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Rizzi R, Bidelman GM. Functional benefits of continuous vs. categorical listening strategies on the neural encoding and perception of noise-degraded speech. Brain Res 2024; 1844:149166. [PMID: 39151718 DOI: 10.1016/j.brainres.2024.149166] [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: 05/19/2024] [Revised: 07/26/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Acoustic information in speech changes continuously, yet listeners form discrete perceptual categories to ease the demands of perception. Being a more continuous/gradient as opposed to a more discrete/categorical listener may be further advantageous for understanding speech in noise by increasing perceptual flexibility and resolving ambiguity. The degree to which a listener's responses to a continuum of speech sounds are categorical versus continuous can be quantified using visual analog scaling (VAS) during speech labeling tasks. Here, we recorded event-related brain potentials (ERPs) to vowels along an acoustic-phonetic continuum (/u/ to /a/) while listeners categorized phonemes in both clean and noise conditions. Behavior was assessed using standard two alternative forced choice (2AFC) and VAS paradigms to evaluate categorization under task structures that promote discrete vs. continuous hearing, respectively. Behaviorally, identification curves were steeper under 2AFC vs. VAS categorization but were relatively immune to noise, suggesting robust access to abstract, phonetic categories even under signal degradation. Behavioral slopes were correlated with listeners' QuickSIN scores; shallower slopes corresponded with better speech in noise performance, suggesting a perceptual advantage to noise degraded speech comprehension conferred by a more gradient listening strategy. At the neural level, P2 amplitudes and latencies of the ERPs were modulated by task and noise; VAS responses were larger and showed greater noise-related latency delays than 2AFC responses. More gradient responders had smaller shifts in ERP latency with noise, suggesting their neural encoding of speech was more resilient to noise degradation. Interestingly, source-resolved ERPs showed that more gradient listening was also correlated with stronger neural responses in left superior temporal gyrus. Our results demonstrate that listening strategy modulates the categorical organization of speech and behavioral success, with more continuous/gradient listening being advantageous to sentential speech in noise perception.
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Affiliation(s)
- Rose Rizzi
- Department of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA
| | - Gavin M Bidelman
- Department of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA.
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2
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Courellis HS, Minxha J, Cardenas AR, Kimmel DL, Reed CM, Valiante TA, Salzman CD, Mamelak AN, Fusi S, Rutishauser U. Abstract representations emerge in human hippocampal neurons during inference. Nature 2024; 632:841-849. [PMID: 39143207 DOI: 10.1038/s41586-024-07799-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: 11/30/2023] [Accepted: 07/09/2024] [Indexed: 08/16/2024]
Abstract
Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour2,3. Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.
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Affiliation(s)
- Hristos S Courellis
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Juri Minxha
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Araceli R Cardenas
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Daniel L Kimmel
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Taufik A Valiante
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - C Daniel Salzman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Sciences, Columbia University, New York, NY, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Stefano Fusi
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Sciences, Columbia University, New York, NY, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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3
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Purg Suljic N, Kraljic A, Rahmati M, Cho YT, Slana Ozimic A, Murray JD, Anticevic A, Repovs G. Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.07.548112. [PMID: 37662268 PMCID: PMC10473605 DOI: 10.1101/2023.07.07.548112] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six fMRI studies, resulting in a sample of 155 (77 women, 25 ± 5 years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared to categorical representations.
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4
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Jurewicz K, Sleezer BJ, Mehta PS, Hayden BY, Ebitz RB. Irrational choices via a curvilinear representational geometry for value. Nat Commun 2024; 15:6424. [PMID: 39080250 PMCID: PMC11289086 DOI: 10.1038/s41467-024-49568-4] [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] [Accepted: 06/06/2024] [Indexed: 08/02/2024] Open
Abstract
We make decisions by comparing values, but it is not yet clear how value is represented in the brain. Many models assume, if only implicitly, that the representational geometry of value is linear. However, in part due to a historical focus on noisy single neurons, rather than neuronal populations, this hypothesis has not been rigorously tested. Here, we examine the representational geometry of value in the ventromedial prefrontal cortex (vmPFC), a part of the brain linked to economic decision-making, in two male rhesus macaques. We find that values are encoded along a curved manifold in vmPFC. This curvilinear geometry predicts a specific pattern of irrational decision-making: that decision-makers will make worse choices when an irrelevant, decoy option is worse in value, compared to when it is better. We observe this type of irrational choices in behavior. Together, these results not only suggest that the representational geometry of value is nonlinear, but that this nonlinearity could impose bounds on rational decision-making.
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Affiliation(s)
- Katarzyna Jurewicz
- Department of Neurosciences, Faculté de médecine, and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage, Université de Montréal, Montréal, QC, Canada
- Department of Physiology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC, Canada
| | - Brianna J Sleezer
- Department of Neuroscience, Center for Magnetic Resonance Research, and Center for Neuroengineering, University of Minnesota, Minneapolis, MN, USA
| | - Priyanka S Mehta
- Department of Neuroscience, Center for Magnetic Resonance Research, and Center for Neuroengineering, University of Minnesota, Minneapolis, MN, USA
- Psychology Program, Department of Human Behavior, Justice, and Diversity, University of Wisconsin, Superior, Superior, WI, USA
| | - Benjamin Y Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - R Becket Ebitz
- Department of Neurosciences, Faculté de médecine, and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage, Université de Montréal, Montréal, QC, Canada.
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5
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Ranjbar A, Suratgar AA, Menhaj MB, Abbasi-Asl R. Structurally-constrained encoding framework using a multi-voxel reduced-rank latent model for human natural vision. J Neural Eng 2024; 21:046027. [PMID: 38986451 DOI: 10.1088/1741-2552/ad6184] [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: 07/02/2023] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective. Voxel-wise visual encoding models based on convolutional neural networks (CNNs) have emerged as one of the prominent predictive tools of human brain activity via functional magnetic resonance imaging signals. While CNN-based models imitate the hierarchical structure of the human visual cortex to generate explainable features in response to natural visual stimuli, there is still a need for a brain-inspired model to predict brain responses accurately based on biomedical data.Approach. To bridge this gap, we propose a response prediction module called the Structurally Constrained Multi-Output (SCMO) module to include homologous correlations that arise between a group of voxels in a cortical region and predict more accurate responses.Main results. This module employs all the responses across a visual area to predict individual voxel-wise BOLD responses and therefore accounts for the population activity and collective behavior of voxels. Such a module can determine the relationships within each visual region by creating a structure matrix that represents the underlying voxel-to-voxel interactions. Moreover, since each response module in visual encoding tasks relies on the image features, we conducted experiments using two different feature extraction modules to assess the predictive performance of our proposed module. Specifically, we employed a recurrent CNN that integrates both feedforward and recurrent interactions, as well as the popular AlexNet model that utilizes feedforward connections.Significance.We demonstrate that the proposed framework provides a reliable predictive ability to generate brain responses across multiple areas, outperforming benchmark models in terms of stability and coherency of features.
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Affiliation(s)
- Amin Ranjbar
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
- Distributed and Intelligence Optimization Research Laboratory (DIOR Lab.), Tehran, Iran
| | - Amir Abolfazl Suratgar
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
- Distributed and Intelligence Optimization Research Laboratory (DIOR Lab.), Tehran, Iran
| | - Mohammad Bagher Menhaj
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
- Distributed and Intelligence Optimization Research Laboratory (DIOR Lab.), Tehran, Iran
| | - Reza Abbasi-Asl
- Department of Neurology, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States of America
- UCSF Weill Institute for Neurosciences, San Francisco, CA, United States of America
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6
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Do J, James O, Kim YJ. Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain. iScience 2024; 27:110173. [PMID: 39040068 PMCID: PMC11260863 DOI: 10.1016/j.isci.2024.110173] [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: 12/26/2023] [Revised: 04/15/2024] [Accepted: 05/31/2024] [Indexed: 07/24/2024] Open
Abstract
Recent human brain imaging studies have identified widely distributed cortical areas that represent information about the meaning of language. Yet, the dynamic nature of widespread neural activity as a correlate of the semantic information processing remains poorly explored. Our state space analysis of electroencephalograms (EEGs) recorded during semantic match-to-category task show that depending on the semantic category and decision path chosen by participants, whole-brain delta-band dynamics follow distinct trajectories that are correlated with participants' response time on a trial-by-trial basis. Especially, the proximity of the neural trajectory to category decision-specific region in the state space was predictive of participants' decision-making reaction times. We also found that posterolateral regions primarily encoded word categories while postero-central regions encoded category decisions. Our results demonstrate the role of neural dynamics embedded in the evolving multivariate delta-band activity patterns in processing the semantic relatedness of words and the semantic category-based decision-making.
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Affiliation(s)
- Jongrok Do
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Oliver James
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Yee-Joon Kim
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon 34126, Republic of Korea
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7
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Shashidhara S, Assem M, Glasser MF, Duncan J. Task and stimulus coding in the multiple-demand network. Cereb Cortex 2024; 34:bhae278. [PMID: 39004756 PMCID: PMC11246790 DOI: 10.1093/cercor/bhae278] [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: 03/28/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/16/2024] Open
Abstract
In the human brain, a multiple-demand (MD) network plays a key role in cognitive control, with core components in lateral frontal, dorsomedial frontal and lateral parietal cortex, and multivariate activity patterns that discriminate the contents of many cognitive activities. In prefrontal cortex of the behaving monkey, different cognitive operations are associated with very different patterns of neural activity, while details of a particular stimulus are encoded as small variations on these basic patterns (Sigala et al, 2008). Here, using the advanced fMRI methods of the Human Connectome Project and their 360-region cortical parcellation, we searched for a similar result in MD activation patterns. In each parcel, we compared multivertex patterns for every combination of three tasks (working memory, task-switching, and stop-signal) and two stimulus classes (faces and buildings). Though both task and stimulus category were discriminated in every cortical parcel, the strength of discrimination varied strongly across parcels. The different cognitive operations of the three tasks were strongly discriminated in MD regions. Stimulus categories, in contrast, were most strongly discriminated in a large region of primary and higher visual cortex, and intriguingly, in both parietal and frontal lobe regions adjacent to core MD regions. In the monkey, frontal neurons show a strong pattern of nonlinear mixed selectivity, with activity reflecting specific conjunctions of task events. In our data, however, there was limited evidence for mixed selectivity; throughout the brain, discriminations of task and stimulus combined largely linearly, with a small nonlinear component. In MD regions, human fMRI data recapitulate some but not all aspects of electrophysiological data from nonhuman primates.
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Affiliation(s)
- Sneha Shashidhara
- Center for Social and Behaviour Change, Ashoka University, Sonipat, 131029, India
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB27EF, United Kingdom
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB27EF, United Kingdom
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University in St. Louis, Saint Louis, MO 63110, United States
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB27EF, United Kingdom
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8
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Stroud JP, Duncan J, Lengyel M. The computational foundations of dynamic coding in working memory. Trends Cogn Sci 2024; 28:614-627. [PMID: 38580528 DOI: 10.1016/j.tics.2024.02.011] [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: 12/11/2023] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.
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Affiliation(s)
- Jake P Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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9
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Combrisson E, Basanisi R, Gueguen MCM, Rheims S, Kahane P, Bastin J, Brovelli A. Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife 2024; 12:RP92938. [PMID: 38941238 PMCID: PMC11213568 DOI: 10.7554/elife.92938] [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] [Indexed: 06/30/2024] Open
Abstract
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Maelle CM Gueguen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of LyonLyonFrance
| | - Philippe Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut NeurosciencesGrenobleFrance
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
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10
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Bays PM, Schneegans S, Ma WJ, Brady TF. Representation and computation in visual working memory. Nat Hum Behav 2024; 8:1016-1034. [PMID: 38849647 DOI: 10.1038/s41562-024-01871-2] [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: 09/29/2022] [Accepted: 03/22/2024] [Indexed: 06/09/2024]
Abstract
The ability to sustain internal representations of the sensory environment beyond immediate perception is a fundamental requirement of cognitive processing. In recent years, debates regarding the capacity and fidelity of the working memory (WM) system have advanced our understanding of the nature of these representations. In particular, there is growing recognition that WM representations are not merely imperfect copies of a perceived object or event. New experimental tools have revealed that observers possess richer information about the uncertainty in their memories and take advantage of environmental regularities to use limited memory resources optimally. Meanwhile, computational models of visuospatial WM formulated at different levels of implementation have converged on common principles relating capacity to variability and uncertainty. Here we review recent research on human WM from a computational perspective, including the neural mechanisms that support it.
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Affiliation(s)
- Paul M Bays
- Department of Psychology, University of Cambridge, Cambridge, UK
| | | | - Wei Ji Ma
- Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
| | - Timothy F Brady
- Department of Psychology, University of California, San Diego, La Jolla, CA, USA.
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11
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Bougou V, Vanhoyland M, Bertrand A, Van Paesschen W, Op De Beeck H, Janssen P, Theys T. Neuronal tuning and population representations of shape and category in human visual cortex. Nat Commun 2024; 15:4608. [PMID: 38816391 PMCID: PMC11139926 DOI: 10.1038/s41467-024-49078-3] [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: 06/28/2023] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
Object recognition and categorization are essential cognitive processes which engage considerable neural resources in the human ventral visual stream. However, the tuning properties of human ventral stream neurons for object shape and category are virtually unknown. We performed large-scale recordings of spiking activity in human Lateral Occipital Complex in response to stimuli in which the shape dimension was dissociated from the category dimension. Consistent with studies in nonhuman primates, the neuronal representations were primarily shape-based, although we also observed category-like encoding for images of animals. Surprisingly, linear decoders could reliably classify stimulus category even in data sets that were entirely shape-based. In addition, many recording sites showed an interaction between shape and category tuning. These results represent a detailed study on shape and category coding at the neuronal level in the human ventral visual stream, furnishing essential evidence that reconciles human imaging and macaque single-cell studies.
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Affiliation(s)
- Vasiliki Bougou
- Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
- Laboratory for Neuro-and Psychophysiology, Research Group Neurophysiology, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
| | - Michaël Vanhoyland
- Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
- Laboratory for Neuro-and Psychophysiology, Research Group Neurophysiology, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
- Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium
| | | | - Wim Van Paesschen
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
| | - Hans Op De Beeck
- Laboratory Biological Psychology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Peter Janssen
- Laboratory for Neuro-and Psychophysiology, Research Group Neurophysiology, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium.
| | - Tom Theys
- Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium
- Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium
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12
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Rizzi R, Bidelman GM. Functional benefits of continuous vs. categorical listening strategies on the neural encoding and perception of noise-degraded speech. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594387. [PMID: 38798410 PMCID: PMC11118460 DOI: 10.1101/2024.05.15.594387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Acoustic information in speech changes continuously, yet listeners form discrete perceptual categories to ease the demands of perception. Being a more continuous/gradient as opposed to a discrete/categorical listener may be further advantageous for understanding speech in noise by increasing perceptual flexibility and resolving ambiguity. The degree to which a listener's responses to a continuum of speech sounds are categorical versus continuous can be quantified using visual analog scaling (VAS) during speech labeling tasks. Here, we recorded event-related brain potentials (ERPs) to vowels along an acoustic-phonetic continuum (/u/ to /a/) while listeners categorized phonemes in both clean and noise conditions. Behavior was assessed using standard two alternative forced choice (2AFC) and VAS paradigms to evaluate categorization under task structures that promote discrete (2AFC) vs. continuous (VAS) hearing, respectively. Behaviorally, identification curves were steeper under 2AFC vs. VAS categorization but were relatively immune to noise, suggesting robust access to abstract, phonetic categories even under signal degradation. Behavioral slopes were positively correlated with listeners' QuickSIN scores, suggesting a behavioral advantage for speech in noise comprehension conferred by gradient listening strategy. At the neural level, electrode level data revealed P2 peak amplitudes of the ERPs were modulated by task and noise; responses were larger under VAS vs. 2AFC categorization and showed larger noise-related delay in latency in the VAS vs. 2AFC condition. More gradient responders also had smaller shifts in ERP latency with noise, suggesting their neural encoding of speech was more resilient to noise degradation. Interestingly, source-resolved ERPs showed that more gradient listening was also correlated with stronger neural responses in left superior temporal gyrus. Our results demonstrate that listening strategy (i.e., being a discrete vs. continuous listener) modulates the categorical organization of speech and behavioral success, with continuous/gradient listening being more advantageous to speech in noise perception.
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13
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Kent RD. The Feel of Speech: Multisystem and Polymodal Somatosensation in Speech Production. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024; 67:1424-1460. [PMID: 38593006 DOI: 10.1044/2024_jslhr-23-00575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
PURPOSE The oral structures such as the tongue and lips have remarkable somatosensory capacities, but understanding the roles of somatosensation in speech production requires a more comprehensive knowledge of somatosensation in the speech production system in its entirety, including the respiratory, laryngeal, and supralaryngeal subsystems. This review was conducted to summarize the system-wide somatosensory information available for speech production. METHOD The search was conducted with PubMed/Medline and Google Scholar for articles published until November 2023. Numerous search terms were used in conducting the review, which covered the topics of psychophysics, basic and clinical behavioral research, neuroanatomy, and neuroscience. RESULTS AND CONCLUSIONS The current understanding of speech somatosensation rests primarily on the two pillars of psychophysics and neuroscience. The confluence of polymodal afferent streams supports the development, maintenance, and refinement of speech production. Receptors are both canonical and noncanonical, with the latter occurring especially in the muscles innervated by the facial nerve. Somatosensory representation in the cortex is disproportionately large and provides for sensory interactions. Speech somatosensory function is robust over the lifespan, with possible declines in advanced aging. The understanding of somatosensation in speech disorders is largely disconnected from research and theory on speech production. A speech somatoscape is proposed as the generalized, system-wide sensation of speech production, with implications for speech development, speech motor control, and speech disorders.
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14
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Heinen R, Bierbrauer A, Wolf OT, Axmacher N. Representational formats of human memory traces. Brain Struct Funct 2024; 229:513-529. [PMID: 37022435 PMCID: PMC10978732 DOI: 10.1007/s00429-023-02636-9] [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: 12/06/2022] [Accepted: 03/28/2023] [Indexed: 04/07/2023]
Abstract
Neural representations are internal brain states that constitute the brain's model of the external world or some of its features. In the presence of sensory input, a representation may reflect various properties of this input. When perceptual information is no longer available, the brain can still activate representations of previously experienced episodes due to the formation of memory traces. In this review, we aim at characterizing the nature of neural memory representations and how they can be assessed with cognitive neuroscience methods, mainly focusing on neuroimaging. We discuss how multivariate analysis techniques such as representational similarity analysis (RSA) and deep neural networks (DNNs) can be leveraged to gain insights into the structure of neural representations and their different representational formats. We provide several examples of recent studies which demonstrate that we are able to not only measure memory representations using RSA but are also able to investigate their multiple formats using DNNs. We demonstrate that in addition to slow generalization during consolidation, memory representations are subject to semantization already during short-term memory, by revealing a shift from visual to semantic format. In addition to perceptual and conceptual formats, we describe the impact of affective evaluations as an additional dimension of episodic memories. Overall, these studies illustrate how the analysis of neural representations may help us gain a deeper understanding of the nature of human memory.
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Affiliation(s)
- Rebekka Heinen
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany.
| | - Anne Bierbrauer
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
- Institute for Systems Neuroscience, Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251, Hamburg, Germany
| | - Oliver T Wolf
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
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15
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Okuyama S, Kuki T, Mushiake H. Recruitment of the premotor cortex during arithmetic operations by the monkey. Sci Rep 2024; 14:6450. [PMID: 38548764 PMCID: PMC10978941 DOI: 10.1038/s41598-024-56755-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/11/2024] [Indexed: 04/01/2024] Open
Abstract
Arithmetic operations are complex mental processes rooted in the abstract concept of numerosity. Despite the significance, the neural architecture responsible for these operations has remained largely uncharted. In this study, we explored the presence of specific neuronal activity in the dorsal premotor cortex of the monkey dedicated to numerical addition and subtraction. Our findings reveal that many of these neural activities undergo a transformation, shifting their coding from arithmetic to motor representations. These motor representations include information about which hand to use and the number of steps involved in the action. We consistently observed that cells related to the right-hand encoded addition, while those linked to the left-hand encoded subtraction, suggesting that arithmetic operations and motor commands are intertwining with each other. Furthermore, we used a multivariate decoding technique to predict the monkey's behaviour based on the activity of these arithmetic-related cells. The classifier trained to discern arithmetic operations, including addition and subtraction, not only predicted the arithmetic decisions but also the subsequent motor actions of the right and left-hand. These findings imply a cognitive extension of the motor cortex's function, where inherent neural systems are repurposed to facilitate arithmetic operations.
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Affiliation(s)
- Sumito Okuyama
- Department of Physiology, Tohoku University School of Medicine, Sendai, 980-8575, Japan
- Department of Neurosurgery, Southern Tohoku General Hospital, Miyagi, 989-2483, Japan
| | - Toshinobu Kuki
- Department of Physiology, Tohoku University School of Medicine, Sendai, 980-8575, Japan
| | - Hajime Mushiake
- Department of Physiology, Tohoku University School of Medicine, Sendai, 980-8575, Japan.
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16
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Ma F, Zhang L, Zhou J. Event-specific and persistent representations for contextual states in orbitofrontal neurons. Curr Biol 2024; 34:1023-1033.e5. [PMID: 38366594 DOI: 10.1016/j.cub.2024.01.060] [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: 09/25/2023] [Revised: 12/13/2023] [Accepted: 01/24/2024] [Indexed: 02/18/2024]
Abstract
Flexible and context-dependent behaviors require animals, including humans, to identify their current contextual state for proper rules to apply, especially when information that defines these states is partially observable. Depending on behavioral needs, contextual states usually persist for prolonged periods and across other events, including sensory stimuli, actions, and rewards, highlighting prominent challenges of holding a reliable state representation. The orbitofrontal cortex (OFC) is crucial in behaviors requiring the identification of the current context (e.g., reversal learning); however, how single units in the OFC accomplish this function has not been assessed. Do they maintain such information persistently, in separate populations from those responding phasically to events within a task, or is contextual information dynamic and embedded in these phasic responses? Here, we investigated this question by recording single units from OFC in rats performing a task that required them to identify the current contextual state related to estimated proximity to future reward with distracting olfactory cues. We found that while some OFC neurons encode contextual states, most change their selectivity upon the transition of task events. Nevertheless, despite dynamic activities in single neurons, the neural populations maintain persistent representations regarding current contextual states within particular neural subspaces.
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Affiliation(s)
- Fengjun Ma
- College of Biological Sciences, China Agricultural University, Beijing 100193, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Lingwei Zhang
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Jingfeng Zhou
- Chinese Institute for Brain Research, Beijing 102206, China.
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17
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Yao J, Hou R, Fan H, Liu J, Chen Z, Hou J, Cheng Q, Li CT. Prefrontal projections modulate recurrent circuitry in the insular cortex to support short-term memory. Cell Rep 2024; 43:113756. [PMID: 38358886 DOI: 10.1016/j.celrep.2024.113756] [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: 05/01/2023] [Revised: 11/30/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
Short-term memory (STM) maintains information during a short delay period. How long-range and local connections interact to support STM encoding remains elusive. Here, we tackle the problem focusing on long-range projections from the medial prefrontal cortex (mPFC) to the anterior agranular insular cortex (aAIC) in head-fixed mice performing an olfactory delayed-response task. Optogenetic and electrophysiological experiments reveal the behavioral importance of the two regions in encoding STM information. Spike-correlogram analysis reveals strong local and cross-region functional coupling (FC) between memory neurons encoding the same information. Optogenetic suppression of mPFC-aAIC projections during the delay period reduces behavioral performance, the proportion of memory neurons, and memory-specific FC within the aAIC, whereas optogenetic excitation enhances all of them. mPFC-aAIC projections also bidirectionally modulate the efficacy of STM-information transfer, measured by the contribution of FC spiking pairs to the memory-coding ability of following neurons. Thus, prefrontal projections modulate insular neurons' functional connectivity and memory-coding ability to support STM.
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Affiliation(s)
- Jian Yao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China
| | - Ruiqing Hou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hongmei Fan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiawei Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaoqin Chen
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200031, China
| | - Jincan Hou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China
| | - Qi Cheng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China
| | - Chengyu T Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200031, China.
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18
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Sharma KK, Diltz MA, Lincoln T, Albuquerque ER, Romanski LM. Neuronal Population Encoding of Identity in Primate Prefrontal Cortex. J Neurosci 2024; 44:e0703232023. [PMID: 37963766 PMCID: PMC10860606 DOI: 10.1523/jneurosci.0703-23.2023] [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: 04/19/2023] [Revised: 08/22/2023] [Accepted: 10/10/2023] [Indexed: 11/16/2023] Open
Abstract
The ventrolateral prefrontal cortex (VLPFC) shows robust activation during the perception of faces and voices. However, little is known about what categorical features of social stimuli drive neural activity in this region. Since perception of identity and expression are critical social functions, we examined whether neural responses to naturalistic stimuli were driven by these two categorical features in the prefrontal cortex. We recorded single neurons in the VLPFC, while two male rhesus macaques (Macaca mulatta) viewed short audiovisual videos of unfamiliar conspecifics making expressions of aggressive, affiliative, and neutral valence. Of the 285 neurons responsive to the audiovisual stimuli, 111 neurons had a main effect (two-way ANOVA) of identity, expression, or their interaction in their stimulus-related firing rates; however, decoding of expression and identity using single-unit firing rates rendered poor accuracy. Interestingly, when decoding from pseudo-populations of recorded neurons, the accuracy for both expression and identity increased with population size, suggesting that the population transmitted information relevant to both variables. Principal components analysis of mean population activity across time revealed that population responses to the same identity followed similar trajectories in the response space, facilitating segregation from other identities. Our results suggest that identity is a critical feature of social stimuli that dictates the structure of population activity in the VLPFC, during the perception of vocalizations and their corresponding facial expressions. These findings enhance our understanding of the role of the VLPFC in social behavior.
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Affiliation(s)
- K K Sharma
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - M A Diltz
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - T Lincoln
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - E R Albuquerque
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - L M Romanski
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
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19
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Henderson MM, Serences JT, Rungratsameetaweemana N. Dynamic categorization rules alter representations in human visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.11.557257. [PMID: 37745512 PMCID: PMC10515851 DOI: 10.1101/2023.09.11.557257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Everyday perceptual tasks require sensory stimuli to be dynamically encoded and analyzed according to changing behavioral goals. For example, when searching for an apple at the supermarket, one might first find the Granny Smith apples by separating all visible apples into the categories "green" and "non-green". However, suddenly remembering that your family actually likes Fuji apples would necessitate reconfiguring the boundary to separate "red" from "red-yellow" objects. This flexible processing enables identical sensory stimuli to elicit varied behaviors based on the current task context. While this phenomenon is ubiquitous in nature, little is known about the neural mechanisms that underlie such flexible computation. Traditionally, sensory regions have been viewed as mainly devoted to processing inputs, with limited involvement in adapting to varying task contexts. However, from the standpoint of efficient computation, it is plausible that sensory regions integrate inputs with current task goals, facilitating more effective information relay to higher-level cortical areas. Here we test this possibility by asking human participants to visually categorize novel shape stimuli based on different linear and non-linear boundaries. Using fMRI and multivariate analyses of retinotopically-defined visual areas, we found that shape representations in visual cortex became more distinct across relevant decision boundaries in a context-dependent manner, with the largest changes in discriminability observed for stimuli near the decision boundary. Importantly, these context-driven modulations were associated with improved categorization performance. Together, these findings demonstrate that codes in visual cortex are adaptively modulated to optimize object separability based on currently relevant decision boundaries.
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Affiliation(s)
- Margaret M Henderson
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, USA
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, USA
| | - John T Serences
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, USA
- Department of Psychology, University of California, San Diego, La Jolla, USA
- Kavli Foundation for the Brain and Mind, University of California, San Diego, La Jolla, USA
| | - Nuttida Rungratsameetaweemana
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, USA
- The Salk Institute for Biological Studies, La Jolla, USA
- Department of Biomedical Engineering, Columbia University, New York, USA
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20
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Kyzar M, Kamiński J, Brzezicka A, Reed CM, Chung JM, Mamelak AN, Rutishauser U. Dataset of human-single neuron activity during a Sternberg working memory task. Sci Data 2024; 11:89. [PMID: 38238342 PMCID: PMC10796636 DOI: 10.1038/s41597-024-02943-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
We present a dataset of 1809 single neurons recorded from the human medial temporal lobe (amygdala and hippocampus) and medial frontal lobe (anterior cingulate cortex, pre-supplementary motor area, ventral medial prefrontal cortex) across 41 sessions from 21 patients that underwent seizure monitoring with depth electrodes. Subjects performed a screening task (907 neurons) to identify images for which highly selective cells were present. Subjects then performed a working memory task (902 neurons), in which they were sequentially presented with 1-3 images for which highly selective cells were present and, following a maintenance period, were asked if the probe was identical to one of the maintained images. This Neurodata Without Borders formatted dataset includes spike times, extracellular spike waveforms, stimuli presented, behavior, electrode locations, and subject demographics. As validation, we replicate previous findings on the selectivity of concept cells and their persistent activity during working memory maintenance. This large dataset of rare human single-neuron recordings and behavior enables the investigation of the neural mechanisms of working memory in humans.
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Affiliation(s)
- Michael Kyzar
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jan Kamiński
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center of Excellence for Neural Plasticity and Brain Disorders: BRAINCITY, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Aneta Brzezicka
- Institute of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jeffrey M Chung
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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21
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O'Bryan SR, Jung S, Mohan AJ, Scolari M. Category Learning Selectively Enhances Representations of Boundary-Adjacent Exemplars in Early Visual Cortex. J Neurosci 2024; 44:e1039232023. [PMID: 37968121 PMCID: PMC10860654 DOI: 10.1523/jneurosci.1039-23.2023] [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: 05/16/2023] [Revised: 08/31/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023] Open
Abstract
Category learning and visual perception are fundamentally interactive processes, such that successful categorization often depends on the ability to make fine visual discriminations between stimuli that vary on continuously valued dimensions. Research suggests that category learning can improve perceptual discrimination along the stimulus dimensions that predict category membership and that these perceptual enhancements are a byproduct of functional plasticity in the visual system. However, the precise mechanisms underlying learning-dependent sensory modulation in categorization are not well understood. We hypothesized that category learning leads to a representational sharpening of underlying sensory populations tuned to values at or near the category boundary. Furthermore, such sharpening should occur largely during active learning of new categories. These hypotheses were tested using fMRI and a theoretically constrained model of vision to quantify changes in the shape of orientation representations while human adult subjects learned to categorize physically identical stimuli based on either an orientation rule (N = 12) or an orthogonal spatial frequency rule (N = 13). Consistent with our predictions, modeling results revealed relatively enhanced reconstructed representations of stimulus orientation in visual cortex (V1-V3) only for orientation rule learners. Moreover, these reconstructed representations varied as a function of distance from the category boundary, such that representations for challenging stimuli near the boundary were significantly sharper than those for stimuli at the category centers. These results support an efficient model of plasticity wherein only the sensory populations tuned to the most behaviorally relevant regions of feature space are enhanced during category learning.
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Affiliation(s)
- Sean R O'Bryan
- Department of Psychological Sciences, Texas Tech University, Lubbock, Texas 79409
| | - Shinyoung Jung
- Department of Psychological Sciences, Texas Tech University, Lubbock, Texas 79409
| | - Anto J Mohan
- Department of Psychological Sciences, Texas Tech University, Lubbock, Texas 79409
| | - Miranda Scolari
- Department of Psychological Sciences, Texas Tech University, Lubbock, Texas 79409
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22
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Meyers EM. NeuroDecodeR: a package for neural decoding in R. Front Neuroinform 2024; 17:1275903. [PMID: 38235167 PMCID: PMC10791947 DOI: 10.3389/fninf.2023.1275903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/16/2023] [Indexed: 01/19/2024] Open
Abstract
Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries.
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Affiliation(s)
- Ethan M. Meyers
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
- School of Cognitive Science, Hampshire College, Amherst, MA, United States
- The Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA, United States
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23
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Wang J, Li Q, Li Z, Chen A. EEG-based multivariate pattern analysis reveals the control mechanisms of emotion regulation through distancing. Int J Clin Health Psychol 2024; 24:100423. [PMID: 38059070 PMCID: PMC10696394 DOI: 10.1016/j.ijchp.2023.100423] [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: 07/26/2023] [Accepted: 11/13/2023] [Indexed: 12/08/2023] Open
Abstract
Background/objective A neurocognitive model of distancing has systematically identified a set of brain regions that support the control mechanisms for emotion regulation (ER). However, the temporal dynamics of these control mechanisms during ER remains unclear. Method To address this issue, we recorded behavioral and electroencephalogram (EEG) data to compare proactive and reactive ER modes in an adapted ER task (N = 30 adults). In different ER modes, participants were instructed to downregulate their negative emotional experiences by applying the reappraisal tactic of distancing. Results The behavioral results showed that proactive ER, which involves preparing for the upcoming regulation, reduced the negative emotional experience more than reactive ER, which involves no preparation process, in the reappraisal-negative condition. This indicated that proactive ER was more effective than reactive ER in regulating negative emotions. Event-related potential (ERP) and multivariate pattern analysis (MVPA) results showed that ER through distancing involved two phases: First, the reappraisal cue enhanced the allocation of attention to activate the mental building blocks and constructed a new perspective in the preparation process. Second, participants who benefited from the preparation process initiated the ER earlier and adaptively re-engaged in the ER if time permitted. Conclusions Taken together, the control mechanisms underlying the preparation process influence the timing of ER, while the control mechanisms underlying the regulation process determine the regulatory effect.
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Affiliation(s)
- Jing Wang
- School of Psychology, Liaoning Normal University, Dalian, China
| | - Qing Li
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Zhifang Li
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Antao Chen
- School of Psychology, Research Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai, China
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24
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Herry C, Jercog D. Stable coding of aversive associations in medial prefrontal populations. C R Biol 2023; 346:127-138. [PMID: 38116876 DOI: 10.5802/crbiol.126] [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: 02/19/2023] [Revised: 09/29/2023] [Accepted: 10/11/2023] [Indexed: 12/21/2023]
Abstract
The medial prefrontal cortex (mPFC) is at the core of numerous psychiatric conditions, including fear and anxiety-related disorders. Whereas an abundance of evidence suggests a crucial role of the mPFC in regulating fear behaviour, the precise role of the mPFC in this process is not yet entirely clear. While studies at the single-cell level have demonstrated the involvement of this area in various aspects of fear processing, such as the encoding of threat-related cues and fear expression, an increasingly prevalent idea in the systems neuroscience field is that populations of neurons are, in fact, the essential unit of computation in many integrative brain regions such as prefrontal areas. What mPFC neuronal populations represent when we face threats? To address this question, we performed electrophysiological single-unit population recordings in the dorsal mPFC while mice faced threat-predicting cues eliciting defensive behaviours, and performed pharmacological and optogenetic inactivations of this area and the amygdala. Our data indicated that the presence of threat-predicting cues induces a stable coding dynamics of internally driven representations in the dorsal mPFC, necessary to drive learned defensive behaviours. Moreover, these neural population representations primary reflect learned associations rather than specific defensive behaviours, and the construct of such representations relies on the functional integrity of the amygdala.
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25
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Ceccarelli F, Ferrucci L, Londei F, Ramawat S, Brunamonti E, Genovesio A. Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex. Nat Commun 2023; 14:8325. [PMID: 38097560 PMCID: PMC10721651 DOI: 10.1038/s41467-023-43712-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.
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Affiliation(s)
- Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
- PhD program in Behavioral Neuroscience, Sapienza University, Rome, Italy
| | - Surabhi Ramawat
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Emiliano Brunamonti
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy.
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26
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Nigro M, Tortorelli LS, Dinh K, Garad M, Zlebnik NE, Yang H. Prefrontal dynamics and encoding of flexible rule switching. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.13.571356. [PMID: 38168151 PMCID: PMC10760137 DOI: 10.1101/2023.12.13.571356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Behavioral flexibility, the ability to adjust behavioral strategies in response to changing environmental contingencies and internal demands, is fundamental to cognitive functions. Despite a large body of pharmacology and lesion studies, the underlying neurophysiological correlates and mechanisms that support flexible rule switching remain elusive. To address this question, we trained mice to distinguish complex sensory cues comprising different perceptual dimensions (set shifting). Endoscopic calcium imaging revealed that medial prefrontal cortex (mPFC) neurons represented multiple task-related events and exhibited pronounced dynamic changes during rule switching. Notably, prominent encoding capacity in the mPFC was associated with switching across, but not within perceptual dimensions. We then showed the involvement of the ascending modulatory input from the locus coeruleus (LC), as inhibiting the LC impaired rule switching behavior and impeded mPFC dynamic processes and encoding. Our results highlight the pivotal role of the mPFC in set shifting processes and demonstrate the profound impact of ascending neuromodulation on shaping prefrontal neural dynamics and behavioral flexibility.
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Affiliation(s)
- Marco Nigro
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA
| | - Lucas Silva Tortorelli
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA
| | - Kevin Dinh
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA
| | - Machhindra Garad
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA
| | - Natalie E Zlebnik
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, CA 92521, USA
- Neuroscience Graduate Program, University of California, Riverside, CA 92521, USA
| | - Hongdian Yang
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA
- Neuroscience Graduate Program, University of California, Riverside, CA 92521, USA
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27
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Pearl JE, Matsumoto N, Hayashi K, Matsuda K, Miura K, Nagai Y, Miyakawa N, Minanimoto T, Saunders RC, Sugase-Miyamoto Y, Richmond BJ, Eldridge MAG. Neural correlates of category learning in monkey inferior temporal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.568765. [PMID: 38168336 PMCID: PMC10760203 DOI: 10.1101/2023.12.05.568765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We trained two monkeys implanted with multi-electrode arrays to categorize natural images of cats and dogs, in order to observe changes in neural activity related to category learning. We recorded neural activity from area TE, which is required for normal learning of visual categories based on perceptual similarity. Neural activity during a passive viewing task was compared pre- and post-training. After the category training, the accuracy of abstract category decoding improved. Specifically, the proportion of single units with category selectivity increased, and units sustained their category-specific responses for longer. Visual category learning thus appears to enhance category separability in area TE by driving changes in the stimulus selectivity of individual neurons and by recruiting more units to the active network.
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Affiliation(s)
- Jonah E Pearl
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
- Current affiliation: Department of Neurobiology, Harvard Medical School, Boston, MA 02115, United States
| | - Narihisa Matsumoto
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
| | - Kazuko Hayashi
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
| | - Keiji Matsuda
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Yuji Nagai
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, (QST), Inage, Chiba 263-8555, Japan
| | - Naohisa Miyakawa
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, (QST), Inage, Chiba 263-8555, Japan
| | - Takafumi Minanimoto
- Department of Functional Brain Imaging, National Institutes for Quantum Science and Technology, (QST), Inage, Chiba 263-8555, Japan
| | - Richard C Saunders
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
| | - Yasuko Sugase-Miyamoto
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
| | - Barry J Richmond
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
| | - Mark A G Eldridge
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
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Stroud JP, Watanabe K, Suzuki T, Stokes MG, Lengyel M. Optimal information loading into working memory explains dynamic coding in the prefrontal cortex. Proc Natl Acad Sci U S A 2023; 120:e2307991120. [PMID: 37983510 PMCID: PMC10691340 DOI: 10.1073/pnas.2307991120] [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: 05/15/2023] [Accepted: 09/29/2023] [Indexed: 11/22/2023] Open
Abstract
Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.
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Affiliation(s)
- Jake P. Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Kei Watanabe
- Graduate School of Frontier Biosciences, Osaka University, Osaka565-0871, Japan
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Communication and Information Technology, Osaka565-0871, Japan
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, OxfordOX2 6GG, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, BudapestH-1051, Hungary
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29
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Chillale RK, Shamma S, Ostojic S, Boubenec Y. Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement. eLife 2023; 12:e85706. [PMID: 37970945 DOI: 10.7554/elife.85706] [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: 12/20/2022] [Accepted: 11/12/2023] [Indexed: 11/19/2023] Open
Abstract
Grouping sets of sounds into relevant categories is an important cognitive ability that enables the association of stimuli with appropriate goal-directed behavioral responses. In perceptual tasks, the primary auditory cortex (A1) assumes a prominent role by concurrently encoding both sound sensory features and task-related variables. Here, we sought to explore the role of A1 in the initiation of sound categorization, shedding light on its involvement in this cognitive process. We trained ferrets to discriminate click trains of different rates in a Go/No-Go delayed categorization task and recorded neural activity during both active behavior and passive exposure to the same sounds. Purely categorical response components were extracted and analyzed separately from sensory responses to reveal their contributions to the overall population response throughout the trials. We found that categorical activity emerged during sound presentation in the population average and was present in both active behavioral and passive states. However, upon task engagement, categorical responses to the No-Go category became suppressed in the population code, leading to an asymmetrical representation of the Go stimuli relative to the No-Go sounds and pre-stimulus baseline. The population code underwent an abrupt change at stimulus offset, with sustained responses after the Go sounds during the delay period. Notably, the categorical responses observed during the stimulus period exhibited a significant correlation with those extracted from the delay epoch, suggesting an early involvement of A1 in stimulus categorization.
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Affiliation(s)
- Rupesh K Chillale
- Laboratoire des Systèmes Perceptifs, Département d'Études Cognitives, École Normale Supérieure, PSL University,, Paris, France
- Laboratoire de Neurosciences Cognitives Computationnelle (INSERM U960), Département d'Études Cognitives, École Normale Supérieure, Paris, France
| | - Shihab Shamma
- Laboratoire des Systèmes Perceptifs, Département d'Études Cognitives, École Normale Supérieure, PSL University,, Paris, France
- Institute for System Research, Department of Electrical and Computer Engineering, University of Maryland, College Park, College Park, Maryland, United States
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives Computationnelle (INSERM U960), Département d'Études Cognitives, École Normale Supérieure, Paris, France
| | - Yves Boubenec
- Laboratoire des Systèmes Perceptifs, Département d'Études Cognitives, École Normale Supérieure, PSL University,, Paris, France
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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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31
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Li S, Rosen MC, Chang S, David S, Freedman DJ. Alterations of neural activity in the prefrontal cortex associated with deficits in working memory performance. Front Behav Neurosci 2023; 17:1213435. [PMID: 37915531 PMCID: PMC10616307 DOI: 10.3389/fnbeh.2023.1213435] [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: 04/27/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023] Open
Abstract
Working memory (WM), a core cognitive function, enables the temporary holding and manipulation of information in mind to support ongoing behavior. Neurophysiological recordings conducted in nonhuman primates have revealed neural correlates of this process in a network of higher-order cortical regions, particularly the prefrontal cortex (PFC). Here, we review the circuit mechanisms and functional importance of WM-related activity in these areas. Recent neurophysiological data indicates that the absence of these neural correlates at different stages of WM is accompanied by distinct behavioral deficits, which are characteristic of various disease states/normal aging and which we review here. Finally, we discuss emerging evidence of electrical stimulation ameliorating these WM deficits in both humans and non-human primates. These results are important for a basic understanding of the neural mechanisms supporting WM, as well as for translational efforts to developing therapies capable of enhancing healthy WM ability or restoring WM from dysfunction.
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Affiliation(s)
- Sihai Li
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Matthew C. Rosen
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Suha Chang
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Samuel David
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - David J. Freedman
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
- Neuroscience Institute, The University of Chicago, Chicago, IL, United States
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32
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Smit S, Moerel D, Zopf R, Rich AN. Vicarious touch: Overlapping neural patterns between seeing and feeling touch. Neuroimage 2023; 278:120269. [PMID: 37423272 DOI: 10.1016/j.neuroimage.2023.120269] [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: 05/15/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023] Open
Abstract
Simulation theories propose that vicarious touch arises when seeing someone else being touched triggers corresponding representations of being touched. Prior electroencephalography (EEG) findings show that seeing touch modulates both early and late somatosensory responses (measured with or without direct tactile stimulation). Functional Magnetic Resonance Imaging (fMRI) studies have shown that seeing touch increases somatosensory cortical activation. These findings have been taken to suggest that when we see someone being touched, we simulate that touch in our sensory systems. The somatosensory overlap when seeing and feeling touch differs between individuals, potentially underpinning variation in vicarious touch experiences. Increases in amplitude (EEG) or cerebral blood flow response (fMRI), however, are limited in that they cannot test for the information contained in the neural signal: seeing touch may not activate the same information as feeling touch. Here, we use time-resolved multivariate pattern analysis on whole-brain EEG data from people with and without vicarious touch experiences to test whether seen touch evokes overlapping neural representations with the first-hand experience of touch. Participants felt touch to the fingers (tactile trials) or watched carefully matched videos of touch to another person's fingers (visual trials). In both groups, EEG was sufficiently sensitive to allow decoding of touch location (little finger vs. thumb) on tactile trials. However, only in individuals who reported feeling touch when watching videos of touch could a classifier trained on tactile trials distinguish touch location on visual trials. This demonstrates that, for people who experience vicarious touch, there is overlap in the information about touch location held in the neural patterns when seeing and feeling touch. The timecourse of this overlap implies that seeing touch evokes similar representations to later stages of tactile processing. Therefore, while simulation may underlie vicarious tactile sensations, our findings suggest this involves an abstracted representation of directly felt touch.
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Affiliation(s)
- Sophie Smit
- Perception in Action Research Centre & School of Psychological Sciences, Macquarie University, 16 University Ave, NSW 2109, Australia.
| | - Denise Moerel
- Perception in Action Research Centre & School of Psychological Sciences, Macquarie University, 16 University Ave, NSW 2109, Australia; School of Psychology, The University of Sydney, Griffith Taylor Building A19, Camperdown, NSW 2050, Australia
| | - Regine Zopf
- Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Philosophenweg 3, Jena 07743, Federal Republic of Germany
| | - Anina N Rich
- Perception in Action Research Centre & School of Psychological Sciences, Macquarie University, 16 University Ave, NSW 2109, Australia
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33
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Watanabe K, Kadohisa M, Kusunoki M, Buckley MJ, Duncan J. Cycles of goal silencing and reactivation underlie complex problem-solving in primate frontal and parietal cortex. Nat Commun 2023; 14:5054. [PMID: 37598206 PMCID: PMC10439911 DOI: 10.1038/s41467-023-40676-1] [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: 08/30/2022] [Accepted: 08/03/2023] [Indexed: 08/21/2023] Open
Abstract
While classic views proposed that working memory (WM) is mediated by sustained firing, recent evidence suggests a contribution of activity-silent states. Within WM, human neuroimaging studies suggest a switch between attentional foreground and background, with only the foregrounded item represented in active neural firing. To address this process at the cellular level, we recorded prefrontal (PFC) and posterior parietal (PPC) neurons in a complex problem-solving task, with monkeys searching for one or two target locations in a first cycle of trials, and retaining them for memory-guided revisits on subsequent cycles. When target locations were discovered, neither frontal nor parietal neurons showed sustained goal-location codes continuing into subsequent trials and cycles. Instead there were sequences of timely goal silencing and reactivation, and following reactivation, sustained states until behavioral response. With two target locations, goal representations in both regions showed evidence of transitions between foreground and background, but the PFC representation was more complete, extending beyond the current trial to include both past and future selections. In the absence of unbroken sustained codes, different neuronal states interact to support maintenance and retrieval of WM representations across successive trials.
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Affiliation(s)
- Kei Watanabe
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK.
- Graduate School of Frontier Biosciences, Osaka University, Osaka, 565-0871, Japan.
- Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan.
| | - Mikiko Kadohisa
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB2 7EF, Cambridge, UK
| | - Makoto Kusunoki
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB2 7EF, Cambridge, UK
| | - Mark J Buckley
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
| | - John Duncan
- Department of Experimental Psychology, University of Oxford, OX1 3SR, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, CB2 7EF, Cambridge, UK
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34
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Liu W, Vicario DS. Dynamic encoding of phonetic categories in zebra finch auditory forebrain. Sci Rep 2023; 13:11172. [PMID: 37430030 DOI: 10.1038/s41598-023-37982-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 06/30/2023] [Indexed: 07/12/2023] Open
Abstract
Vocal communication requires the formation of acoustic categories to enable invariant representations of sounds despite superficial variations. Humans form acoustic categories for speech phonemes, enabling the listener to recognize words independent of speakers; animals can also discriminate speech phonemes. We investigated the neural mechanisms of this process using electrophysiological recordings from the zebra finch secondary auditory area, caudomedial nidopallium (NCM), during passive exposure to human speech stimuli consisting of two naturally spoken words produced by multiple speakers. Analysis of neural distance and decoding accuracy showed improvements in neural discrimination between word categories over the course of exposure, and this improved representation transferred to the same words by novel speakers. We conclude that NCM neurons formed generalized representations of word categories independent of speaker-specific variations that became more refined over the course of passive exposure. The discovery of this dynamic encoding process in NCM suggests a general processing mechanism for forming categorical representations of complex acoustic signals that humans share with other animals.
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Affiliation(s)
- Wanyi Liu
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
| | - David S Vicario
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
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35
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Domanski APF, Kucewicz MT, Russo E, Tricklebank MD, Robinson ESJ, Durstewitz D, Jones MW. Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall. Curr Biol 2023; 33:1220-1236.e4. [PMID: 36898372 PMCID: PMC10728550 DOI: 10.1016/j.cub.2023.02.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/05/2023] [Accepted: 02/08/2023] [Indexed: 03/11/2023]
Abstract
Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.
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Affiliation(s)
- Aleksander P F Domanski
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; The Alan Turing Institute, British Library, 96 Euston Rd, London, UK; The Francis Crick Institute, 1 Midland Road, London, UK
| | - Michal T Kucewicz
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; BioTechMed Center, Brain & Mind Electrophysiology Laboratory, Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.
| | - Eleonora Russo
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany; Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany
| | - Mark D Tricklebank
- Centre for Neuroimaging Science, King's College London, Denmark Hill, London SE5 8AF, UK
| | - Emma S J Robinson
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Matt W Jones
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
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36
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Hanganu-Opatz IL, Klausberger T, Sigurdsson T, Nieder A, Jacob SN, Bartos M, Sauer JF, Durstewitz D, Leibold C, Diester I. Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective. Neuron 2023; 111:1020-1036. [PMID: 37023708 DOI: 10.1016/j.neuron.2023.03.017] [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: 11/04/2022] [Revised: 02/15/2023] [Accepted: 03/10/2023] [Indexed: 04/08/2023]
Abstract
The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.
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Affiliation(s)
- Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Thomas Klausberger
- Center for Brain Research, Division of Cognitive Neurobiology, Medical University of Vienna, Vienna, Austria
| | - Torfi Sigurdsson
- Institute of Neurophysiology, Goethe University, Frankfurt, Germany
| | - Andreas Nieder
- Animal Physiology Unit, Institute of Neurobiology, University of Tübingen, 72076 Tübingen, Germany
| | - Simon N Jacob
- Translational Neurotechnology Laboratory, Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marlene Bartos
- Institute for Physiology I, Medical Faculty, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jonas-Frederic Sauer
- Institute for Physiology I, Medical Faculty, University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health & Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Christian Leibold
- Faculty of Biology, Bernstein Center Freiburg, BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany
| | - Ilka Diester
- Optophysiology - Optogenetics and Neurophysiology, IMBIT // BrainLinks-BrainTools, University of Freiburg, Freiburg im Breisgau, Germany.
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37
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Jozwik KM, Kietzmann TC, Cichy RM, Kriegeskorte N, Mur M. Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics. J Neurosci 2023; 43:1731-1741. [PMID: 36759190 PMCID: PMC10010451 DOI: 10.1523/jneurosci.1424-22.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/08/2022] [Accepted: 12/20/2022] [Indexed: 02/11/2023] Open
Abstract
Deep neural networks (DNNs) are promising models of the cortical computations supporting human object recognition. However, despite their ability to explain a significant portion of variance in neural data, the agreement between models and brain representational dynamics is far from perfect. We address this issue by asking which representational features are currently unaccounted for in neural time series data, estimated for multiple areas of the ventral stream via source-reconstructed magnetoencephalography data acquired in human participants (nine females, six males) during object viewing. We focus on the ability of visuo-semantic models, consisting of human-generated labels of object features and categories, to explain variance beyond the explanatory power of DNNs alone. We report a gradual reversal in the relative importance of DNN versus visuo-semantic features as ventral-stream object representations unfold over space and time. Although lower-level visual areas are better explained by DNN features starting early in time (at 66 ms after stimulus onset), higher-level cortical dynamics are best accounted for by visuo-semantic features starting later in time (at 146 ms after stimulus onset). Among the visuo-semantic features, object parts and basic categories drive the advantage over DNNs. These results show that a significant component of the variance unexplained by DNNs in higher-level cortical dynamics is structured and can be explained by readily nameable aspects of the objects. We conclude that current DNNs fail to fully capture dynamic representations in higher-level human visual cortex and suggest a path toward more accurate models of ventral-stream computations.SIGNIFICANCE STATEMENT When we view objects such as faces and cars in our visual environment, their neural representations dynamically unfold over time at a millisecond scale. These dynamics reflect the cortical computations that support fast and robust object recognition. DNNs have emerged as a promising framework for modeling these computations but cannot yet fully account for the neural dynamics. Using magnetoencephalography data acquired in human observers during object viewing, we show that readily nameable aspects of objects, such as 'eye', 'wheel', and 'face', can account for variance in the neural dynamics over and above DNNs. These findings suggest that DNNs and humans may in part rely on different object features for visual recognition and provide guidelines for model improvement.
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Affiliation(s)
- Kamila M Jozwik
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom
| | - Tim C Kietzmann
- Institute of Cognitive Science, University of Osnabrück, 49069 Osnabrück, Germany
| | - Radoslaw M Cichy
- Department of Education and Psychology, Freie Universität Berlin, 14195 Berlin, Germany
| | - Nikolaus Kriegeskorte
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York 10027
| | - Marieke Mur
- Department of Psychology, Western University, London, Ontario N6A 3K7, Canada
- Department of Computer Science, Western University, London, Ontario N6A 3K7, Canada
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Kadohisa M, Kusunoki M, Mitchell DJ, Bhatia C, Buckley MJ, Duncan J. Frontal and temporal coding dynamics in successive steps of complex behavior. Neuron 2023; 111:430-443.e3. [PMID: 36473483 DOI: 10.1016/j.neuron.2022.11.004] [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: 03/01/2022] [Revised: 06/21/2022] [Accepted: 11/03/2022] [Indexed: 12/12/2022]
Abstract
Ventrolateral prefrontal cortex (vlPFC), dorsolateral prefrontal cortex (dlPFC), and temporal cortex (TE) all contribute to visual decision-making. Accumulating evidence suggests that vlPFC may play a central role in multiple cognitive operations, perhaps resembling domain-general regions of the human frontal lobe. We trained monkeys in a task calling for learning, retrieval, and spatial selection of rewarded target objects. Recordings of neural activity covered large areas of vlPFC, dlPFC, and TE. Results suggested a central role for vlPFC in each cognitive operation with strong coding of each task feature, while only location was strongly coded in dlPFC and current object identity in TE. During target selection, target location was communicated first from vlPFC to dlPFC, followed by extensive mutual support. In vlPFC, stimulus identities were independently coded in different task operations. The results suggest a central role for the inferior frontal convexity in controlling successive operations of a complex, multi-step task.
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Affiliation(s)
- Mikiko Kadohisa
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK; Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK
| | - Makoto Kusunoki
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK; Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK
| | - Daniel J Mitchell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | - Cheshta Bhatia
- Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford St, Cambridge, MA 02138, USA
| | - Mark J Buckley
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK; Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK.
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The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings. J Neurosci 2023; 43:484-500. [PMID: 36535769 PMCID: PMC9864561 DOI: 10.1523/jneurosci.1546-22.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/18/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings.SIGNIFICANCE STATEMENT When we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.
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Using Nonhuman Primate Models to Reverse-Engineer Prefrontal Circuit Failure Underlying Cognitive Deficits in Schizophrenia. Curr Top Behav Neurosci 2023; 63:315-362. [PMID: 36607528 DOI: 10.1007/7854_2022_407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In this chapter, I review studies in nonhuman primates that emulate the circuit failure in prefrontal cortex responsible for working memory and cognitive control deficits in schizophrenia. These studies have characterized how synaptic malfunction, typically induced by blockade of NMDAR, disrupts neural function and computation in prefrontal networks to explain errors in cognitive tasks that are seen in schizophrenia. This work is finding causal relationships between pathogenic events of relevance to schizophrenia at vastly different levels of scale, from synapses, to neurons, local, circuits, distributed networks, computation, and behavior. Pharmacological manipulation, the dominant approach in primate models, has limited construct validity for schizophrenia pathogenesis, as the disease results from a complex interplay between environmental, developmental, and genetic factors. Genetic manipulation replicating schizophrenia risk is more advanced in rodent models. Nonetheless, gene manipulation in nonhuman primates is rapidly advancing, and primate developmental models have been established. Integration of large scale neural recording, genetic manipulation, and computational modeling in nonhuman primates holds considerable potential to provide a crucial schizophrenia model moving forward. Data generated by this approach is likely to fill several crucial gaps in our understanding of the causal sequence leading to schizophrenia in humans. This causal chain presents a vexing problem largely because it requires understanding how events at very different levels of scale relate to one another, from genes to circuits to cognition to social interactions. Nonhuman primate models excel here. They optimally enable discovery of causal relationships across levels of scale in the brain that are relevant to cognitive deficits in schizophrenia. The mechanistic understanding of prefrontal circuit failure they promise to provide may point the way to more effective therapeutic interventions to restore function to prefrontal networks in the disease.
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Abstract
Working memory is where thoughts are held and manipulated. For many years, the dominant model was that working memory relied on steady-state neural dynamics. A neural representation was activated and then held in that state. However, as often happens, the more we examine working memory (especially with new technology), the more complex it looks. Recent discoveries show that working memory involves multiple mechanisms, including discontinuous bouts of spiking. Memories are also dynamic, evolving in a task-dependent manner. Cortical rhythms may control those dynamics, thereby endowing top-down "executive" control over our thoughts.
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42
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Zhou Y, Mohan K, Freedman DJ. Abstract Encoding of Categorical Decisions in Medial Superior Temporal and Lateral Intraparietal Cortices. J Neurosci 2022; 42:9069-9081. [PMID: 36261285 PMCID: PMC9732825 DOI: 10.1523/jneurosci.0017-22.2022] [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: 01/04/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 01/05/2023] Open
Abstract
Categorization is an essential cognitive and perceptual process for decision-making and recognition. The posterior parietal cortex, particularly the lateral intraparietal (LIP) area has been suggested to transform visual feature encoding into abstract categorical representations. By contrast, areas closer to sensory input, such as the middle temporal (MT) area, encode stimulus features but not more abstract categorical information during categorization tasks. Here, we compare the contributions of the medial superior temporal (MST) and LIP areas in category computation by recording neuronal activity in both areas from two male rhesus macaques trained to perform a visual motion categorization task. MST is a core motion-processing region interconnected with MT and is often considered an intermediate processing stage between MT and LIP. We show that MST exhibits robust decision-correlated motion category encoding and working memory encoding similar to LIP, suggesting that MST plays a substantial role in cognitive computation, extending beyond its widely recognized role in visual motion processing.SIGNIFICANCE STATEMENT Categorization requires assigning incoming sensory stimuli into behaviorally relevant groups. Previous work found that parietal area LIP shows a strong encoding of the learned category membership of visual motion stimuli, while visual area MT shows strong direction tuning but not category tuning during a motion direction categorization task. Here we show that the medial superior temporal (MST) area, a visual motion-processing region interconnected with both LIP and MT, shows strong visual category encoding similar to that observed in LIP. This suggests that MST plays a greater role in abstract cognitive functions, extending beyond its well known role in visual motion processing.
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Affiliation(s)
- Yang Zhou
- Department of Neurobiology, The University of Chicago, Chicago, Illinois 60637
- PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, People's Republic of China
| | - Krithika Mohan
- Department of Neurobiology, The University of Chicago, Chicago, Illinois 60637
| | - David J Freedman
- Department of Neurobiology, The University of Chicago, Chicago, Illinois 60637
- The University of Chicago Neuroscience Institute, The University of Chicago, Chicago, Illinois 60637
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43
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Dynamic and stable population coding of attentional instructions coexist in the prefrontal cortex. Proc Natl Acad Sci U S A 2022; 119:e2202564119. [PMID: 36161937 DOI: 10.1073/pnas.2202564119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A large body of recent work suggests that neural representations in prefrontal cortex (PFC) are changing over time to adapt to task demands. However, it remains unclear whether and how such dynamic coding schemes depend on the encoded variable and are influenced by anatomical constraints. Using a cued attention task and multivariate classification methods, we show that neuronal ensembles in PFC encode and retain in working memory spatial and color attentional instructions in an anatomically specific manner. Spatial instructions could be decoded both from the frontal eye field (FEF) and the ventrolateral PFC (vlPFC) population, albeit more robustly from FEF, whereas color instructions were decoded more robustly from vlPFC. Decoding spatial and color information from vlPFC activity in the high-dimensional state space indicated stronger dynamics for color, across the cue presentation and memory periods. The change in the color code was largely due to rapid changes in the network state during the transition to the delay period. However, we found that dynamic vlPFC activity contained time-invariant color information within a low-dimensional subspace of neural activity that allowed for stable decoding of color across time. Furthermore, spatial attention influenced decoding of stimuli features profoundly in vlPFC, but less so in visual area V4. Overall, our results suggest that dynamic population coding of attentional instructions within PFC is shaped by anatomical constraints and can coexist with stable subspace coding that allows time-invariant decoding of information about the future target.
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Fontanier V, Sarazin M, Stoll FM, Delord B, Procyk E. Inhibitory control of frontal metastability sets the temporal signature of cognition. eLife 2022; 11:63795. [PMID: 35635439 PMCID: PMC9200403 DOI: 10.7554/elife.63795] [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: 10/07/2020] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.
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Affiliation(s)
| | - Matthieu Sarazin
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Frederic M Stoll
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Bruno Delord
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Emmanuel Procyk
- Stem Cell and Brain Research Institute U1208, Inserm, Lyon, France
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Neural signature of the perceptual decision in the neural population responses of the inferior temporal cortex. Sci Rep 2022; 12:8628. [PMID: 35606516 PMCID: PMC9127116 DOI: 10.1038/s41598-022-12236-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/20/2022] [Indexed: 11/24/2022] Open
Abstract
Rapid categorization of visual objects is critical for comprehending our complex visual world. The role of individual cortical neurons and neural populations in categorizing visual objects during passive vision has previously been studied. However, it is unclear whether and how perceptually guided behaviors affect the encoding of stimulus categories by neural population activity in the higher visual cortex. Here we studied the activity of the inferior temporal (IT) cortical neurons in macaque monkeys during both passive viewing and categorization of ambiguous body and object images. We found enhanced category information in the IT neural population activity during the correct, but not wrong, trials of the categorization task compared to the passive task. This encoding enhancement was task difficulty dependent with progressively larger values in trials with more ambiguous stimuli. Enhancement of IT neural population information for behaviorally relevant stimulus features suggests IT neural networks' involvement in perceptual decision-making behavior.
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46
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Prefrontal pyramidal neurons are critical for all phases of working memory. Cell Rep 2022; 39:110659. [PMID: 35417688 DOI: 10.1016/j.celrep.2022.110659] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/03/2021] [Accepted: 03/21/2022] [Indexed: 11/23/2022] Open
Abstract
The prefrontal cortex (PFC) is essential for working memory (WM) and has primarily been viewed as being responsible for maintaining information over a delay, but it is unclear whether it also plays a more general role during WM. Using task phase-specific optogenetic silencing of pyramidal neurons in the medial PFC (mPFC) of mice performing a spatial WM task, we find that the mPFC is required not only during the delay phase of the task but also during other phases requiring the encoding and retrieval of spatial information. Imaging of mPFC pyramidal neurons reveals that they are most strongly influenced by the animals' position and running direction, indicating a fundamental role in spatial navigation. Pyramidal neuron ensembles also represent to-be-remembered goal locations in a dynamic manner. Taken together, these results delineate the functional contribution of mPFC pyramidal neurons to WM, extending their role beyond the maintenance of information.
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47
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Kapoor V, Dwarakanath A, Safavi S, Werner J, Besserve M, Panagiotaropoulos TI, Logothetis NK. Decoding internally generated transitions of conscious contents in the prefrontal cortex without subjective reports. Nat Commun 2022; 13:1535. [PMID: 35318323 PMCID: PMC8940963 DOI: 10.1038/s41467-022-28897-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/17/2022] [Indexed: 11/30/2022] Open
Abstract
A major debate about the neural correlates of conscious perception concerns its cortical organization, namely, whether it includes the prefrontal cortex (PFC), which mediates executive functions, or it is constrained within posterior cortices. It has been suggested that PFC activity during paradigms investigating conscious perception is conflated with post-perceptual processes associated with reporting the contents of consciousness or feedforward signals originating from exogenous stimulus manipulations and relayed via posterior cortical areas. We addressed this debate by simultaneously probing neuronal populations in the rhesus macaque (Macaca mulatta) PFC during a no-report paradigm, capable of instigating internally generated transitions in conscious perception, without changes in visual stimulation. We find that feature-selective prefrontal neurons are modulated concomitantly with subjective perception and perceptual suppression of their preferred stimulus during both externally induced and internally generated changes in conscious perception. Importantly, this enables reliable single-trial, population decoding of conscious contents. Control experiments confirm significant decoding of stimulus contents, even when oculomotor responses, used for inferring perception, are suppressed. These findings suggest that internally generated changes in the contents of conscious visual perception are reliably reflected within the activity of prefrontal populations in the absence of volitional reports or changes in sensory input. The role of the prefrontal cortex in conscious perception is debated because of its involvement in task relevant behaviour, such as subjective perceptual reports. Here, the authors show that prefrontal activity in rhesus macaques correlates with subjective perception and the contents of consciousness can be decoded from prefrontal population activity even without reports.
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Affiliation(s)
- Vishal Kapoor
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany. .,International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Institute of Neuroscience (ION), Chinese Academy of Sciences, Shanghai, China.
| | - Abhilash Dwarakanath
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany
| | - Shervin Safavi
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany.,International Max Planck Research School, Tübingen, 72076, Germany
| | - Joachim Werner
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany
| | - Michel Besserve
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany.,Department of Empirical Inference, Max Planck Institute for Intelligent Systems, 72076, Tübingen, Germany
| | - Theofanis I Panagiotaropoulos
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany. .,Cognitive Neuroimaging Unit, CEA, DSV/I2BM, INSERM, Universite Paris-Sud, Universite Paris-Saclay, Neurospin Center, 91191, Gif/Yvette, France.
| | - Nikos K Logothetis
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany.,International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Institute of Neuroscience (ION), Chinese Academy of Sciences, Shanghai, China.,Division of Imaging Science and Biomedical Engineering, University of Manchester, Manchester, M13 9PT, UK
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Cognitive strategies shift information from single neurons to populations in prefrontal cortex. Neuron 2022; 110:709-721.e4. [PMID: 34932940 PMCID: PMC8857053 DOI: 10.1016/j.neuron.2021.11.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/27/2021] [Accepted: 11/19/2021] [Indexed: 11/24/2022]
Abstract
Neurons in primate lateral prefrontal cortex (LPFC) play a critical role in working memory (WM) and cognitive strategies. Consistent with adaptive coding models, responses of these neurons are not fixed but flexibly adjust on the basis of cognitive demands. However, little is known about how these adjustments affect population codes. Here, we investigated ensemble coding in LPFC while monkeys implemented different strategies in a WM task. Although single neurons were less tuned when monkeys used more stereotyped strategies, task information could still be accurately decoded from neural populations. This was due to changes in population codes that distributed information among a greater number of neurons, each contributing less to the overall population. Moreover, this shift occurred for task-relevant, but not irrelevant, information. These results demonstrate that cognitive strategies that impose structure on information held in mind rearrange population codes in LPFC, such that information becomes more distributed among neurons in an ensemble.
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49
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Zhang Y, Pan X, Wang Y. Category learning in a recurrent neural network with reinforcement learning. Front Psychiatry 2022; 13:1008011. [PMID: 36387007 PMCID: PMC9640766 DOI: 10.3389/fpsyt.2022.1008011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022] Open
Abstract
It is known that humans and animals can learn and utilize category information quickly and efficiently to adapt to changing environments, and several brain areas are involved in learning and encoding category information. However, it is unclear that how the brain system learns and forms categorical representations from the view of neural circuits. In order to investigate this issue from the network level, we combine a recurrent neural network with reinforcement learning to construct a deep reinforcement learning model to demonstrate how the category is learned and represented in the network. The model consists of a policy network and a value network. The policy network is responsible for updating the policy to choose actions, while the value network is responsible for evaluating the action to predict rewards. The agent learns dynamically through the information interaction between the policy network and the value network. This model was trained to learn six stimulus-stimulus associative chains in a sequential paired-association task that was learned by the monkey. The simulated results demonstrated that our model was able to learn the stimulus-stimulus associative chains, and successfully reproduced the similar behavior of the monkey performing the same task. Two types of neurons were found in this model: one type primarily encoded identity information about individual stimuli; the other type mainly encoded category information of associated stimuli in one chain. The two types of activity-patterns were also observed in the primate prefrontal cortex after the monkey learned the same task. Furthermore, the ability of these two types of neurons to encode stimulus or category information was enhanced during this model was learning the task. Our results suggest that the neurons in the recurrent neural network have the ability to form categorical representations through deep reinforcement learning during learning stimulus-stimulus associations. It might provide a new approach for understanding neuronal mechanisms underlying how the prefrontal cortex learns and encodes category information.
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Affiliation(s)
- Ying Zhang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, China
| | - Xiaochuan Pan
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, China
| | - Yihong Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, China
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50
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Bonnasse-Gahot L, Nadal JP. Categorical Perception: A Groundwork for Deep Learning. Neural Comput 2021; 34:437-475. [PMID: 34758487 DOI: 10.1162/neco_a_01454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 07/26/2021] [Indexed: 11/04/2022]
Abstract
Classification is one of the major tasks that deep learning is successfully tackling. Categorization is also a fundamental cognitive ability. A well-known perceptual consequence of categorization in humans and other animals, categorical per ception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on experimental and theoretical results in cognitive science, here we study categorical effects in artificial neural networks. We combine a theoretical analysis that makes use of mutual and Fisher information quantities and a series of numerical simulations on networks of increasing complexity. These formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries. We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that, for each layer in the network, quantifies the separability of the categories at the neural population level. We show on both shallow and deep neural networks that category learning automatically induces categorical perception. We further show that the deeper a layer, the stronger the categorical effects. As an outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique. More generally, our view, which finds echoes in the neuroscience literature, insists on the differential impact of noise in any given layer depending on the geometry of the neural representation that is being learned, that is, on how this geometry reflects the structure of the categories.
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Affiliation(s)
- Laurent Bonnasse-Gahot
- Centre d'Analyse et de Mathématique Sociales, École des Hautes Études en Sciences Sociales, 75006 Paris, France
| | - Jean-Pierre Nadal
- Centre d'Analyse et de Mathématique Sociales, École des Hautes Études en Sciences Sociales, 75006 Paris, France, and Laboratoire de Physique de l'ENS, Université de Paris, École Normale Supérieure, 75006 Paris, France
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