1
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Dan C, Hulse BK, Kappagantula R, Jayaraman V, Hermundstad AM. A neural circuit architecture for rapid learning in goal-directed navigation. Neuron 2024; 112:2581-2599.e23. [PMID: 38795708 DOI: 10.1016/j.neuron.2024.04.036] [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: 01/03/2023] [Revised: 01/16/2024] [Accepted: 04/30/2024] [Indexed: 05/28/2024]
Abstract
Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies' HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.
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Affiliation(s)
- Chuntao Dan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Brad K Hulse
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Ramya Kappagantula
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Vivek Jayaraman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | - Ann M Hermundstad
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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2
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Marrero K, Aruljothi K, Delgadillo C, Kabbara S, Swatch L, Zagha E. Goal-directed learning is multidimensional and accompanied by diverse and widespread changes in neocortical signaling. Cereb Cortex 2024; 34:bhae328. [PMID: 39110412 PMCID: PMC11304966 DOI: 10.1093/cercor/bhae328] [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: 05/08/2024] [Revised: 07/19/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024] Open
Abstract
New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of response-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.
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Affiliation(s)
- Krista Marrero
- Neuroscience Graduate Program, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
| | - Krithiga Aruljothi
- Department of Psychology, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
| | - Christian Delgadillo
- Division of Biomedical Sciences, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
| | - Sarah Kabbara
- Neuroscience Graduate Program, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
| | - Lovleen Swatch
- College of Natural & Agricultural Sciences, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
| | - Edward Zagha
- Neuroscience Graduate Program, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
- Department of Psychology, University of California Riverside, 900 University Avenue, Riverside, CA 92521, USA
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3
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Kissinger ST, O'neil E, Li B, Johnson KW, Krajewski JL, Kato AS. Distinctive Neurophysiological Signatures of Analgesia after Inflammatory Pain in the ACC of Freely Moving Mice. J Neurosci 2024; 44:e2231232024. [PMID: 38755005 PMCID: PMC11255429 DOI: 10.1523/jneurosci.2231-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/11/2024] [Accepted: 05/01/2024] [Indexed: 05/18/2024] Open
Abstract
Preclinical assessments of pain have often relied upon behavioral measurements and anesthetized neurophysiological recordings. Current technologies enabling large-scale neural recordings, however, have the potential to unveil quantifiable pain signals in conscious animals for preclinical studies. Although pain processing is distributed across many brain regions, the anterior cingulate cortex (ACC) is of particular interest in isolating these signals given its suggested role in the affective ("unpleasant") component of pain. Here, we explored the utility of the ACC toward preclinical pain research using head-mounted miniaturized microscopes to record calcium transients in freely moving male mice expressing genetically encoded calcium indicator 6f (GCaMP6f) under the Thy1 promoter. We verified the expression of GCaMP6f in excitatory neurons and found no intrinsic behavioral differences in this model. Using a multimodal stimulation paradigm across naive, pain, and analgesic conditions, we found that while ACC population activity roughly scaled with stimulus intensity, single-cell representations were highly flexible. We found only low-magnitude increases in population activity after complete Freund's adjuvant (CFA) and insufficient evidence for the existence of a robust nociceptive ensemble in the ACC. However, we found a temporal sharpening of response durations and generalized increases in pairwise neural correlations in the presence of the mechanistically distinct analgesics gabapentin or ibuprofen after (but not before) CFA-induced inflammatory pain. This increase was not explainable by changes in locomotion alone. Taken together, these results highlight challenges in isolating distinct pain signals among flexible representations in the ACC but suggest a neurophysiological hallmark of analgesia after pain that generalizes to at least two analgesics.
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Affiliation(s)
- Samuel T Kissinger
- Lilly Research Laboratories, Department of Neuroscience, Indianapolis, Indiana 46285
| | - Estefania O'neil
- Lilly Research Laboratories, Department of Neuroscience, Indianapolis, Indiana 46285
| | - Baolin Li
- Lilly Research Laboratories, Department of Neuroscience, Indianapolis, Indiana 46285
| | - Kirk W Johnson
- Lilly Research Laboratories, Department of Neuroscience, Indianapolis, Indiana 46285
| | - Jeffrey L Krajewski
- Lilly Research Laboratories, Department of Neuroscience, Indianapolis, Indiana 46285
| | - Akihiko S Kato
- Lilly Research Laboratories, Department of Neuroscience, Indianapolis, Indiana 46285
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4
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Chen W, Liang J, Wu Q, Han Y. Anterior cingulate cortex provides the neural substrates for feedback-driven iteration of decision and value representation. Nat Commun 2024; 15:6020. [PMID: 39019943 PMCID: PMC11255269 DOI: 10.1038/s41467-024-50388-9] [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: 02/12/2023] [Accepted: 07/05/2024] [Indexed: 07/19/2024] Open
Abstract
Adjusting decision-making under uncertain and dynamic situations is the hallmark of intelligence. It requires a system capable of converting feedback information to renew the internal value. The anterior cingulate cortex (ACC) involves in error and reward events that prompt switching or maintenance of current decision strategies. However, it is unclear whether and how the changes of stimulus-action mapping during behavioral adaptation are encoded, nor how such computation drives decision adaptation. Here, we tracked ACC activity in male mice performing go/no-go auditory discrimination tasks with manipulated stimulus-reward contingencies. Individual ACC neurons integrate the outcome information to the value representation in the next-run trials. Dynamic recruitment of them determines the learning rate of error-guided value iteration and decision adaptation, forming a non-linear feedback-driven updating system to secure the appropriate decision switch. Optogenetically suppressing ACC significantly slowed down feedback-driven decision switching without interfering with the execution of the established strategy.
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Affiliation(s)
- Wenqi Chen
- Department of Neurobiology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Jiejunyi Liang
- State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Qiyun Wu
- State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yunyun Han
- Department of Neurobiology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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5
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Lippl S, Kay K, Jensen G, Ferrera VP, Abbott LF. A mathematical theory of relational generalization in transitive inference. Proc Natl Acad Sci U S A 2024; 121:e2314511121. [PMID: 38968113 PMCID: PMC11252811 DOI: 10.1073/pnas.2314511121] [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: 08/22/2023] [Accepted: 05/30/2024] [Indexed: 07/07/2024] Open
Abstract
Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ([Formula: see text] and [Formula: see text]) and generalize it to new combinations of items ([Formula: see text]). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.
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Affiliation(s)
- Samuel Lippl
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
| | - Kenneth Kay
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University, New York, NY10027
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY10027
| | - Greg Jensen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
- Department of Psychology, Reed College, Portland, OR97202
| | - Vincent P. Ferrera
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
- Department of Psychiatry, Columbia University Medical Center, New York, NY10032
| | - L. F. Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
- Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University, New York, NY10027
- Department of Neuroscience, Columbia University Medical Center, New York, NY10032
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6
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Federman N, Romano SA, Amigo-Duran M, Salomon L, Marin-Burgin A. Acquisition of non-olfactory encoding improves odour discrimination in olfactory cortex. Nat Commun 2024; 15:5572. [PMID: 38956072 PMCID: PMC11220071 DOI: 10.1038/s41467-024-49897-4] [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: 09/20/2023] [Accepted: 06/20/2024] [Indexed: 07/04/2024] Open
Abstract
Olfaction is influenced by contextual factors, past experiences, and the animal's internal state. Whether this information is integrated at the initial stages of cortical odour processing is not known, nor how these signals may influence odour encoding. Here we revealed multiple and diverse non-olfactory responses in the primary olfactory (piriform) cortex (PCx), which dynamically enhance PCx odour discrimination according to behavioural demands. We performed recordings of PCx neurons from mice trained in a virtual reality task to associate odours with visual contexts to obtain a reward. We found that learning shifts PCx activity from encoding solely odours to a regime in which positional, contextual, and associative responses emerge on odour-responsive neurons that become mixed-selective. The modulation of PCx activity by these non-olfactory signals was dynamic, improving odour decoding during task engagement and in rewarded contexts. This improvement relied on the acquired mixed-selectivity, demonstrating how integrating extra-sensory inputs in sensory cortices can enhance sensory processing while encoding the behavioural relevance of stimuli.
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Grants
- 108878 Canadian International Development Agency (Agence Canadienne de Développement International)
- PICT2018-0880 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2020-0360 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2020-1536 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2016-2758 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2017-4023 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PIP2787 Ministerio de Ciencia, Tecnología e Innovación Productiva (Ministry of Science, Technology and Productive Innovation, Argentina)
- SPIRIT 216044 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
- Fondo para la convergencia estructural del Mercosur–FOCEM grant cOF 03/11
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Affiliation(s)
- Noel Federman
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina.
| | - Sebastián A Romano
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina.
| | - Macarena Amigo-Duran
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, PhD Program, Buenos Aires, Argentina
| | - Lucca Salomon
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, PhD Program, Buenos Aires, Argentina
| | - Antonia Marin-Burgin
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina.
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7
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Panniello M, Gillon CJ, Maffulli R, Celotto M, Richards BA, Panzeri S, Kohl MM. Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning. Cell Rep 2024; 43:114244. [PMID: 38796851 DOI: 10.1016/j.celrep.2024.114244] [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: 03/07/2023] [Revised: 01/16/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.
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Affiliation(s)
- Mariangela Panniello
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK; School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QQ, UK; Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Colleen J Gillon
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada; Department of Cell & Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada; Mila, Montréal, QC H2S 3H1, Canada
| | - Roberto Maffulli
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marco Celotto
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany; Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
| | - Blake A Richards
- Mila, Montréal, QC H2S 3H1, Canada; School of Computer Science, McGill University, Montréal, QC H3A 2A7, Canada; Department of Neurology & Neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada; Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, ON M5G 1M1, Canada; Montreal Neurological Institute, Montréal, QC H3A 2B4, Canada
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Michael M Kohl
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK; School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QQ, UK.
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8
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Drieu C, Zhu Z, Wang Z, Fuller K, Wang A, Elnozahy S, Kuchibhotla K. Rapid emergence of latent knowledge in the sensory cortex drives learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.10.597946. [PMID: 38915657 PMCID: PMC11195094 DOI: 10.1101/2024.06.10.597946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Rapid learning confers significant advantages to animals in ecological environments. Despite the need for speed, animals appear to only slowly learn to associate rewarded actions with predictive cues1-4. This slow learning is thought to be supported by a gradual expansion of predictive cue representation in the sensory cortex2,5. However, evidence is growing that animals learn more rapidly than classical performance measures suggest6-8, challenging the prevailing model of sensory cortical plasticity. Here, we investigated the relationship between learning and sensory cortical representations. We trained mice on an auditory go/no-go task that dissociated the rapid acquisition of task contingencies (learning) from its slower expression (performance)7. Optogenetic silencing demonstrated that the auditory cortex (AC) drives both rapid learning and slower performance gains but becomes dispensable at expert. Rather than enhancement or expansion of cue representations9, two-photon calcium imaging of AC excitatory neurons throughout learning revealed two higher-order signals that were causal to learning and performance. First, a reward prediction (RP) signal emerged rapidly within tens of trials, was present after action-related errors only early in training, and faded at expert levels. Strikingly, silencing at the time of the RP signal impaired rapid learning, suggesting it serves an associative and teaching role. Second, a distinct cell ensemble encoded and controlled licking suppression that drove the slower performance improvements. These two ensembles were spatially clustered but uncoupled from underlying sensory representations, indicating a higher-order functional segregation within AC. Our results reveal that the sensory cortex manifests higher-order computations that separably drive rapid learning and slower performance improvements, reshaping our understanding of the fundamental role of the sensory cortex.
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Affiliation(s)
- Céline Drieu
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD, USA
| | - Ziyi Zhu
- Department of Neuroscience, School of Medicine, Johns Hopkins University, MD, USA
| | - Ziyun Wang
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Kylie Fuller
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Aaron Wang
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Sarah Elnozahy
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
- Present address: Sainsbury Wellcome Centre, London, UK
| | - Kishore Kuchibhotla
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD, USA
- Department of Neuroscience, School of Medicine, Johns Hopkins University, MD, USA
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9
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Pellegrino A, Stein H, Cayco-Gajic NA. Dimensionality reduction beyond neural subspaces with slice tensor component analysis. Nat Neurosci 2024; 27:1199-1210. [PMID: 38710876 DOI: 10.1038/s41593-024-01626-2] [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: 12/06/2022] [Accepted: 03/20/2024] [Indexed: 05/08/2024]
Abstract
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.
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Affiliation(s)
- Arthur Pellegrino
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Heike Stein
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - N Alex Cayco-Gajic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
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10
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Zhu M, Kuhlman SJ, Barth AL. Transient enhancement of stimulus-evoked activity in neocortex during sensory learning. Learn Mem 2024; 31:a053870. [PMID: 38955432 PMCID: PMC11261211 DOI: 10.1101/lm.053870.123] [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: 09/15/2023] [Accepted: 05/07/2024] [Indexed: 07/04/2024]
Abstract
Synaptic potentiation has been linked to learning in sensory cortex, but the connection between this potentiation and increased sensory-evoked neural activity is not clear. Here, we used longitudinal in vivo Ca2+ imaging in the barrel cortex of awake mice to test the hypothesis that increased excitatory synaptic strength during the learning of a whisker-dependent sensory-association task would be correlated with enhanced stimulus-evoked firing. To isolate stimulus-evoked responses from dynamic, task-related activity, imaging was performed outside of the training context. Although prior studies indicate that multiwhisker stimuli drive robust subthreshold activity, we observed sparse activation of L2/3 pyramidal (Pyr) neurons in both control and trained mice. Despite evidence for excitatory synaptic strengthening at thalamocortical and intracortical synapses in this brain area at the onset of learning-indeed, under our imaging conditions thalamocortical axons were robustly activated-we observed that L2/3 Pyr neurons in somatosensory (barrel) cortex displayed only modest increases in stimulus-evoked activity that were concentrated at the onset of training. Activity renormalized over longer training periods. In contrast, when stimuli and rewards were uncoupled in a pseudotraining paradigm, stimulus-evoked activity in L2/3 Pyr neurons was significantly suppressed. These findings indicate that sensory-association training but not sensory stimulation without coupled rewards may briefly enhance sensory-evoked activity, a phenomenon that might help link sensory input to behavioral outcomes at the onset of learning.
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Affiliation(s)
- Mo Zhu
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Sandra J Kuhlman
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Alison L Barth
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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11
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Yoshinaga Y, Sato N. Reach-to-Grasp and tactile discrimination task: A new task for the study of sensory-motor learning. Behav Brain Res 2024; 466:115007. [PMID: 38648867 DOI: 10.1016/j.bbr.2024.115007] [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: 01/30/2024] [Revised: 04/04/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024]
Abstract
Although active touch in rodents arises from the forepaws as well as whiskers, most research on active touch only focuses on whiskers. This results in a paucity of tasks designed to assess the process of active touch with a forepaw. We develop a new experimental task, the Reach-to-Grasp and Tactile Discrimination task (RGTD task), to examine active touch with a forepaw in rodents, particularly changes in processes of active touch during motor skill learning. In the RGTD task, animals are required to (1) extend their forelimb to an object, (2) grasp the object, and (3) manipulate the grasped object with the forelimb. The animals must determine the direction of the manipulation based on active touch sensations arising during the period of the grasping. In experiment 1 of the present study, we showed that rats can learn the RGTD task. In experiment 2, we confirmed that the rats are capable of reversal learning of the RGTD task. The RGTD task shared most of the reaching movements involved with conventional forelimb reaching tasks. From the standpoint of a discrimination task, the RGTD task enables rigorous experimental control, for example by removing bias in the stimulus-response correspondence, and makes it possible to utilize diverse experimental procedures that have been difficult in prior tasks.
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Affiliation(s)
- Yudai Yoshinaga
- Department of Psychological Sciences, Kwansei Gakuin University, 1-1-155, Uegahara, Nishinomiya, Hyogo 662-8501, Japan; Research Fellow of Japan Society for the Promotion of Science, Japan
| | - Nobuya Sato
- Department of Psychological Sciences, Kwansei Gakuin University, 1-1-155, Uegahara, Nishinomiya, Hyogo 662-8501, Japan; Center for Applied Psychological Science (CAPS), Kwansei Gakuin University, 1-1-155, Uegahara, Nishinomiya, Hyogo, Japan.
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12
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Steinfeld R, Tacão-Monteiro A, Renart A. Differential representation of sensory information and behavioral choice across layers of the mouse auditory cortex. Curr Biol 2024; 34:2200-2211.e6. [PMID: 38733991 DOI: 10.1016/j.cub.2024.04.040] [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: 12/05/2023] [Revised: 03/22/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024]
Abstract
The activity of neurons in sensory areas sometimes covaries with upcoming choices in decision-making tasks. However, the prevalence, causal origin, and functional role of choice-related activity remain controversial. Understanding the circuit-logic of decision signals in sensory areas will require understanding their laminar specificity, but simultaneous recordings of neural activity across the cortical layers in forced-choice discrimination tasks have not yet been performed. Here, we describe neural activity from such recordings in the auditory cortex of mice during a frequency discrimination task with delayed report, which, as we show, requires the auditory cortex. Stimulus-related information was widely distributed across layers but disappeared very quickly after stimulus offset. Choice selectivity emerged toward the end of the delay period-suggesting a top-down origin-but only in the deep layers. Early stimulus-selective and late choice-selective deep neural ensembles were correlated, suggesting that the choice-selective signal fed back to the auditory cortex is not just action specific but develops as a consequence of the sensory-motor contingency imposed by the task.
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Affiliation(s)
- Raphael Steinfeld
- Champalimaud Research, Champalimaud Foundation, Avenida Brasília, 1400-038 Lisbon, Portugal.
| | - André Tacão-Monteiro
- Champalimaud Research, Champalimaud Foundation, Avenida Brasília, 1400-038 Lisbon, Portugal
| | - Alfonso Renart
- Champalimaud Research, Champalimaud Foundation, Avenida Brasília, 1400-038 Lisbon, Portugal.
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13
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Levi A, Aviv N, Stark E. Learning to learn: Single session acquisition of new rules by freely moving mice. PNAS NEXUS 2024; 3:pgae203. [PMID: 38818240 PMCID: PMC11138122 DOI: 10.1093/pnasnexus/pgae203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024]
Abstract
Learning from examples and adapting to new circumstances are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in nonhuman subjects. To determine how rapidly freely moving mice can learn a new discrimination criterion (DC), we design a two-alternative forced-choice visual discrimination paradigm in which the DCs governing the task can change between sessions. We find that experienced animals can learn a new DC after being exposed to only five training and three testing trials. The propensity for single session learning improves over time and is accurately predicted based on animal experience and criterion difficulty. After establishing the procedural learning of a paradigm, mice continuously improve their performance in new circumstances. Thus, mice learn to learn.
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Affiliation(s)
- Amir Levi
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Noam Aviv
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Stark
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol Department of Neurobiology, Haifa University, Haifa 3103301, Israel
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14
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Losey DM, Hennig JA, Oby ER, Golub MD, Sadtler PT, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Yu BM, Chase SM. Learning leaves a memory trace in motor cortex. Curr Biol 2024; 34:1519-1531.e4. [PMID: 38531360 PMCID: PMC11097210 DOI: 10.1016/j.cub.2024.03.003] [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: 01/21/2023] [Revised: 12/06/2023] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.
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Affiliation(s)
- Darby M Losey
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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15
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Lippl S, Kay K, Jensen G, Ferrera VP, Abbott L. A mathematical theory of relational generalization in transitive inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.22.554287. [PMID: 37662223 PMCID: PMC10473627 DOI: 10.1101/2023.08.22.554287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. Here we investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation (A > B and B > C) and generalize it to new combinations of items (A > C). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and has been found to improve generalization, unexpectedly show poor generalization and anomalous behavior. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.
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Affiliation(s)
- Samuel Lippl
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Center for Theoretical Neuroscience, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
| | - Kenneth Kay
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Center for Theoretical Neuroscience, Columbia University, NY
- Grossman Center for the Statistics of Mind, Columbia University, NY
| | - Greg Jensen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
- Department of Psychology at Reed College, OR
| | - Vincent P. Ferrera
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
- Department of Psychiatry, Columbia University Medical Center, NY
| | - L.F. Abbott
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
- Center for Theoretical Neuroscience, Columbia University, NY
- Department of Neuroscience, Columbia University Medical Center, NY
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16
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Tlaie A, Shapcott K, van der Plas TL, Rowland J, Lees R, Keeling J, Packer A, Tiesinga P, Schölvinck ML, Havenith MN. What does the mean mean? A simple test for neuroscience. PLoS Comput Biol 2024; 20:e1012000. [PMID: 38640119 PMCID: PMC11062559 DOI: 10.1371/journal.pcbi.1012000] [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: 10/03/2023] [Revised: 05/01/2024] [Accepted: 03/12/2024] [Indexed: 04/21/2024] Open
Abstract
Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.
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Affiliation(s)
- Alejandro Tlaie
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain
| | | | - Thijs L. van der Plas
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - James Rowland
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Robert Lees
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Joshua Keeling
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Adam Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Paul Tiesinga
- Department of Neuroinformatics, Donders Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Martha N. Havenith
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
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17
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Polanía R, Burdakov D, Hare TA. Rationality, preferences, and emotions with biological constraints: it all starts from our senses. Trends Cogn Sci 2024; 28:264-277. [PMID: 38341322 DOI: 10.1016/j.tics.2024.01.003] [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: 12/28/2022] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/12/2024]
Abstract
Is the role of our sensory systems to represent the physical world as accurately as possible? If so, are our preferences and emotions, often deemed irrational, decoupled from these 'ground-truth' sensory experiences? We show why the answer to both questions is 'no'. Brain function is metabolically costly, and the brain loses some fraction of the information that it encodes and transmits. Therefore, if brains maximize objective functions that increase the fitness of their species, they should adapt to the objective-maximizing rules of the environment at the earliest stages of sensory processing. Consequently, observed 'irrationalities', preferences, and emotions stem from the necessity for our early sensory systems to adapt and process information while considering the metabolic costs and internal states of the organism.
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Affiliation(s)
- Rafael Polanía
- Decision Neuroscience Laboratory, Department of Health Sciences and Technology, ETH, Zurich, Zurich, Switzerland.
| | - Denis Burdakov
- Neurobehavioral Dynamics Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Todd A Hare
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
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18
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Myers-Joseph D, Wilmes KA, Fernandez-Otero M, Clopath C, Khan AG. Disinhibition by VIP interneurons is orthogonal to cross-modal attentional modulation in primary visual cortex. Neuron 2024; 112:628-645.e7. [PMID: 38070500 DOI: 10.1016/j.neuron.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/24/2023] [Accepted: 11/08/2023] [Indexed: 02/24/2024]
Abstract
Attentional modulation of sensory processing is a key feature of cognition; however, its neural circuit basis is poorly understood. A candidate mechanism is the disinhibition of pyramidal cells through vasoactive intestinal peptide (VIP) and somatostatin (SOM)-positive interneurons. However, the interaction of attentional modulation and VIP-SOM disinhibition has never been directly tested. We used all-optical methods to bi-directionally manipulate VIP interneuron activity as mice performed a cross-modal attention-switching task. We measured the activities of VIP, SOM, and parvalbumin (PV)-positive interneurons and pyramidal neurons identified in the same tissue and found that although activity in all cell classes was modulated by both attention and VIP manipulation, their effects were orthogonal. Attention and VIP-SOM disinhibition relied on distinct patterns of changes in activity and reorganization of interactions between inhibitory and excitatory cells. Circuit modeling revealed a precise network architecture consistent with multiplexing strong yet non-interacting modulations in the same neural population.
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Affiliation(s)
- Dylan Myers-Joseph
- Centre for Developmental Neurobiology, King's College London, London SE1 1UL, UK
| | | | | | - Claudia Clopath
- Department of Bioengineering, Imperial College, London SW7 2AZ, UK
| | - Adil G Khan
- Centre for Developmental Neurobiology, King's College London, London SE1 1UL, UK.
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19
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Malone TJ, Tien NW, Ma Y, Cui L, Lyu S, Wang G, Nguyen D, Zhang K, Myroshnychenko MV, Tyan J, Gordon JA, Kupferschmidt DA, Gu Y. A consistent map in the medial entorhinal cortex supports spatial memory. Nat Commun 2024; 15:1457. [PMID: 38368457 PMCID: PMC10874432 DOI: 10.1038/s41467-024-45853-4] [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: 09/20/2023] [Accepted: 02/05/2024] [Indexed: 02/19/2024] Open
Abstract
The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.
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Affiliation(s)
- Taylor J Malone
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nai-Wen Tien
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Yan Ma
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lian Cui
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Shangru Lyu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Garret Wang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Duc Nguyen
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- Center of Neural Science, New York University, New York, NY, USA
| | - Kai Zhang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Maxym V Myroshnychenko
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jean Tyan
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Joshua A Gordon
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Office of the Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - David A Kupferschmidt
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Yi Gu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.
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20
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Potter C, Bassi C, Runyan CA. Simultaneous interneuron labeling reveals population-level interactions among parvalbumin, somatostatin, and pyramidal neurons in cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.09.523298. [PMID: 36711788 PMCID: PMC9882008 DOI: 10.1101/2023.01.09.523298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Cortical interneurons shape network activity in cell type-specific ways, and are also influenced by interactions with other cell types. These specific cell-type interactions are understudied, as transgenic labeling methods typically restrict labeling to one neuron type at a time. Although recent methods have enabled post-hoc identification of cell types, these are not available to many labs. Here, we present a method to distinguish between two red fluorophores in vivo, which allowed imaging of activity in somatostatin (SOM), parvalbumin (PV), and putative pyramidal neurons (PYR) in mouse association cortex. We compared population events of elevated activity and observed that the PYR network state corresponded to the ratio between mean SOM and PV neuron activity, demonstrating the importance of simultaneous labeling to explain dynamics. These results extend previous findings in sensory cortex, as activity became sparser and less correlated when the ratio between SOM and PV activity was high.
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Affiliation(s)
- Christian Potter
- Department of Neuroscience
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA
| | - Constanza Bassi
- Department of Neuroscience
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA
| | - Caroline A. Runyan
- Department of Neuroscience
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA
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21
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de Brito CSN, Gerstner W. Learning what matters: Synaptic plasticity with invariance to second-order input correlations. PLoS Comput Biol 2024; 20:e1011844. [PMID: 38346073 PMCID: PMC10890752 DOI: 10.1371/journal.pcbi.1011844] [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: 12/01/2022] [Revised: 02/23/2024] [Accepted: 01/18/2024] [Indexed: 02/25/2024] Open
Abstract
Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, our learning objective explains the functional form of observed excitatory plasticity mechanisms, showing how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations so that receptive fields become aligned with features hidden in higher-order statistics. Invariance to second-order correlations enhances the versatility of biologically realistic learning models, supporting optimal decoding from noisy inputs and sparse population coding from spatially correlated stimuli. In a spiking model with triplet spike-timing-dependent plasticity (STDP), we show that individual neurons can learn localized oriented receptive fields, circumventing the need for input preprocessing, such as whitening, or population-level lateral inhibition. The theory advances our understanding of local unsupervised learning in cortical circuits, offers new interpretations of the Bienenstock-Cooper-Munro and triplet STDP models, and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.
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Affiliation(s)
- Carlos Stein Naves de Brito
- École Polytechnique Fédérale de Lausanne, EPFL, Lusanne, Switzerland
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Wulfram Gerstner
- École Polytechnique Fédérale de Lausanne, EPFL, Lusanne, Switzerland
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22
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Song Y, Millidge B, Salvatori T, Lukasiewicz T, Xu Z, Bogacz R. Inferring neural activity before plasticity as a foundation for learning beyond backpropagation. Nat Neurosci 2024; 27:348-358. [PMID: 38172438 PMCID: PMC7615830 DOI: 10.1038/s41593-023-01514-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 11/02/2023] [Indexed: 01/05/2024]
Abstract
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as 'credit assignment'. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. Here, we set out a fundamentally different principle on credit assignment called 'prospective configuration'. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms and (3) reproduces surprising patterns of neural activity and behavior observed in diverse human and rat learning experiments.
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Affiliation(s)
- Yuhang Song
- Department of Computer Science, University of Oxford, Oxford, UK.
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
- Fractile, Ltd., London, UK.
| | - Beren Millidge
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Tommaso Salvatori
- Department of Computer Science, University of Oxford, Oxford, UK
- Institute of Logic and Computation, Vienna University of Technology, Vienna, Austria
- VERSES AI Research Lab, Los Angeles, CA, USA
| | - Thomas Lukasiewicz
- Department of Computer Science, University of Oxford, Oxford, UK.
- Institute of Logic and Computation, Vienna University of Technology, Vienna, Austria.
| | - Zhenghua Xu
- Department of Computer Science, University of Oxford, Oxford, UK.
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China.
| | - Rafal Bogacz
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
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23
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Ueno H, Takahashi Y, Mori S, Murakami S, Wani K, Matsumoto Y, Okamoto M, Ishihara T. Mice Recognise Mice in Neighbouring Rearing Cages and Change Their Social Behaviour. Behav Neurol 2024; 2024:9215607. [PMID: 38264671 PMCID: PMC10805542 DOI: 10.1155/2024/9215607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/27/2023] [Accepted: 01/04/2024] [Indexed: 01/25/2024] Open
Abstract
Mice are social animals that change their behaviour primarily in response to visual, olfactory, and auditory information from conspecifics. Rearing conditions such as cage size and colour are important factors influencing mouse behaviour. In recent years, transparent plastic cages have become standard breeding cages. The advantage of using a transparent cage is that the experimenter can observe the mouse from outside the cage without touching the cage. However, mice may recognise the environment outside the cage and change their behaviour. We speculated that mice housed in transparent cages might recognise mice in neighbouring cages. We used only male mice in this experiment. C57BL/6 mice were kept in transparent rearing cages with open lids, and the cage positions were maintained for 3 weeks. Subsequently, we examined how mice behaved toward cagemate mice, mice from neighbouring cages, and mice from distant cages. We compared the level of interest in mice using a social preference test. Similar to previous reports, subject mice showed a high degree of interest in unfamiliar mice from distant cages. By contrast, subject mice reacted to mice from neighbouring cages as familiar mice, similar to cagemate mice. This suggests that mice housed in transparent cages with open lids perceive the external environment and identify mice in neighbouring cages. Researchers should pay attention to the environment outside the mouse cage, especially for the social preference test.
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Affiliation(s)
- Hiroshi Ueno
- Department of Medical Technology, Kawasaki University of Medical Welfare, Okayama 701-0193, Japan
| | - Yu Takahashi
- Department of Psychiatry, Kawasaki Medical School, Kurashiki 701-0192, Japan
| | - Sachiko Mori
- Department of Psychiatry, Kawasaki Medical School, Kurashiki 701-0192, Japan
| | - Shinji Murakami
- Department of Psychiatry, Kawasaki Medical School, Kurashiki 701-0192, Japan
| | - Kenta Wani
- Department of Psychiatry, Kawasaki Medical School, Kurashiki 701-0192, Japan
| | - Yosuke Matsumoto
- Department of Neuropsychiatry, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan
| | - Motoi Okamoto
- Department of Medical Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Takeshi Ishihara
- Department of Psychiatry, Kawasaki Medical School, Kurashiki 701-0192, Japan
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24
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Benisty H, Barson D, Moberly AH, Lohani S, Tang L, Coifman RR, Crair MC, Mishne G, Cardin JA, Higley MJ. Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior. Nat Neurosci 2024; 27:148-158. [PMID: 38036743 PMCID: PMC11316935 DOI: 10.1038/s41593-023-01498-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 10/16/2023] [Indexed: 12/02/2023]
Abstract
Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.
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Affiliation(s)
- Hadas Benisty
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Daniel Barson
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Andrew H Moberly
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Sweyta Lohani
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Lan Tang
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Ronald R Coifman
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
| | - Michael C Crair
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Gal Mishne
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Jessica A Cardin
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Michael J Higley
- Department of Neuroscience, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
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25
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Tang Y, Gervais C, Moffitt R, Nareddula S, Zimmermann M, Nadew YY, Quinn CJ, Saldarriaga V, Edens P, Chubykin AA. Visual experience induces 4-8 Hz synchrony between V1 and higher-order visual areas. Cell Rep 2023; 42:113482. [PMID: 37999977 PMCID: PMC10790627 DOI: 10.1016/j.celrep.2023.113482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 09/20/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Visual perceptual experience induces persistent 4-8 Hz oscillations in the mouse primary visual cortex (V1), encoding visual familiarity. Recent studies suggest that higher-order visual areas (HVAs) are functionally specialized and segregated into information streams processing distinct visual features. However, whether visual memories are processed and stored within the distinct streams is not understood. We report here that V1 and lateromedial (LM), but not V1 and anterolateral, become more phase synchronized in 4-8 Hz after the entrainment of visual stimulus that maximally induces responses in LM. Directed information analysis reveals changes in the top-down functional connectivity between V1 and HVAs. Optogenetic inactivation of LM reduces post-stimulus oscillation peaks in V1 and impairs visual discrimination behavior. Our results demonstrate that 4-8 Hz familiarity-evoked oscillations are specific for the distinct visual features and are present in the corresponding HVAs, where they may be used for the inter-areal communication with V1 during memory-related behaviors.
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Affiliation(s)
- Yu Tang
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Catherine Gervais
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rylann Moffitt
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Sanghamitra Nareddula
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Michael Zimmermann
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Yididiya Y Nadew
- Department of Computer Sciences, Iowa State University, Ames, IA 50011, USA
| | | | - Violeta Saldarriaga
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Paige Edens
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Alexander A Chubykin
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA.
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26
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Liu J, He Y, Lavoie A, Bouvier G, Liu BH. A direction-selective cortico-brainstem pathway adaptively modulates innate behaviors. Nat Commun 2023; 14:8467. [PMID: 38123558 PMCID: PMC10733370 DOI: 10.1038/s41467-023-42910-2] [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: 11/29/2022] [Accepted: 10/25/2023] [Indexed: 12/23/2023] Open
Abstract
Sensory cortices modulate innate behaviors through corticofugal projections targeting phylogenetically-old brainstem nuclei. However, the principles behind the functional connectivity of these projections remain poorly understood. Here, we show that in mice visual cortical neurons projecting to the optic-tract and dorsal-terminal nuclei (NOT-DTN) possess distinct response properties and anatomical connectivity, supporting the adaption of an essential innate eye movement, the optokinetic reflex (OKR). We find that these corticofugal neurons are enriched in specific visual areas, and they prefer temporo-nasal visual motion, matching the direction bias of downstream NOT-DTN neurons. Remarkably, continuous OKR stimulation selectively enhances the activity of these temporo-nasally biased cortical neurons, which can efficiently promote OKR plasticity. Lastly, we demonstrate that silencing downstream NOT-DTN neurons, which project specifically to the inferior olive-a key structure in oculomotor plasticity, impairs the cortical modulation of OKR and OKR plasticity. Our results unveil a direction-selective cortico-brainstem pathway that adaptively modulates innate behaviors.
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Affiliation(s)
- Jiashu Liu
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, L5L 1C6, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Yingtian He
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, L5L 1C6, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Andreanne Lavoie
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, L5L 1C6, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Guy Bouvier
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, 91400, Saclay, France
| | - Bao-Hua Liu
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, L5L 1C6, Canada.
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.
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27
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Baruchin LJ, Alleman M, Schröder S. Reward Modulates Visual Responses in the Superficial Superior Colliculus of Mice. J Neurosci 2023; 43:8663-8680. [PMID: 37879894 PMCID: PMC7615379 DOI: 10.1523/jneurosci.0089-23.2023] [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: 01/09/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 10/27/2023] Open
Abstract
The processing of sensory input is constantly adapting to behavioral demands and internal states. The drive to obtain reward, e.g., searching for water when thirsty, is a strong behavioral demand and associating the reward with its source, a certain environment or action, is paramount for survival. Here, we show that water reward increases subsequent visual activity in the superficial layers of the superior colliculus (SC), which receive direct input from the retina and belong to the earliest stages of visual processing. We trained mice of either sex to perform a visual decision task and recorded the activity of neurons in the SC using two-photon calcium imaging and high-density electrophysiological recordings. Responses to visual stimuli in around 20% of visually responsive neurons in the superficial SC were affected by reward delivered in the previous trial. Reward mostly increased visual responses independent from modulations due to pupil size changes. The modulation of visual responses by reward could not be explained by movements like licking. It was specific to responses to the following visual stimulus, independent of slow fluctuations in neural activity and independent of how often the stimulus was previously rewarded. Electrophysiological recordings confirmed these results and revealed that reward affected the early phase of the visual response around 80 ms after stimulus onset. Modulation of visual responses by reward, but not pupil size, significantly improved the performance of a population decoder to detect visual stimuli, indicating the relevance of reward modulation for the visual performance of the animal.SIGNIFICANCE STATEMENT To learn which actions lead to food, water, or safety, it is necessary to integrate the receiving of reward with sensory stimuli related to the reward. Cortical stages of sensory processing have been shown to represent stimulus-reward associations. Here, we show, however, that reward influences neurons at a much earlier stage of sensory processing, the superior colliculus (SC), receiving direct input from the retina. Visual responses were increased shortly after the animal received the water reward, which led to an improved stimulus signal in the population of these visual neurons. Reward modulation of early visual responses may thus improve perception of visual environments predictive of reward.
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Affiliation(s)
- Liad J Baruchin
- School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom
| | - Matteo Alleman
- Institute of Ophthalmology, University College London, London WC1E 6BT, United Kingdom
| | - Sylvia Schröder
- School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom
- Institute of Ophthalmology, University College London, London WC1E 6BT, United Kingdom
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28
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Gurnani H, Cayco Gajic NA. Signatures of task learning in neural representations. Curr Opin Neurobiol 2023; 83:102759. [PMID: 37708653 DOI: 10.1016/j.conb.2023.102759] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/28/2023] [Accepted: 07/20/2023] [Indexed: 09/16/2023]
Abstract
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level.
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Affiliation(s)
- Harsha Gurnani
- Department of Biology, University of Washington, Seattle, WA, USA. https://twitter.com/HarshaGurnani
| | - N Alex Cayco Gajic
- Laboratoire de Neuroscience Cognitives, Ecole Normale Supérieure, Université PSL, Paris, France.
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29
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Zhang A, Zador AM. Neurons in the primary visual cortex of freely moving rats encode both sensory and non-sensory task variables. PLoS Biol 2023; 21:e3002384. [PMID: 38048367 PMCID: PMC10721203 DOI: 10.1371/journal.pbio.3002384] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/14/2023] [Accepted: 10/17/2023] [Indexed: 12/06/2023] Open
Abstract
Neurons in primary visual cortex (area V1) are strongly driven by both sensory stimuli and non-sensory events. However, although the representation of sensory stimuli has been well characterized, much less is known about the representation of non-sensory events. Here, we characterize the specificity and organization of non-sensory representations in rat V1 during a freely moving visual decision task. We find that single neurons encode diverse combinations of task features simultaneously and across task epochs. Despite heterogeneity at the level of single neuron response patterns, both visual and nonvisual task variables could be reliably decoded from small neural populations (5 to 40 units) throughout a trial. Interestingly, in animals trained to make an auditory decision following passive observation of a visual stimulus, some but not all task features could also be decoded from V1 activity. Our results support the view that even in V1-the earliest stage of the cortical hierarchy-bottom-up sensory information may be combined with top-down non-sensory information in a task-dependent manner.
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Affiliation(s)
- Anqi Zhang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
- Cold Spring Harbor Laboratory School of Biological Sciences, Cold Spring Harbor, New York, United States of America
| | - Anthony M. Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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30
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Niraula S, Hauser WL, Rouse AG, Subramanian J. Repeated passive visual experience modulates spontaneous and non-familiar stimuli-evoked neural activity. Sci Rep 2023; 13:20907. [PMID: 38017135 PMCID: PMC10684504 DOI: 10.1038/s41598-023-47957-1] [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: 02/20/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023] Open
Abstract
Familiarity creates subjective memory of repeated innocuous experiences, reduces neural and behavioral responsiveness to those experiences, and enhances novelty detection. The neural correlates of the internal model of familiarity and the cellular mechanisms of enhanced novelty detection following multi-day repeated passive experience remain elusive. Using the mouse visual cortex as a model system, we test how the repeated passive experience of a 45° orientation-grating stimulus for multiple days alters spontaneous and non-familiar stimuli evoked neural activity in neurons tuned to familiar or non-familiar stimuli. We found that familiarity elicits stimulus competition such that stimulus selectivity reduces in neurons tuned to the familiar 45° stimulus; it increases in those tuned to the 90° stimulus but does not affect neurons tuned to the orthogonal 135° stimulus. Furthermore, neurons tuned to orientations 45° apart from the familiar stimulus dominate local functional connectivity. Interestingly, responsiveness to natural images, which consists of familiar and non-familiar orientations, increases subtly in neurons that exhibit stimulus competition. We also show the similarity between familiar grating stimulus-evoked and spontaneous activity increases, indicative of an internal model of altered experience.
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Affiliation(s)
- Suraj Niraula
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, 66045, USA
| | - William L Hauser
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, 66045, USA
| | - Adam G Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, 66103, USA
| | - Jaichandar Subramanian
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, 66045, USA.
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31
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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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32
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Marmor O, Pollak Y, Doron C, Helmchen F, Gilad A. History information emerges in the cortex during learning. eLife 2023; 12:e83702. [PMID: 37921842 PMCID: PMC10624423 DOI: 10.7554/elife.83702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 10/23/2023] [Indexed: 11/04/2023] Open
Abstract
We learn from our experience but the underlying neuronal mechanisms incorporating past information to facilitate learning is relatively unknown. Specifically, which cortical areas encode history-related information and how is this information modulated across learning? To study the relationship between history and learning, we continuously imaged cortex-wide calcium dynamics as mice learn to use their whiskers to discriminate between two different textures. We mainly focused on comparing the same trial type with different trial history, that is, a different preceding trial. We found trial history information in barrel cortex (BC) during stimulus presentation. Importantly, trial history in BC emerged only as the mouse learned the task. Next, we also found learning-dependent trial history information in rostrolateral (RL) association cortex that emerges before stimulus presentation, preceding activity in BC. Trial history was also encoded in other cortical areas and was not related to differences in body movements. Interestingly, a binary classifier could discriminate trial history at the single trial level just as well as current information both in BC and RL. These findings suggest that past experience emerges in the cortex around the time of learning, starting from higher-order association area RL and propagating down (i.e., top-down projection) to lower-order BC where it can be integrated with incoming sensory information. This integration between the past and present may facilitate learning.
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Affiliation(s)
- Odeya Marmor
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
| | - Yael Pollak
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
| | - Chen Doron
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
| | - Fritjof Helmchen
- Brain Research Institute, University of ZurichZurichSwitzerland
- Neuroscience Center ZurichZurichSwitzerland
| | - Ariel Gilad
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of JerusalemJerusalemIsrael
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33
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Antono JE, Dang S, Auksztulewicz R, Pooresmaeili A. Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex. J Neurosci 2023; 43:7361-7375. [PMID: 37684031 PMCID: PMC10621764 DOI: 10.1523/jneurosci.0355-23.2023] [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: 02/24/2023] [Revised: 07/30/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023] Open
Abstract
Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.
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Affiliation(s)
- Jessica Emily Antono
- Perception and Cognition Lab, European Neuroscience Institute Goettingen-A Joint Initiative of the University Medical Center Goettingen and the Max-Planck-Society, Germany, Goettingen, 37077, Germany
| | - Shilpa Dang
- Perception and Cognition Lab, European Neuroscience Institute Goettingen-A Joint Initiative of the University Medical Center Goettingen and the Max-Planck-Society, Germany, Goettingen, 37077, Germany
- School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, Karwar, Jodhpur 342030, India
| | - Ryszard Auksztulewicz
- Center for Cognitive Neuroscience Berlin, Free University Berlin, Berlin, 14195, Germany
| | - Arezoo Pooresmaeili
- Perception and Cognition Lab, European Neuroscience Institute Goettingen-A Joint Initiative of the University Medical Center Goettingen and the Max-Planck-Society, Germany, Goettingen, 37077, Germany
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34
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Hayden DJ, Finnie PSB, Thomazeau A, Li AY, Cooke SF, Bear MF. Electrophysiological Signatures of Visual Recognition Memory across All Layers of Mouse V1. J Neurosci 2023; 43:7307-7321. [PMID: 37714707 PMCID: PMC10621768 DOI: 10.1523/jneurosci.0090-23.2023] [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: 01/16/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
In mouse primary visual cortex (V1), familiar stimuli evoke significantly altered responses when compared with novel stimuli. This stimulus-selective response plasticity (SRP) was described originally as an increase in the magnitude of visual evoked potentials (VEPs) elicited in layer 4 (L4) by familiar phase-reversing grating stimuli. SRP is dependent on NMDA receptors (NMDARs) and has been hypothesized to reflect potentiation of thalamocortical (TC) synapses in L4. However, recent evidence indicates that the synaptic modifications that manifest as SRP do not occur on L4 principal cells. To shed light on where and how SRP is induced and expressed in male and female mice, the present study had three related aims: (1) to confirm that NMDAR are required specifically in glutamatergic principal neurons of V1, (2) to investigate the consequences of deleting NMDAR specifically in L6, and (3) to use translaminar electrophysiological recordings to characterize SRP expression in different layers of V1. We find that knock-out (KO) of NMDAR in L6 principal neurons disrupts SRP. Current-source density (CSD) analysis of the VEP depth profile shows augmentation of short latency current sinks in layers 3, 4, and 6 in response to phase reversals of familiar stimuli. Multiunit recordings demonstrate that increased peak firing occurs in response to phase reversals of familiar stimuli across all layers, but that activity between phase reversals is suppressed. Together, these data reveal important aspects of the underlying phenomenology of SRP and generate new hypotheses for the expression of experience-dependent plasticity in V1.SIGNIFICANCE STATEMENT Repeated exposure to stimuli that portend neither reward nor punishment leads to behavioral habituation, enabling organisms to dedicate attention to novel or otherwise significant features of the environment. The neural basis of this process, which is so often dysregulated in neurologic and psychiatric disorders, remains poorly understood. Learning and memory of stimulus familiarity can be studied in mouse visual cortex by measuring electrophysiological responses to simple phase-reversing grating stimuli. The current study advances knowledge of this process by documenting changes in visual evoked potentials (VEPs), neuronal spiking activity, and oscillations in the local field potentials (LFPs) across all layers of mouse visual cortex. In addition, we identify a key contribution of a specific population of neurons in layer 6 (L6) of visual cortex.
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Affiliation(s)
- Dustin J Hayden
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Peter S B Finnie
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Aurore Thomazeau
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Alyssa Y Li
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Biochemistry Program, Wellesley College, Wellesley, Massachusetts 02481
| | - Samuel F Cooke
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Mark F Bear
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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Hajnal MA, Tran D, Einstein M, Martelo MV, Safaryan K, Polack PO, Golshani P, Orbán G. Continuous multiplexed population representations of task context in the mouse primary visual cortex. Nat Commun 2023; 14:6687. [PMID: 37865648 PMCID: PMC10590415 DOI: 10.1038/s41467-023-42441-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/10/2023] [Indexed: 10/23/2023] Open
Abstract
Effective task execution requires the representation of multiple task-related variables that determine how stimuli lead to correct responses. Even the primary visual cortex (V1) represents other task-related variables such as expectations, choice, and context. However, it is unclear how V1 can flexibly accommodate these variables without interfering with visual representations. We trained mice on a context-switching cross-modal decision task, where performance depends on inferring task context. We found that the context signal that emerged in V1 was behaviorally relevant as it strongly covaried with performance, independent from movement. Importantly, this signal was integrated into V1 representation by multiplexing visual and context signals into orthogonal subspaces. In addition, auditory and choice signals were also multiplexed as these signals were orthogonal to the context representation. Thus, multiplexing allows V1 to integrate visual inputs with other sensory modalities and cognitive variables to avoid interference with the visual representation while ensuring the maintenance of task-relevant variables.
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Affiliation(s)
- Márton Albert Hajnal
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary
| | - Duy Tran
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Albert Einstein College of Medicine, New York, NY, 10461, USA
| | - Michael Einstein
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Mauricio Vallejo Martelo
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Karen Safaryan
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Pierre-Olivier Polack
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- West Los Angeles VA Medical Center, CA, 90073, Los Angeles, USA.
| | - Gergő Orbán
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary.
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Malone TJ, Tien NW, Ma Y, Cui L, Lyu S, Wang G, Nguyen D, Zhang K, Myroshnychenko MV, Tyan J, Gordon JA, Kupferschmidt DA, Gu Y. A consistent map in the medial entorhinal cortex supports spatial memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.560254. [PMID: 37986767 PMCID: PMC10659391 DOI: 10.1101/2023.09.30.560254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.
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Affiliation(s)
- Taylor J. Malone
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- These authors contributed equally to this work
| | - Nai-Wen Tien
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Current address: Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- These authors contributed equally to this work
| | - Yan Ma
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- These authors contributed equally to this work
| | - Lian Cui
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shangru Lyu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Garret Wang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Duc Nguyen
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Current address: Center of Neural Science, New York University, New York, NY, USA
| | - Kai Zhang
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Anesthesiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Maxym V. Myroshnychenko
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jean Tyan
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joshua A. Gordon
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
- Office of the Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - David A. Kupferschmidt
- Integrative Neuroscience Section, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yi Gu
- Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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Dai J, Sun QQ. Learning induced neuronal identity switch in the superficial layers of the primary somatosensory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.30.555603. [PMID: 37693620 PMCID: PMC10491147 DOI: 10.1101/2023.08.30.555603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
During learning, multi-dimensional inputs are integrated within the sensory cortices. However, the strategies by which the sensory cortex employs to achieve learning remains poorly understood. We studied the sensory cortical neuronal coding of trace eyeblink conditioning (TEC) in head-fixed, freely running mice, where whisker deflection was used as a conditioned stimulus (CS) and an air puff to the cornea delivered after an interval was used as unconditioned stimulus (US). After training, mice learned the task with a set of stereotypical behavioral changes, most prominent ones include prolonged closure of eyelids, and increased reverse running between CS and US onset. The local blockade of the primary somatosensory cortex (S1) activities with muscimol abolished the behavior learning suggesting that S1 is required for the TEC. In naive animals, based on the response properties to the CS and US, identities of the small proportion (~20%) of responsive primary neurons (PNs) were divided into two subtypes: CR (i.e. CS-responsive) and UR neurons (i.e. US-responsive). After animals learned the task, identity of CR and UR neurons changed: while the CR neurons are less responsive to CS, UR neurons gain responsiveness to CS, a new phenomenon we defined as 'learning induced neuronal identity switch (LINIS)'. To explore the potential mechanisms underlying LINIS, we found that systemic and local (i.e. in S1) administration of the nicotinic receptor antagonist during TEC training blocked the LINIS, and concomitantly disrupted the behavior learning. Additionally, we monitored responses of two types of cortical interneurons (INs) and observed that the responses of the somatostatin-expressing (SST), but not parvalbumin-expressing (PV) INs are negatively correlated with the learning performance, suggesting that SST-INs contribute to the LINIS. Thus, we conclude that L2/3 PNs in S1 encode perceptual learning by LINIS like mechanisms, and cholinergic pathways and cortical SST interneurons are involved in the formation of LINIS.
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Affiliation(s)
- Jiaman Dai
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY82071, USA
- Wyoming Sensory Biology Center of Biomedical Research Excellence, University of Wyoming, Laramie, WY82071, USA
| | - Qian-Quan Sun
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY82071, USA
- Wyoming Sensory Biology Center of Biomedical Research Excellence, University of Wyoming, Laramie, WY82071, USA
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Niraula S, Hauser WL, Rouse AG, Subramanian J. Repeated passive visual experience modulates spontaneous and non-familiar stimulievoked neural activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529278. [PMID: 36865208 PMCID: PMC9980096 DOI: 10.1101/2023.02.21.529278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Familiarity creates subjective memory of repeated innocuous experiences, reduces neural and behavioral responsiveness to those experiences, and enhances novelty detection. The neural correlates of the internal model of familiarity and the cellular mechanisms of enhanced novelty detection following multi-day repeated passive experience remain elusive. Using the mouse visual cortex as a model system, we test how the repeated passive experience of a 45° orientation-grating stimulus for multiple days alters spontaneous and non-familiar stimuli evoked neural activity in neurons tuned to familiar or non-familiar stimuli. We found that familiarity elicits stimulus competition such that stimulus selectivity reduces in neurons tuned to the familiar 45° stimulus; it increases in those tuned to the 90° stimulus but does not affect neurons tuned to the orthogonal 135° stimulus. Furthermore, neurons tuned to orientations 45° apart from the familiar stimulus dominate local functional connectivity. Interestingly, responsiveness to natural images, which consists of familiar and non-familiar orientations, increases subtly in neurons that exhibit stimulus competition. We also show the similarity between familiar grating stimulus-evoked and spontaneous activity increases, indicative of an internal model of altered experience.
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Affiliation(s)
- Suraj Niraula
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS 66045, USA
| | - William L. Hauser
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS 66045, USA
| | - Adam G. Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Jaichandar Subramanian
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS 66045, USA
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Wang F, Nguyen QTH, Kaneshiro B, Hasak L, Wang AM, Toomarian EY, Norcia AM, McCandliss BD. Lexical and sublexical cortical tuning for print revealed by Steady-State Visual Evoked Potentials (SSVEPs) in early readers. Dev Sci 2023; 26:e13352. [PMID: 36413170 PMCID: PMC10881121 DOI: 10.1111/desc.13352] [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: 12/30/2021] [Revised: 10/27/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022]
Abstract
There are multiple levels of processing relevant to reading that vary in their visual, sublexical, and lexical orthographic processing demands. Segregating distinct cortical sources for each of these levels has been challenging in EEG studies of early readers. To address this challenge, we applied recent advances in analyzing high-density EEG using Steady-State Visual Evoked Potentials (SSVEPs) via data-driven Reliable Components Analysis (RCA) in a group of early readers spanning from kindergarten to second grade. Three controlled stimulus contrasts-familiar words versus unfamiliar pseudofonts, familiar words versus pseudowords, and pseudowords versus nonwords-were used to isolate coarse print tuning, lexical processing, and sublexical orthography-related processing, respectively. First, three overlapping yet distinct neural sources-left vOT, dorsal parietal, and primary visual cortex were revealed underlying coarse print tuning. Second, we segregated distinct cortical sources for the other two levels of processing: lexical fine tuning over occipito-temporal/parietal regions; sublexical orthographic fine tuning over left occipital regions. Finally, exploratory group analyses based on children's reading fluency suggested that coarse print tuning emerges early even in children with limited reading knowledge, while sublexical and higher-level lexical processing emerge only in children with sufficient reading knowledge. RESEARCH HIGHLIGHTS: Cognitive processes underlying coarse print tuning, sublexical, and lexical fine tuning were examined in beginning readers. Three overlapping yet distinct neural sources-left ventral occipito-temporal (vOT), left temporo-parietal, and primary visual cortex-were revealed underlying coarse print tuning. Responses to sublexical orthographic fine tuning were found over left occipital regions, while responses to higher-level linguistic fine tuning were found over occipito-temporal/parietal regions. Exploratory group analyses suggested that coarse print tuning emerges in children with limited reading knowledge, while sublexical and higher-level linguistic fine tuning effects emerge in children with sufficient reading knowledge.
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Affiliation(s)
- Fang Wang
- Graduate School of Education, Stanford University, Stanford, California, USA
| | | | - Blair Kaneshiro
- Graduate School of Education, Stanford University, Stanford, California, USA
| | - Lindsey Hasak
- Graduate School of Education, Stanford University, Stanford, California, USA
| | - Angie M. Wang
- Graduate School of Education, Stanford University, Stanford, California, USA
| | - Elizabeth Y. Toomarian
- Graduate School of Education, Stanford University, Stanford, California, USA
- Synapse School, Menlo Park, California, USA
| | - Anthony M. Norcia
- Department of Psychology, Stanford University, Stanford, California, USA
- Wu Tsai Neurosciences Institute, Stanford, California, USA
| | - Bruce D. McCandliss
- Graduate School of Education, Stanford University, Stanford, California, USA
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Si YG, Su WX, Chen XD, Li ZY, Yan B, Zhang JY. Emerging V1 neuronal ensembles with enhanced connectivity after associative learning. Front Neurosci 2023; 17:1176253. [PMID: 37456996 PMCID: PMC10346858 DOI: 10.3389/fnins.2023.1176253] [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: 02/28/2023] [Accepted: 06/09/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction The visual stimulus-specific responses in the primary visual cortex (V1) undergo plastic changes after associative learning. During the learning process, neuronal ensembles, defined as groups of coactive neurons, are well known to be related to learning and memory. However, it remains unclear what effect learning has on ensembles, and which neuronal subgroups within those ensembles play a key role in associative learning. Methods We used two-photon calcium imaging in mice to record the activity of V1 neurons before and after fear conditioning associated with a visual cue (blue light). We first defined neuronal ensembles by thresholding their functional connectivity in response to blue (conditioned) or green (control) light. We defined neurons that existed both before and after conditioning as stable neurons. Neurons which were recruited after conditioning were defined as new neurons. The graph theory-based analysis was performed to quantify the changes in connectivity within ensembles after conditioning. Results A significant enhancement in the connectivity strength (the average correlation with other neurons) was observed in the blue ensembles after conditioning. We found that stable neurons within the blue ensembles showed a significantly smaller clustering coefficient (the value represented the degree of interconnectedness among a node's neighbors) after conditioning than they were before conditioning. Additionally, new neurons within the blue ensembles had a larger clustering coefficient, similar relative degree (the value represented the number of functional connections between neurons) and connectivity strength compared to stable neurons in the same ensembles. Discussion Overall, our results demonstrated that the plastic changes caused by conditioning occurred in subgroups of neurons in the ensembles. Moreover, new neurons from conditioned ensembles may play a crucial role in memory formation, as they exhibited not only similar connection competence in relative degree and connectivity strength as stable neurons, but also showed a significantly larger clustering coefficient compared to the stable neurons within the same ensembles after conditioning.
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Affiliation(s)
- Yue-Guang Si
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Wen-Xin Su
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Eye & ENT Hospital, Fudan University, Shanghai, China
- Department of Psychology, University of Essex, Colchester, United Kingdom
| | - Xing-Dong Chen
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Ze-Yu Li
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Biao Yan
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Jia-Yi Zhang
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Eye & ENT Hospital, Fudan University, Shanghai, China
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Pancholi R, Ryan L, Peron S. Learning in a sensory cortical microstimulation task is associated with elevated representational stability. Nat Commun 2023; 14:3860. [PMID: 37385989 PMCID: PMC10310840 DOI: 10.1038/s41467-023-39542-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 06/16/2023] [Indexed: 07/01/2023] Open
Abstract
Sensory cortical representations can be highly dynamic, raising the question of how representational stability impacts learning. We train mice to discriminate the number of photostimulation pulses delivered to opsin-expressing pyramidal neurons in layer 2/3 of primary vibrissal somatosensory cortex. We simultaneously track evoked neural activity across learning using volumetric two-photon calcium imaging. In well-trained animals, trial-to-trial fluctuations in the amount of photostimulus-evoked activity predicted animal choice. Population activity levels declined rapidly across training, with the most active neurons showing the largest declines in responsiveness. Mice learned at varied rates, with some failing to learn the task in the time provided. The photoresponsive population showed greater instability both within and across behavioral sessions among animals that failed to learn. Animals that failed to learn also exhibited a faster deterioration in stimulus decoding. Thus, greater stability in the stimulus response is associated with learning in a sensory cortical microstimulation task.
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Affiliation(s)
- Ravi Pancholi
- Center for Neural Science, New York University, 4 Washington Place Rm. 621, New York, NY, 10003, USA
| | - Lauren Ryan
- Center for Neural Science, New York University, 4 Washington Place Rm. 621, New York, NY, 10003, USA
| | - Simon Peron
- Center for Neural Science, New York University, 4 Washington Place Rm. 621, New York, NY, 10003, USA.
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Wang BA, Veismann M, Banerjee A, Pleger B. Human orbitofrontal cortex signals decision outcomes to sensory cortex during behavioral adaptations. Nat Commun 2023; 14:3552. [PMID: 37322004 PMCID: PMC10272188 DOI: 10.1038/s41467-023-38671-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 05/10/2023] [Indexed: 06/17/2023] Open
Abstract
The ability to respond flexibly to an ever-changing environment relies on the orbitofrontal cortex (OFC). However, how the OFC associates sensory information with predicted outcomes to enable flexible sensory learning in humans remains elusive. Here, we combine a probabilistic tactile reversal learning task with functional magnetic resonance imaging (fMRI) to investigate how lateral OFC (lOFC) interacts with the primary somatosensory cortex (S1) to guide flexible tactile learning in humans. fMRI results reveal that lOFC and S1 exhibit distinct task-dependent engagement: while the lOFC responds transiently to unexpected outcomes immediately following reversals, S1 is persistently engaged during re-learning. Unlike the contralateral stimulus-selective S1, activity in ipsilateral S1 mirrors the outcomes of behavior during re-learning, closely related to top-down signals from lOFC. These findings suggest that lOFC contributes to teaching signals to dynamically update representations in sensory areas, which implement computations critical for adaptive behavior.
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Affiliation(s)
- Bin A Wang
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
| | - Maike Veismann
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
| | - Abhishek Banerjee
- Adaptive Decisions Lab, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
| | - Burkhard Pleger
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany.
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany.
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43
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Marshel JH. Visual cortex: How mice learn to detect entirely novel inputs. Curr Biol 2023; 33:R449-R452. [PMID: 37279668 DOI: 10.1016/j.cub.2023.04.064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A new study has revealed that neural amplification in mouse primary visual cortex substantially increases between training sessions as mice learn to detect novel optogenetic input directly into visual cortex, suggesting consolidation and recurrent network plasticity contribute to learning the behavior.
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Affiliation(s)
- James H Marshel
- CNC Department, Stanford University, Stanford, CA 94305, USA.
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Wyrick DG, Cain N, Larsen RS, Lecoq J, Valley M, Ahmed R, Bowlus J, Boyer G, Caldejon S, Casal L, Chvilicek M, DePartee M, Groblewski PA, Huang C, Johnson K, Kato I, Larkin J, Lee E, Liang E, Luviano J, Mace K, Nayan C, Nguyen T, Reding M, Seid S, Sevigny J, Stoecklin M, Williford A, Choi H, Garrett M, Mazzucato L. Differential encoding of temporal context and expectation under representational drift across hierarchically connected areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.02.543483. [PMID: 37333203 PMCID: PMC10274646 DOI: 10.1101/2023.06.02.543483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The classic view that neural populations in sensory cortices preferentially encode responses to incoming stimuli has been strongly challenged by recent experimental studies. Despite the fact that a large fraction of variance of visual responses in rodents can be attributed to behavioral state and movements, trial-history, and salience, the effects of contextual modulations and expectations on sensory-evoked responses in visual and association areas remain elusive. Here, we present a comprehensive experimental and theoretical study showing that hierarchically connected visual and association areas differentially encode the temporal context and expectation of naturalistic visual stimuli, consistent with the theory of hierarchical predictive coding. We measured neural responses to expected and unexpected sequences of natural scenes in the primary visual cortex (V1), the posterior medial higher order visual area (PM), and retrosplenial cortex (RSP) using 2-photon imaging in behaving mice collected through the Allen Institute Mindscope's OpenScope program. We found that information about image identity in neural population activity depended on the temporal context of transitions preceding each scene, and decreased along the hierarchy. Furthermore, our analyses revealed that the conjunctive encoding of temporal context and image identity was modulated by expectations of sequential events. In V1 and PM, we found enhanced and specific responses to unexpected oddball images, signaling stimulus-specific expectation violation. In contrast, in RSP the population response to oddball presentation recapitulated the missing expected image rather than the oddball image. These differential responses along the hierarchy are consistent with classic theories of hierarchical predictive coding whereby higher areas encode predictions and lower areas encode deviations from expectation. We further found evidence for drift in visual responses on the timescale of minutes. Although activity drift was present in all areas, population responses in V1 and PM, but not in RSP, maintained stable encoding of visual information and representational geometry. Instead we found that RSP drift was independent of stimulus information, suggesting a role in generating an internal model of the environment in the temporal domain. Overall, our results establish temporal context and expectation as substantial encoding dimensions in the visual cortex subject to fast representational drift and suggest that hierarchically connected areas instantiate a predictive coding mechanism.
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Affiliation(s)
- David G Wyrick
- Department of Biology and Institute of Neuroscience, University of Oregon
- Allen Institute, Mindscope program, University of Oregon
| | - Nicholas Cain
- Allen Institute, Mindscope program, University of Oregon
| | | | - Jérôme Lecoq
- Allen Institute, Mindscope program, University of Oregon
| | - Matthew Valley
- Allen Institute, Mindscope program, University of Oregon
| | - Ruweida Ahmed
- Allen Institute, Mindscope program, University of Oregon
| | - Jessica Bowlus
- Allen Institute, Mindscope program, University of Oregon
| | | | | | - Linzy Casal
- Allen Institute, Mindscope program, University of Oregon
| | | | | | | | - Cindy Huang
- Allen Institute, Mindscope program, University of Oregon
| | | | - India Kato
- Allen Institute, Mindscope program, University of Oregon
| | - Josh Larkin
- Allen Institute, Mindscope program, University of Oregon
| | - Eric Lee
- Allen Institute, Mindscope program, University of Oregon
| | | | | | - Kyla Mace
- Allen Institute, Mindscope program, University of Oregon
| | - Chelsea Nayan
- Allen Institute, Mindscope program, University of Oregon
| | | | - Melissa Reding
- Allen Institute, Mindscope program, University of Oregon
| | - Sam Seid
- Allen Institute, Mindscope program, University of Oregon
| | - Joshua Sevigny
- Allen Institute, Mindscope program, University of Oregon
| | | | - Ali Williford
- Allen Institute, Mindscope program, University of Oregon
| | - Hannah Choi
- Allen Institute, Mindscope program, University of Oregon
- School of Mathematics, Georgia Institute of Technology, University of Oregon
| | - Marina Garrett
- Allen Institute, Mindscope program, University of Oregon
| | - Luca Mazzucato
- Department of Biology and Institute of Neuroscience, University of Oregon
- Department of Mathematics and Physics, University of Oregon
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45
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Akitake B, Douglas HM, LaFosse PK, Beiran M, Deveau CE, O'Rawe J, Li AJ, Ryan LN, Duffy SP, Zhou Z, Deng Y, Rajan K, Histed MH. Amplified cortical neural responses as animals learn to use novel activity patterns. Curr Biol 2023; 33:2163-2174.e4. [PMID: 37148876 DOI: 10.1016/j.cub.2023.04.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 02/09/2023] [Accepted: 04/14/2023] [Indexed: 05/08/2023]
Abstract
Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting that the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found that their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task; therefore, these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.
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Affiliation(s)
- Bradley Akitake
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hannah M Douglas
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Paul K LaFosse
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Manuel Beiran
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ciana E Deveau
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jonathan O'Rawe
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Anna J Li
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lauren N Ryan
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Samuel P Duffy
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhishang Zhou
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yanting Deng
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kanaka Rajan
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mark H Histed
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA.
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46
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Schaffner J, Bao SD, Tobler PN, Hare TA, Polania R. Sensory perception relies on fitness-maximizing codes. Nat Hum Behav 2023:10.1038/s41562-023-01584-y. [PMID: 37106080 PMCID: PMC10365992 DOI: 10.1038/s41562-023-01584-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/09/2023] [Indexed: 04/29/2023]
Abstract
Sensory information encoded by humans and other organisms is generally presumed to be as accurate as their biological limitations allow. However, perhaps counterintuitively, accurate sensory representations may not necessarily maximize the organism's chances of survival. To test this hypothesis, we developed a unified normative framework for fitness-maximizing encoding by combining theoretical insights from neuroscience, computer science, and economics. Behavioural experiments in humans revealed that sensory encoding strategies are flexibly adapted to promote fitness maximization, a result confirmed by deep neural networks with information capacity constraints trained to solve the same task as humans. Moreover, human functional MRI data revealed that novel behavioural goals that rely on object perception induce efficient stimulus representations in early sensory structures. These results suggest that fitness-maximizing rules imposed by the environment are applied at early stages of sensory processing in humans and machines.
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Affiliation(s)
- Jonathan Schaffner
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, Zurich, Switzerland
| | - Sherry Dongqi Bao
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, Zurich, Switzerland
| | - Philippe N Tobler
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, Zurich, Switzerland
| | - Todd A Hare
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, Zurich, Switzerland.
| | - Rafael Polania
- Neuroscience Center Zurich, Zurich, Switzerland.
- Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
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47
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Kira S, Safaai H, Morcos AS, Panzeri S, Harvey CD. A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nat Commun 2023; 14:2121. [PMID: 37055431 PMCID: PMC10102117 DOI: 10.1038/s41467-023-37804-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/30/2023] [Indexed: 04/15/2023] Open
Abstract
Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.
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Affiliation(s)
- Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ari S Morcos
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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48
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Nair S, Szaflarski JP, Wang Y, Pizarro D, Killen JF, Allendorfer JB. Assessing dynamic brain activity during verbal associative learning using MEG/fMRI co-processing. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2022.100154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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49
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Ananthamurthy KG, Bhalla US. Synthetic Data Resource and Benchmarks for Time Cell Analysis and Detection Algorithms. eNeuro 2023; 10:ENEURO.0007-22.2023. [PMID: 36823166 PMCID: PMC10027052 DOI: 10.1523/eneuro.0007-22.2023] [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: 01/02/2022] [Revised: 11/21/2022] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
Hippocampal CA1 cells take part in reliable, time-locked activity sequences in tasks that involve an association between temporally separated stimuli, in a manner that tiles the interval between the stimuli. Such cells have been termed time cells. Here, we adopt a first-principles approach to comparing diverse analysis and detection algorithms for identifying time cells. We generated synthetic activity datasets using calcium signals recorded in vivo from the mouse hippocampus using two-photon (2-P) imaging, as template response waveforms. We assigned known, ground truth values to perturbations applied to perfect activity signals, including noise, calcium event width, timing imprecision, hit trial ratio and background (untuned) activity. We tested a range of published and new algorithms and their variants on this dataset. We find that most algorithms correctly classify over 80% of cells, but have different balances between true and false positives, and different sensitivity to the five categories of perturbation. Reassuringly, most methods are reasonably robust to perturbations, including background activity, and show good concordance in classification of time cells. The same algorithms were also used to analyze and identify time cells in experimental physiology datasets recorded in vivo and most show good concordance.
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Affiliation(s)
- Kambadur G Ananthamurthy
- National Centre for Biological Sciences - Tata Institute of Fundamental Research, Bellary Road, Bengaluru - 560065, Karnataka, India
| | - Upinder S Bhalla
- National Centre for Biological Sciences - Tata Institute of Fundamental Research, Bellary Road, Bengaluru - 560065, Karnataka, India
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50
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Murphy SC, Godenzini L, Guzulaitis R, Lawrence AJ, Palmer LM. Cocaine regulates sensory filtering in cortical pyramidal neurons. Cell Rep 2023; 42:112122. [PMID: 36790932 DOI: 10.1016/j.celrep.2023.112122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 12/14/2022] [Accepted: 01/30/2023] [Indexed: 02/16/2023] Open
Abstract
Exposure to cocaine leads to robust changes in the structure and function of neurons within the mesocorticolimbic pathway. However, little is known about how cocaine influences the processing of information within the sensory cortex. We address this by using patch-clamp and juxtacellular voltage recordings and two-photon Ca2+ imaging in vivo to investigate the influence of acute cocaine exposure on layer 2/3 (L2/3) pyramidal neurons within the primary somatosensory cortex (S1). Here, cocaine dampens membrane potential state transitions and decreases spontaneous somatic action potentials and Ca2+ transients. In contrast to the uniform decrease in background spontaneous activity, cocaine has a heterogeneous influence on sensory encoding, increasing tactile-evoked responses in dendrites that do not typically encode sensory information and decreasing responses in those dendrites that are more reliable sensory encoders. Combined, these findings suggest that cocaine acts as a filter that suppresses background noise to selectively modulate incoming sensory information.
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Affiliation(s)
- Sean C Murphy
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia
| | - Luca Godenzini
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia
| | - Robertas Guzulaitis
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia; Life Sciences Center, Vilnius University, 10257 Vilnius, Lithuania
| | - Andrew J Lawrence
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia
| | - Lucy M Palmer
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia.
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