibility compared to traditional back-propagation (BP) methods in implementation. By repurposing the computational graph of credit assignment into a goal-driven feedback pathway, FFA alleviates weight transport problems encountered in BP, enhancing the bio-plausibility of the learning algorithm. Our study presents FFA as a promising proof-of-concept for the mechanisms underlying how feedback connections in the visual cortex support flexible visual functions. This work also contributes to the broader field of visual inference underlying perceptual phenomena and has implications for developing more biologically inspired learning algorithms.
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Affiliation(s)
- Tahereh Toosi
- Center for Theoretical Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
| | - Elias B. Issa
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
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25
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Barbosa J, Proville R, Rodgers CC, DeWeese MR, Ostojic S, Boubenec Y. Early selection of task-relevant features through population gating. Nat Commun 2023; 14:6837. [PMID: 37884507 PMCID: PMC10603060 DOI: 10.1038/s41467-023-42519-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.
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Affiliation(s)
- Joao Barbosa
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France.
| | - Rémi Proville
- Tailored Data Solutions, 192 Cours Gambetta, 84300, Cavaillon, France
| | - Chris C Rodgers
- Department of Neurosurgery, Emory University, Atlanta, GA, 30033, USA
| | - Michael R DeWeese
- Department of Physics, Helen Wills Neuroscience Institute, and Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France
| | - Yves Boubenec
- Laboratoire des Systèmes Perceptifs, Département d'Études Cognitives, École Normale Supérieure PSL Research University, CNRS, Paris, France
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26
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Jiang Y, He S, Zhang J. Different roles of response covariability and its attentional modulation in the sensory cortex and posterior parietal cortex. Proc Natl Acad Sci U S A 2023; 120:e2216942120. [PMID: 37812698 PMCID: PMC10589615 DOI: 10.1073/pnas.2216942120] [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: 10/04/2022] [Accepted: 08/16/2023] [Indexed: 10/11/2023] Open
Abstract
The covariability of neural responses in the neuron population is highly relevant to the information encoding. Cognitive processes, such as attention, are found to modulate the covariability in the visual cortex to improve information encoding, suggesting the computational advantage of covariability modulation in the neural system. However, is the covariability modulation a general mechanism for enhanced information encoding throughout the information processing pathway, or only adopted in certain processing stages, depending on the property of neural representation? Here, with ultrahigh-field MRI, we examined the covariability, which was estimated by noise correlation, in different attention states in the early visual cortex and posterior parietal cortex (PPC) of the human brain, and its relationship to the quality of information encoding. Our results showed that while attention decreased the covariability to improve the stimulus encoding in the early visual cortex, covariability modulation was not observed in the PPC, where covariability had little impact on information encoding. Further, attention promoted the information flow between the early visual cortex and PPC, with an apparent emphasis on a flow from high- to low-dimensional representations, suggesting the existence of a reduction in the dimensionality of neural representation from the early visual cortex to PPC. Finally, the neural response patterns in the PPC could predict the amplitudes of covariability change in the early visual cortex, indicating a top-down control from the PPC to early visual cortex. Our findings reveal the specific roles of the sensory cortex and PPC during attentional modulation of covariability, determined by the complexity and fidelity of the neural representation in each cortical region.
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Affiliation(s)
- Yong Jiang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing100101, China
| | - Sheng He
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing100101, China
- Institute of AI, Hefei Comprehensive National Science Center, Hefei230088, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Jiedong Zhang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing100101, China
- University of Chinese Academy of Sciences, Beijing100049, China
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Borgomaneri S, Zanon M, Di Luzio P, Cataneo A, Arcara G, Romei V, Tamietto M, Avenanti A. Increasing associative plasticity in temporo-occipital back-projections improves visual perception of emotions. Nat Commun 2023; 14:5720. [PMID: 37737239 PMCID: PMC10517146 DOI: 10.1038/s41467-023-41058-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 08/17/2023] [Indexed: 09/23/2023] Open
Abstract
The posterior superior temporal sulcus (pSTS) is a critical node in a network specialized for perceiving emotional facial expressions that is reciprocally connected with early visual cortices (V1/V2). Current models of perceptual decision-making increasingly assign relevance to recursive processing for visual recognition. However, it is unknown whether inducing plasticity into reentrant connections from pSTS to V1/V2 impacts emotion perception. Using a combination of electrophysiological and neurostimulation methods, we demonstrate that strengthening the connectivity from pSTS to V1/V2 selectively increases the ability to perceive facial expressions associated with emotions. This behavior is associated with increased electrophysiological activity in both these brain regions, particularly in V1/V2, and depends on specific temporal parameters of stimulation that follow Hebbian principles. Therefore, we provide evidence that pSTS-to-V1/V2 back-projections are instrumental to perception of emotion from facial stimuli and functionally malleable via manipulation of associative plasticity.
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Affiliation(s)
- Sara Borgomaneri
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Cesena Campus, Cesena, Italy.
| | - Marco Zanon
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Cesena Campus, Cesena, Italy
- Neuroscience Area, International School for Advanced Studies (SISSA), Trieste, Italy
| | - Paolo Di Luzio
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Cesena Campus, Cesena, Italy
| | - Antonio Cataneo
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Cesena Campus, Cesena, Italy
| | | | - Vincenzo Romei
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Cesena Campus, Cesena, Italy
- Facultad de Lenguas y Educación, Universidad Antonio de Nebrija, Madrid, 28015, Spain
| | - Marco Tamietto
- Dipartimento di Psicologia, Università degli Studi di Torino, Torino, Italy.
- Department of Medical and Clinical Psychology, Tilburg University, Tilburg, The Netherlands.
| | - Alessio Avenanti
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Alma Mater Studiorum Università di Bologna, Cesena Campus, Cesena, Italy.
- Centro de Investigación en Neuropsicología y Neurociencias Cognitivas, Universidad Católica del Maule, Talca, Chile.
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28
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Kass RE, Bong H, Olarinre M, Xin Q, Urban KN. Identification of interacting neural populations: methods and statistical considerations. J Neurophysiol 2023; 130:475-496. [PMID: 37465897 PMCID: PMC10642974 DOI: 10.1152/jn.00131.2023] [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: 03/29/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 07/20/2023] Open
Abstract
As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.
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Affiliation(s)
- Robert E Kass
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Heejong Bong
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Motolani Olarinre
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Qi Xin
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Konrad N Urban
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
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29
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Comeaux P, Clark K, Noudoost B. A recruitment through coherence theory of working memory. Prog Neurobiol 2023; 228:102491. [PMID: 37393039 PMCID: PMC10530428 DOI: 10.1016/j.pneurobio.2023.102491] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/14/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
The interactions between prefrontal cortex and other areas during working memory have been studied for decades. Here we outline a conceptual framework describing interactions between these areas during working memory, and review evidence for key elements of this model. We specifically suggest that a top-down signal sent from prefrontal to sensory areas drives oscillations in these areas. Spike timing within sensory areas becomes locked to these working-memory-driven oscillations, and the phase of spiking conveys information about the representation available within these areas. Downstream areas receiving these phase-locked spikes from sensory areas can recover this information via a combination of coherent oscillations and gating of input efficacy based on the phase of their local oscillations. Although the conceptual framework is based on prefrontal interactions with sensory areas during working memory, we also discuss the broader implications of this framework for flexible communication between brain areas in general.
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Affiliation(s)
- Phillip Comeaux
- Dept. of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Salt Lake City, UT 84112, USA; Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Kelsey Clark
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Behrad Noudoost
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA.
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30
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Petro LS, Smith FW, Abbatecola C, Muckli L. The Spatial Precision of Contextual Feedback Signals in Human V1. BIOLOGY 2023; 12:1022. [PMID: 37508451 PMCID: PMC10376409 DOI: 10.3390/biology12071022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/23/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
Neurons in the primary visual cortex (V1) receive sensory inputs that describe small, local regions of the visual scene and cortical feedback inputs from higher visual areas processing the global scene context. Investigating the spatial precision of this visual contextual modulation will contribute to our understanding of the functional role of cortical feedback inputs in perceptual computations. We used human functional magnetic resonance imaging (fMRI) to test the spatial precision of contextual feedback inputs to V1 during natural scene processing. We measured brain activity patterns in the stimulated regions of V1 and in regions that we blocked from direct feedforward input, receiving information only from non-feedforward (i.e., feedback and lateral) inputs. We measured the spatial precision of contextual feedback signals by generalising brain activity patterns across parametrically spatially displaced versions of identical images using an MVPA cross-classification approach. We found that fMRI activity patterns in cortical feedback signals predicted our scene-specific features in V1 with a precision of approximately 4 degrees. The stimulated regions of V1 carried more precise scene information than non-stimulated regions; however, these regions also contained information patterns that generalised up to 4 degrees. This result shows that contextual signals relating to the global scene are similarly fed back to V1 when feedforward inputs are either present or absent. Our results are in line with contextual feedback signals from extrastriate areas to V1, describing global scene information and contributing to perceptual computations such as the hierarchical representation of feature boundaries within natural scenes.
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Affiliation(s)
- Lucy S Petro
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QB, UK
- Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Fraser W Smith
- School of Psychology, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Clement Abbatecola
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QB, UK
- Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
| | - Lars Muckli
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QB, UK
- Imaging Centre for Excellence (ICE), College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G51 4LB, UK
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31
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Celeghin A, Borriero A, Orsenigo D, Diano M, Méndez Guerrero CA, Perotti A, Petri G, Tamietto M. Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues. Front Comput Neurosci 2023; 17:1153572. [PMID: 37485400 PMCID: PMC10359983 DOI: 10.3389/fncom.2023.1153572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/19/2023] [Indexed: 07/25/2023] Open
Abstract
Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as in silico models of the primate visual system. Specifically, we highlight several emerging notions about the anatomical and physiological properties of the visual system that still need to be systematically integrated into current CNN models. These tenets include the implementation of parallel processing pathways from the early stages of retinal input and the reconsideration of several assumptions concerning the serial progression of information flow. We suggest design choices and architectural constraints that could facilitate a closer alignment with biology provide causal evidence of the predictive link between the artificial and biological visual systems. Adopting this principled perspective could potentially lead to new research questions and applications of CNNs beyond modeling object recognition.
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Affiliation(s)
| | | | - Davide Orsenigo
- Department of Psychology, University of Torino, Turin, Italy
| | - Matteo Diano
- Department of Psychology, University of Torino, Turin, Italy
| | | | | | | | - Marco Tamietto
- Department of Psychology, University of Torino, Turin, Italy
- Department of Medical and Clinical Psychology, and CoRPS–Center of Research on Psychology in Somatic Diseases–Tilburg University, Tilburg, Netherlands
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32
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Sihn D, Kwon OS, Kim SP. Robust and efficient representations of dynamic stimuli in hierarchical neural networks via temporal smoothing. Front Comput Neurosci 2023; 17:1164595. [PMID: 37398935 PMCID: PMC10307978 DOI: 10.3389/fncom.2023.1164595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/24/2023] [Indexed: 07/04/2023] Open
Abstract
Introduction Efficient coding that minimizes informational redundancy of neural representations is a widely accepted neural coding principle. Despite the benefit, maximizing efficiency in neural coding can make neural representation vulnerable to random noise. One way to achieve robustness against random noise is smoothening neural responses. However, it is not clear whether the smoothness of neural responses can hold robust neural representations when dynamic stimuli are processed through a hierarchical brain structure, in which not only random noise but also systematic error due to temporal lag can be induced. Methods In the present study, we showed that smoothness via spatio-temporally efficient coding can achieve both efficiency and robustness by effectively dealing with noise and neural delay in the visual hierarchy when processing dynamic visual stimuli. Results The simulation results demonstrated that a hierarchical neural network whose bidirectional synaptic connections were learned through spatio-temporally efficient coding with natural scenes could elicit neural responses to visual moving bars similar to those to static bars with the identical position and orientation, indicating robust neural responses against erroneous neural information. It implies that spatio-temporally efficient coding preserves the structure of visual environments locally in the neural responses of hierarchical structures. Discussion The present results suggest the importance of a balance between efficiency and robustness in neural coding for visual processing of dynamic stimuli across hierarchical brain structures.
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33
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Draganov M, Galiano-Landeira J, Doruk Camsari D, Ramírez JE, Robles M, Chanes L. Noninvasive modulation of predictive coding in humans: causal evidence for frequency-specific temporal dynamics. Cereb Cortex 2023:7156779. [PMID: 37154618 DOI: 10.1093/cercor/bhad127] [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: 01/13/2023] [Revised: 03/17/2023] [Accepted: 03/18/2023] [Indexed: 05/10/2023] Open
Abstract
Increasing evidence indicates that the brain predicts sensory input based on past experiences, importantly constraining how we experience the world. Despite a growing interest on this framework, known as predictive coding, most of such approaches to multiple psychological domains continue to be theoretical or primarily provide correlational evidence. We here explored the neural basis of predictive processing using noninvasive brain stimulation and provide causal evidence of frequency-specific modulations in humans. Participants received 20 Hz (associated with top-down/predictions), 50 Hz (associated with bottom-up/prediction errors), or sham transcranial alternating current stimulation on the left dorsolateral prefrontal cortex while performing a social perception task in which facial expression predictions were induced and subsequently confirmed or violated. Left prefrontal 20 Hz stimulation reinforced stereotypical predictions. In contrast, 50 Hz and sham stimulation failed to yield any significant behavioral effects. Moreover, the frequency-specific effect observed was further supported by electroencephalography data, which showed a boost of brain activity at the stimulated frequency band. These observations provide causal evidence for how predictive processing may be enabled in the human brain, setting up a needed framework to understand how it may be disrupted across brain-related conditions and potentially restored through noninvasive methods.
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Affiliation(s)
- Metodi Draganov
- Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Jordi Galiano-Landeira
- Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Deniz Doruk Camsari
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, United States
| | - Jairo-Enrique Ramírez
- Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Marta Robles
- Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
- Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich 80336, Germany
| | - Lorena Chanes
- Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
- Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
- Serra Húnter Programme, Generalitat de Catalunya, Barcelona 08002, Spain
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34
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Roelfsema PR. Solving the binding problem: Assemblies form when neurons enhance their firing rate-they don't need to oscillate or synchronize. Neuron 2023; 111:1003-1019. [PMID: 37023707 DOI: 10.1016/j.neuron.2023.03.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/25/2023] [Accepted: 03/09/2023] [Indexed: 04/08/2023]
Abstract
When we look at an image, its features are represented in our visual system in a highly distributed manner, calling for a mechanism that binds them into coherent object representations. There have been different proposals for the neuronal mechanisms that can mediate binding. One hypothesis is that binding is achieved by oscillations that synchronize neurons representing features of the same perceptual object. This view allows separate communication channels between different brain areas. Another hypothesis is that binding of features that are represented in different brain regions occurs when the neurons in these areas that respond to the same object simultaneously enhance their firing rate, which would correspond to directing object-based attention to these features. This review summarizes evidence in favor of and against these two hypotheses, examining the neuronal correlates of binding and assessing the time course of perceptual grouping. I conclude that enhanced neuronal firing rates bind features into coherent object representations, whereas oscillations and synchrony are unrelated to binding.
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Affiliation(s)
- Pieter R Roelfsema
- Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), 1105 BA Amsterdam, the Netherlands; Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands; Department of Psychiatry, Academic Medical Centre, Postbus 22660, 1100 DD Amsterdam, the Netherlands; Laboratory of Visual Brain Therapy, Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.
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35
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Libedinsky C. Comparing representations and computations in single neurons versus neural networks. Trends Cogn Sci 2023; 27:517-527. [PMID: 37005114 DOI: 10.1016/j.tics.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 04/03/2023]
Abstract
Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.
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36
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MacDowell CJ, Libby A, Jahn CI, Tafazoli S, Buschman TJ. Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527772. [PMID: 36798411 PMCID: PMC9934668 DOI: 10.1101/2023.02.08.527772] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
Abstract
Cognition is flexible. Behaviors can change on a moment-by-moment basis. Such flexibility is thought to rely on the brain's ability to route information through different networks of brain regions in order to support different cognitive computations. However, the mechanisms that determine which network of brain regions is engaged are unknown. To address this, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice. Different dimensions within the population activity of each brain region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed, allowing a brain region to simultaneously interact with multiple independent, yet overlapping, networks. Alignment of neural activity within a region to a specific subspace network dimension predicted how neural activity propagated between regions. Thus, changing the geometry of the neural representation within a brain region could be a mechanism to selectively engage different brain-wide networks to support cognitive flexibility.
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Affiliation(s)
- Camden J. MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
- Rutgers Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ
| | - Alexandra Libby
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
| | - Caroline I. Jahn
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
| | - Sina Tafazoli
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
| | - Timothy J. Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
- Department of Psychology, Princeton University, Washington Rd, Princeton, NJ
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37
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Debes SR, Dragoi V. Suppressing feedback signals to visual cortex abolishes attentional modulation. Science 2023; 379:468-473. [PMID: 36730414 DOI: 10.1126/science.ade1855] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/23/2022] [Indexed: 02/04/2023]
Abstract
Attention improves perception by enhancing the neural encoding of sensory information. A long-standing hypothesis is that cortical feedback projections carry top-down signals to influence sensory coding. However, this hypothesis has never been tested to establish causal links. We used viral tools to label feedback connections from cortical area V4 targeting early visual cortex (area V1). While monkeys performed a visual-spatial attention task, inactivating feedback axonal terminals in V1 without altering local intracortical and feedforward inputs reduced the response gain of single cells and impaired the accuracy of neural populations for encoding external stimuli. These effects are primarily manifested in the superficial layers of V1 and propagate to downstream area V4. Attention enhances sensory coding across visual cortex by specifically altering the strength of corticocortical feedback in a layer-dependent manner.
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Affiliation(s)
- Samantha R Debes
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas at Houston, Houston, TX 77030, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas at Houston, Houston, TX 77030, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
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38
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Varley TF, Sporns O, Schaffelhofer S, Scherberger H, Dann B. Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior. Proc Natl Acad Sci U S A 2023; 120:e2207677120. [PMID: 36603032 PMCID: PMC9926243 DOI: 10.1073/pnas.2207677120] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/29/2022] [Indexed: 01/06/2023] Open
Abstract
One of the essential functions of biological neural networks is the processing of information. This includes everything from processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological limitations, it has been historically unclear how information processing changes during different cognitive or behavioral states and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the frontoparietal areas AIP, F5, and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, interareal processing varied widely: During visuomotor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with predominance of processing in the feedback direction. Furthermore, the fine-scale network structure reconfigured at the neuron level in response to different grasping conditions, despite no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand and that the information-processing network is hierarchically organized at the neuron level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.
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Affiliation(s)
- Thomas F. Varley
- Department of Psychological & Brain Sciences, Indiana University47405-7007, Bloomington, IN
- School of Informatics, Computing, and Engineering, Indiana University47405-7007, Bloomington, IN
| | - Olaf Sporns
- Department of Psychological & Brain Sciences, Indiana University47405-7007, Bloomington, IN
| | - Stefan Schaffelhofer
- Neurobiology Laboratory, German Primate Center37077, Goettingen, Germany
- Faculty of Biology and Psychology, University of Goettingen37073, Goettingen, Germany
| | - Hansjörg Scherberger
- Neurobiology Laboratory, German Primate Center37077, Goettingen, Germany
- Faculty of Biology and Psychology, University of Goettingen37073, Goettingen, Germany
| | - Benjamin Dann
- Neurobiology Laboratory, German Primate Center37077, Goettingen, Germany
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39
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Higley MJ, Cardin JA. Spatiotemporal dynamics in large-scale cortical networks. Curr Opin Neurobiol 2022; 77:102627. [PMID: 36115252 PMCID: PMC10618958 DOI: 10.1016/j.conb.2022.102627] [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: 04/29/2022] [Revised: 06/27/2022] [Accepted: 08/04/2022] [Indexed: 01/10/2023]
Abstract
Investigating links between nervous system function and behavior requires monitoring neuronal activity at a range of spatial and temporal scales. Here, we summarize recent progress in applying two distinct but complementary approaches to the study of network dynamics in the neocortex. Mesoscopic calcium imaging allows simultaneous monitoring of activity across most of the cortex at moderate spatiotemporal resolution. Electrophysiological recordings provide extremely high temporal resolution of neural signals at multiple targeted locations. A number of recent studies have used these tools to reveal novel patterns of activity across distributed cortical subnetworks. This growing body of work strongly supports the hypothesis that the dynamic coordination of spatially distinct regions is a fundamental aspect of cortical function that supports cognition and behavior.
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Affiliation(s)
- Michael J Higley
- Departments of Neuroscience and Psychiatry, Kavli Institute for Neuroscience, Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jessica A Cardin
- Departments of Neuroscience and Psychiatry, Kavli Institute for Neuroscience, Wu Tsai Institute, Yale University, New Haven, CT, USA.
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40
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Chen Y, Douglas H, Medina BJ, Olarinre M, Siegle JH, Kass RE. Population burst propagation across interacting areas of the brain. J Neurophysiol 2022; 128:1578-1592. [PMID: 36321709 PMCID: PMC9744659 DOI: 10.1152/jn.00066.2022] [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] [Indexed: 12/12/2022] Open
Abstract
For many perceptual and behavioral tasks, a prominent feature of neural spike trains involves high firing rates across relatively short intervals of time. We call these events "population bursts." Because during a population burst information is, presumably, transmitted from one part of the brain to another, burst timing should reveal activity related to the flow of information across neural circuits. We developed a statistical method (based on a point process model) of determining, accurately, the time of the maximum (peak) population firing rate on a trial-by-trial basis and used it to characterize burst propagation across areas. We then examined the tendency of peak firing rates in distinct brain areas to shift earlier or later in time, together, across repeated trials, and found this trial-to-trial coupling of peak times to be a sensitive indicator of interaction across populations. In the data we examined, from the Allen Brain Observatory, we found many very strong correlations (95% confidence intervals above 0.75) in cases where standard methods were unable to demonstrate cross-area correlation. The statistical model introduced cross-area covariation only through population-level trial-dependent time shifts and gain constants (values of which were learned from the data), yet it provided very good fits to data histograms, including histograms of spike count correlations within and across visual areas. Our results demonstrate the utility of carefully assessing timing and propagation, across brain regions, of transient bursts in neural population activity, based on multiple spike train recordings.NEW & NOTEWORTHY We developed a novel statistical method for identifying coordinated propagation of activity across populations of spiking neurons, with high temporal accuracy. Using simultaneous recordings from three visual areas we document precise timing relationships on a trial-by-trial basis, and we show how previously existing techniques can fail to discover coordinated activity in cases where the new approach finds very strong cross-area correlation.
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Affiliation(s)
- Yu Chen
- 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania,2Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Hannah Douglas
- 3Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Bryan J. Medina
- 5Department of Computer Science, University of Central Florida, Orlando, Florida
| | - Motolani Olarinre
- 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania,3Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | | | - Robert E. Kass
- 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania,2Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania,3Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
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41
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Gokcen E, Jasper AI, Semedo JD, Zandvakili A, Kohn A, Machens CK, Yu BM. Disentangling the flow of signals between populations of neurons. NATURE COMPUTATIONAL SCIENCE 2022; 2:512-525. [PMID: 38177794 DOI: 10.1038/s43588-022-00282-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 06/21/2022] [Indexed: 01/06/2024]
Abstract
Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.
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Affiliation(s)
- Evren Gokcen
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Anna I Jasper
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
| | - João D Semedo
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Amin Zandvakili
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, New York, NY, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY, USA
| | - Christian K Machens
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal
| | - Byron M Yu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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Mir Y, Zalányi L, Pálfi E, Ashaber M, Roe AW, Friedman RM, Négyessy L. Modular Organization of Signal Transmission in Primate Somatosensory Cortex. Front Neuroanat 2022; 16:915238. [PMID: 35873660 PMCID: PMC9305200 DOI: 10.3389/fnana.2022.915238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/03/2022] [Indexed: 12/30/2022] Open
Abstract
Axonal patches are known as the major sites of synaptic connections in the cerebral cortex of higher order mammals. However, the functional role of these patches is highly debated. Patches are formed by populations of nearby neurons in a topographic manner and are recognized as the termination fields of long-distance lateral connections within and between cortical areas. In addition, axons form numerous boutons that lie outside the patches, whose function is also unknown. To better understand the functional roles of these two distinct populations of boutons, we compared individual and collective morphological features of axons within and outside the patches of intra-areal, feedforward, and feedback pathways by way of tract tracing in the somatosensory cortex of New World monkeys. We found that, with the exception of tortuosity, which is an invariant property, bouton spacing and axonal convergence properties differ significantly between axons within patch and no-patch domains. Principal component analyses corroborated the clustering of axons according to patch formation without any additional effect by the type of pathway or laminar distribution. Stepwise logistic regression identified convergence and bouton density as the best predictors of patch formation. These findings support that patches are specific sites of axonal convergence that promote the synchronous activity of neuronal populations. On the other hand, no-patch domains could form a neuroanatomical substrate to diversify the responses of cortical neurons.
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Affiliation(s)
- Yaqub Mir
- Theoretical Neuroscience and Complex Systems Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
- *Correspondence: Yaqub Mir
| | - László Zalányi
- Theoretical Neuroscience and Complex Systems Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Emese Pálfi
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
| | - Mária Ashaber
- California Institute of Technology, Department of Biology and Biological Engineering, Pasadena, CA, United States
| | - Anna W. Roe
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, United States
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Robert M. Friedman
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, United States
| | - László Négyessy
- Theoretical Neuroscience and Complex Systems Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
- László Négyessy
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