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Potter CT, Bassi CD, Runyan CA. Simultaneous interneuron labeling reveals cell type-specific, population-level interactions in cortex. iScience 2024; 27:110736. [PMID: 39280622 PMCID: PMC11399611 DOI: 10.1016/j.isci.2024.110736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/28/2024] [Accepted: 08/12/2024] [Indexed: 09/18/2024] Open
Abstract
Cortical interneurons shape network activity in cell type-specific ways, and interact with other cell types. These interactions are understudied, as current methods typically restrict in vivo labeling to one neuron type. Although post-hoc identification of many cell types has been accomplished, the method is not available to many labs. We present a method to distinguish two red fluorophores in vivo, allowing imaging of activity in somatostatin (SOM), parvalbumin (PV), and the rest of the neural population in mouse cortex. We compared population events in PV and SOM neurons and observed that local network states reflected the ratio of SOM to PV neuron activity, demonstrating the importance of simultaneous labeling to explain dynamics. Activity became sparser and less correlated when the ratio between SOM and PV activity was high. Our simple method can be flexibly applied to study interactions among any combination of distinct cell type populations across brain areas.
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Affiliation(s)
- Christian T. Potter
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Constanza D. Bassi
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Caroline A. Runyan
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, USA
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2
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Hasegawa M, Huang Z, Paricio-Montesinos R, Gründemann J. Network state changes in sensory thalamus represent learned outcomes. Nat Commun 2024; 15:7830. [PMID: 39244616 PMCID: PMC11380690 DOI: 10.1038/s41467-024-51868-8] [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: 08/24/2023] [Accepted: 08/16/2024] [Indexed: 09/09/2024] Open
Abstract
Thalamic brain areas play an important role in adaptive behaviors. Nevertheless, the population dynamics of thalamic relays during learning across sensory modalities remain unknown. Using a cross-modal sensory reward-associative learning paradigm combined with deep brain two-photon calcium imaging of large populations of auditory thalamus (medial geniculate body, MGB) neurons in male mice, we identified that MGB neurons are biased towards reward predictors independent of modality. Additionally, functional classes of MGB neurons aligned with distinct task periods and behavioral outcomes, both dependent and independent of sensory modality. During non-sensory delay periods, MGB ensembles developed coherent neuronal representation as well as distinct co-activity network states reflecting predicted task outcome. These results demonstrate flexible cross-modal ensemble coding in auditory thalamus during adaptive learning and highlight its importance in brain-wide cross-modal computations during complex behavior.
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Affiliation(s)
- Masashi Hasegawa
- German Center for Neurodegenerative Diseases (DZNE), Neural Circuit Computations, Bonn, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Ziyan Huang
- German Center for Neurodegenerative Diseases (DZNE), Neural Circuit Computations, Bonn, Germany
| | | | - Jan Gründemann
- German Center for Neurodegenerative Diseases (DZNE), Neural Circuit Computations, Bonn, Germany.
- Department of Biomedicine, University of Basel, Basel, Switzerland.
- University of Bonn, Faculty of Medicine, Bonn, Germany.
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3
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Gavenas J, Rutishauser U, Schurger A, Maoz U. Slow ramping emerges from spontaneous fluctuations in spiking neural networks. Nat Commun 2024; 15:7285. [PMID: 39179554 PMCID: PMC11344096 DOI: 10.1038/s41467-024-51401-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 08/05/2024] [Indexed: 08/26/2024] Open
Abstract
The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.
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Affiliation(s)
- Jake Gavenas
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Aaron Schurger
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA
- INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette, 91191, France
- Commissariat à l'Energie Atomique, Direction des Sciences du Vivant, I2BM, NeuroSpin Center, Gif sur Yvette, 91191, France
| | - Uri Maoz
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Fowler School of Engineering, Chapman University, Orange, CA, USA.
- Anderson School of Management, University of California, Los Angeles, CA, USA.
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4
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Roads BD, Love BC. The Dimensions of dimensionality. Trends Cogn Sci 2024:S1364-6613(24)00189-X. [PMID: 39153897 DOI: 10.1016/j.tics.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 08/19/2024]
Abstract
Cognitive scientists often infer multidimensional representations from data. Whether the data involve text, neuroimaging, neural networks, or human judgments, researchers frequently infer and analyze latent representational spaces (i.e., embeddings). However, the properties of a latent representation (e.g., prediction performance, interpretability, compactness) depend on the inference procedure, which can vary widely across endeavors. For example, dimensions are not always globally interpretable and the dimensionality of different embeddings may not be readily comparable. Moreover, the dichotomy between multidimensional spaces and purportedly richer representational formats, such as graph representations, is misleading. We review what the different notions of dimension in cognitive science imply for how these latent representations should be used and interpreted.
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Affiliation(s)
- Brett D Roads
- Department of Experimental Psychology, University College London, London, WC1E, UK.
| | - Bradley C Love
- Department of Experimental Psychology, University College London, London, WC1E, UK
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5
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Sihn D, Chae S, Kim SP. A method to find temporal structure of neuronal coactivity patterns with across-trial correlations. J Neurosci Methods 2024; 408:110172. [PMID: 38782124 DOI: 10.1016/j.jneumeth.2024.110172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/08/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND The across-trial correlation of neurons' coactivity patterns emerges to be important for information coding, but methods for finding their temporal structures remain largely unexplored. NEW METHOD In the present study, we propose a method to find time clusters in which coactivity patterns of neurons are correlated across trials. We transform the multidimensional neural activity at each timing into a coactivity pattern of binary states, and predict the coactivity patterns at different timings. We devise a method suitable for these coactivity pattern predictions, call general event prediction. Cross-temporal prediction accuracy is then used to estimate across-trial correlations between coactivity patterns at two timings. We extract time clusters from the cross-temporal prediction accuracy by a modified k-means algorithm. RESULTS The feasibility of the proposed method is verified through simulations based on ground truth. We apply the proposed method to a calcium imaging dataset recorded from the motor cortex of mice, and demonstrate time clusters of motor cortical coactivity patterns during a motor task. COMPARISON WITH EXISTING METHODS While the existing cosine similarity method, which does not account for across-trial correlation, shows temporal structures only for contralateral neural responses, the proposed method reveals those for both contralateral and ipsilateral neural responses, demonstrating the effect of across-trial correlations. CONCLUSIONS This study introduces a novel method for measuring the temporal structure of neuronal ensemble activity.
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Affiliation(s)
- Duho Sihn
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea
| | - Soyoung Chae
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea.
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6
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Rudelt L, González Marx D, Spitzner FP, Cramer B, Zierenberg J, Priesemann V. Signatures of hierarchical temporal processing in the mouse visual system. PLoS Comput Biol 2024; 20:e1012355. [PMID: 39173067 PMCID: PMC11373856 DOI: 10.1371/journal.pcbi.1012355] [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: 01/17/2024] [Revised: 09/04/2024] [Accepted: 07/23/2024] [Indexed: 08/24/2024] Open
Abstract
A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and correlation timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system under visual stimulation, while information-theoretic predictability decreases. Moreover, intrinsic timescales for spontaneous activity displayed a similar hierarchy, whereas the hierarchy of predictability was stimulus-dependent. We could reproduce these observations in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.
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Affiliation(s)
- Lucas Rudelt
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Daniel González Marx
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - F Paul Spitzner
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Benjamin Cramer
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Johannes Zierenberg
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Viola Priesemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience (BCCN), Göttingen, Germany
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7
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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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8
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Noel JP, Balzani E, Savin C, Angelaki DE. Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques. Nat Commun 2024; 15:5738. [PMID: 38982106 PMCID: PMC11233555 DOI: 10.1038/s41467-024-50203-5] [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: 07/30/2023] [Accepted: 07/02/2024] [Indexed: 07/11/2024] Open
Abstract
Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here, we have male macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the area's ability for cross-context decoding. Lastly, correlational analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between neurons in prefrontal cortex maintain a stable population code and context-invariant beliefs during naturalistic behavior.
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Affiliation(s)
- Jean-Paul Noel
- Center for Neural Science, New York University, New York City, NY, USA.
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.
| | - Edoardo Balzani
- Center for Neural Science, New York University, New York City, NY, USA
- Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Cristina Savin
- Center for Neural Science, New York University, New York City, NY, USA
| | - Dora E Angelaki
- Center for Neural Science, New York University, New York City, NY, USA
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9
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Park H, Arazi A, Talluri BC, Celotto M, Panzeri S, Stocker AA, Donner TH. Confirmation Bias through Selective Use of Evidence in Human Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.21.600060. [PMID: 38979146 PMCID: PMC11230165 DOI: 10.1101/2024.06.21.600060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Decision-makers often process new evidence selectively, depending on their current beliefs about the world. We asked whether such confirmation biases result from biases in the encoding of sensory evidence in the brain, or alternatively in the utilization of encoded evidence for behavior. Human participants estimated the source of a sequence of visual-spatial evidence samples while we measured cortical population activity with magnetoencephalography (MEG). Halfway through the sequence, participants were prompted to judge the more likely source category. Their processing of subsequent evidence depended on its consistency with the previously chosen category, but the encoding of evidence in cortical activity did not. Instead, the encoded evidence in parietal and primary visual cortex contributed less to the estimation report when that evidence was inconsistent with the previous choice. We conclude that confirmation bias originates from the way in which decision-makers utilize information encoded in the brain. This provides room for deliberative control.
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Affiliation(s)
- Hame Park
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg 20251, Germany
| | - Ayelet Arazi
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg 20251, Germany
| | - Bharath Chandra Talluri
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg 20251, Germany
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, USA
| | - Marco Celotto
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy
| | - Stefano Panzeri
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Alan A Stocker
- Department of Psychology, University of Pennsylvania, 3710 Hamilton walk Philadelphia, PA 19106 USA
| | - Tobias H Donner
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg 20251, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt-University Berlin, Philippstr. 13, Haus 6, 10115 Berlin
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10
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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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11
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Koren V, Emanuel AJ, Panzeri S. Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597979. [PMID: 38895477 PMCID: PMC11185787 DOI: 10.1101/2024.06.07.597979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
How do biological neural systems efficiently encode, transform and propagate information between the sensory periphery and the sensory cortex about sensory features evolving at different time scales? Are these computations efficient in normative information processing terms? While previous work has suggested that biologically plausible models of of such neural information processing may be implemented efficiently within a single processing layer, how such computations extend across several processing layers is less clear. Here, we model propagation of multiple time-varying sensory features across a sensory pathway, by extending the theory of efficient coding with spikes to efficient encoding, transformation and transmission of sensory signals. These computations are optimally realized by a multilayer spiking network with feedforward networks of spiking neurons (receptor layer) and recurrent excitatory-inhibitory networks of generalized leaky integrate-and-fire neurons (recurrent layers). Our model efficiently realizes a broad class of feature transformations, including positive and negative interaction across features, through specific and biologically plausible structures of feedforward connectivity. We find that mixing of sensory features in the activity of single neurons is beneficial because it lowers the metabolic cost at the network level. We apply the model to the somatosensory pathway by constraining it with parameters measured empirically and include in its last node, analogous to the primary somatosensory cortex (S1), two types of inhibitory neurons: parvalbumin-positive neurons realizing lateral inhibition, and somatostatin-positive neurons realizing winner-take-all inhibition. By implementing a negative interaction across stimulus features, this model captures several intriguing empirical observations from the somatosensory system of the mouse, including a decrease of sustained responses from subcortical networks to S1, a non-linear effect of the knock-out of receptor neuron types on the activity in S1, and amplification of weak signals from sensory neurons across the pathway.
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Affiliation(s)
- Veronika Koren
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Alan J Emanuel
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
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12
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Koçillari L, Lorenz GM, Engel NM, Celotto M, Curreli S, Malerba SB, Engel AK, Fellin T, Panzeri S. Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597303. [PMID: 38895197 PMCID: PMC11185652 DOI: 10.1101/2024.06.04.597303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.
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Affiliation(s)
- Loren Koçillari
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Gabriel Matías Lorenz
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Nicola Marie Engel
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Marco Celotto
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
| | | | - Simone Blanco Malerba
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Andreas K. Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | | | - Stefano Panzeri
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
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13
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Proca AM, Rosas FE, Luppi AI, Bor D, Crosby M, Mediano PAM. Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks. PLoS Comput Biol 2024; 20:e1012178. [PMID: 38829900 PMCID: PMC11175422 DOI: 10.1371/journal.pcbi.1012178] [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: 11/07/2023] [Revised: 06/13/2024] [Accepted: 05/18/2024] [Indexed: 06/05/2024] Open
Abstract
Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any subset) plays a key role in areas of the human brain linked with complex cognition. However, two questions remain unanswered: (a) how and why a cognitive system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as networks learn multiple diverse tasks, and that in tasks requiring integration of multiple sources, performance critically relies on synergistic neurons. Overall, our results suggest that synergy is used to combine information from multiple modalities-and more generally for flexible and efficient learning. These findings reveal new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies on the system's information dynamics.
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Affiliation(s)
- Alexandra M. Proca
- Department of Computing, Imperial College London, London, United Kingdom
| | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Sussex Centre for Consciousness Science and Sussex AI, University of Sussex, Brighton, United Kingdom
- Centre for Psychedelic Research and Centre for Complexity Science, Department of Brain Sciences, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Andrea I. Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, Queen Mary University of London, London, United Kingdom
| | - Matthew Crosby
- Department of Computing, Imperial College London, London, United Kingdom
| | - Pedro A. M. Mediano
- Department of Computing, Imperial College London, London, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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14
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Bandi AC, Runyan CA. Different state-dependence of population codes across cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595581. [PMID: 38826351 PMCID: PMC11142168 DOI: 10.1101/2024.05.23.595581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
During perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an 'optimal' state and two 'sub-optimal' states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.
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Affiliation(s)
- Akhil C Bandi
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA
| | - Caroline A Runyan
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA
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15
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Daume J, Kamiński J, Schjetnan AGP, Salimpour Y, Khan U, Kyzar M, Reed CM, Anderson WS, Valiante TA, Mamelak AN, Rutishauser U. Control of working memory by phase-amplitude coupling of human hippocampal neurons. Nature 2024; 629:393-401. [PMID: 38632400 PMCID: PMC11078732 DOI: 10.1038/s41586-024-07309-z] [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: 05/03/2023] [Accepted: 03/13/2024] [Indexed: 04/19/2024]
Abstract
Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference1,2. However, how cognitive control regulates working memory storage is unclear. Here we show that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase-amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in their working memory. In the hippocampus, TG-PAC was indicative of working memory load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. The spike timing of these PAC neurons was coordinated with frontal theta activity when cognitive control demand was high. By introducing noise correlations with persistently active neurons in the hippocampus, PAC neurons shaped the geometry of the population code. This led to higher-fidelity representations of working memory content that were associated with improved behaviour. Our results support a multicomponent architecture of working memory1,2, with frontal control managing maintenance of working memory content in storage-related areas3-5. Within this framework, hippocampal TG-PAC integrates cognitive control and working memory storage across brain areas, thereby suggesting a potential mechanism for top-down control over sensory-driven processes.
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Affiliation(s)
- Jonathan Daume
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Jan Kamiński
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center of Excellence for Neural Plasticity and Brain Disorders: BRAINCITY, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Andrea G P Schjetnan
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Yousef Salimpour
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Umais Khan
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michael Kyzar
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - William S Anderson
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Taufik A Valiante
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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16
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Koren V, Malerba SB, Schwalger T, Panzeri S. Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590955. [PMID: 38712237 PMCID: PMC11071478 DOI: 10.1101/2024.04.24.590955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we rigorously derive the structural, coding, biophysical and dynamical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-stimulus-specific excitatory external input regulating metabolic cost. The efficient network has excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implementing feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal biophysical parameters include 4 to 1 ratio of excitatory vs inhibitory neurons and 3 to 1 ratio of mean inhibitory-to-inhibitory vs. excitatory-to-inhibitory connectivity that closely match those of cortical sensory networks. The efficient network has biologically-plausible spiking dynamics, with a tight instantaneous E-I balance that makes them capable to achieve efficient coding of external stimuli varying over multiple time scales. Together, these results explain how efficient coding may be implemented in cortical networks and suggests that key properties of biological neural networks may be accounted for by efficient coding.
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Affiliation(s)
- Veronika Koren
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
- Institute of Mathematics, Technische Universität Berlin, 10623 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Simone Blanco Malerba
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Tilo Schwalger
- Institute of Mathematics, Technische Universität Berlin, 10623 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
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17
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Luppi AI, Rosas FE, Mediano PAM, Menon DK, Stamatakis EA. Information decomposition and the informational architecture of the brain. Trends Cogn Sci 2024; 28:352-368. [PMID: 38199949 DOI: 10.1016/j.tics.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/09/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024]
Abstract
To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - David K Menon
- Department of Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
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18
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Manley J, Vaziri A. Whole-brain neural substrates of behavioral variability in the larval zebrafish. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.03.583208. [PMID: 38496592 PMCID: PMC10942351 DOI: 10.1101/2024.03.03.583208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Animals engaged in naturalistic behavior can exhibit a large degree of behavioral variability even under sensory invariant conditions. Such behavioral variability can include not only variations of the same behavior, but also variability across qualitatively different behaviors driven by divergent cognitive states, such as fight-or-flight decisions. However, the neural circuit mechanisms that generate such divergent behaviors across trials are not well understood. To investigate this question, here we studied the visual-evoked responses of larval zebrafish to moving objects of various sizes, which we found exhibited highly variable and divergent responses across repetitions of the same stimulus. Given that the neuronal circuits underlying such behaviors span sensory, motor, and other brain areas, we built a novel Fourier light field microscope which enables high-resolution, whole-brain imaging of larval zebrafish during behavior. This enabled us to screen for neural loci which exhibited activity patterns correlated with behavioral variability. We found that despite the highly variable activity of single neurons, visual stimuli were robustly encoded at the population level, and the visual-encoding dimensions of neural activity did not explain behavioral variability. This robustness despite apparent single neuron variability was due to the multi-dimensional geometry of the neuronal population dynamics: almost all neural dimensions that were variable across individual trials, i.e. the "noise" modes, were orthogonal to those encoding for sensory information. Investigating this neuronal variability further, we identified two sparsely-distributed, brain-wide neuronal populations whose pre-motor activity predicted whether the larva would respond to a stimulus and, if so, which direction it would turn on a single-trial level. These populations predicted single-trial behavior seconds before stimulus onset, indicating they encoded time-varying internal modulating behavior, perhaps organizing behavior over longer timescales or enabling flexible behavior routines dependent on the animal's internal state. Our results provide the first whole-brain confirmation that sensory, motor, and internal variables are encoded in a highly mixed fashion throughout the brain and demonstrate that de-mixing each of these components at the neuronal population level is critical to understanding the mechanisms underlying the brain's remarkable flexibility and robustness.
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Affiliation(s)
- Jason Manley
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
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19
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Panzeri S, Nili H. The importance of tradeoffs in neural and motor variability. Phys Life Rev 2024; 48:164-166. [PMID: 38237428 DOI: 10.1016/j.plrev.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 01/07/2024] [Indexed: 03/05/2024]
Affiliation(s)
- Stefano Panzeri
- Institute for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg- Eppendorf (UKE), Hamburg, Germany.
| | - Hamed Nili
- Institute for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg- Eppendorf (UKE), Hamburg, Germany
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20
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Matteucci G, Piasini E, Zoccolan D. Unsupervised learning of mid-level visual representations. Curr Opin Neurobiol 2024; 84:102834. [PMID: 38154417 DOI: 10.1016/j.conb.2023.102834] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/30/2023]
Abstract
Recently, a confluence between trends in neuroscience and machine learning has brought a renewed focus on unsupervised learning, where sensory processing systems learn to exploit the statistical structure of their inputs in the absence of explicit training targets or rewards. Sophisticated experimental approaches have enabled the investigation of the influence of sensory experience on neural self-organization and its synaptic bases. Meanwhile, novel algorithms for unsupervised and self-supervised learning have become increasingly popular both as inspiration for theories of the brain, particularly for the function of intermediate visual cortical areas, and as building blocks of real-world learning machines. Here we review some of these recent developments, placing them in historical context and highlighting some research lines that promise exciting breakthroughs in the near future.
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Affiliation(s)
- Giulio Matteucci
- Department of Basic Neurosciences, University of Geneva, Geneva, 1206, Switzerland. https://twitter.com/giulio_matt
| | - Eugenio Piasini
- International School for Advanced Studies (SISSA), Trieste, 34136, Italy
| | - Davide Zoccolan
- International School for Advanced Studies (SISSA), Trieste, 34136, Italy.
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21
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Weng G, Clark K, Akbarian A, Noudoost B, Nategh N. Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas. Front Comput Neurosci 2024; 18:1273053. [PMID: 38348287 PMCID: PMC10859875 DOI: 10.3389/fncom.2024.1273053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 01/09/2024] [Indexed: 02/15/2024] Open
Abstract
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors' contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
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Affiliation(s)
- Geyu Weng
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Kelsey Clark
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Amir Akbarian
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Neda Nategh
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States
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22
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Koçillari L, Celotto M, Francis NA, Mukherjee S, Babadi B, Kanold PO, Panzeri S. Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex. Brain Inform 2023; 10:34. [PMID: 38052917 PMCID: PMC10697912 DOI: 10.1186/s40708-023-00212-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/02/2023] [Indexed: 12/07/2023] Open
Abstract
Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity-that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.
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Affiliation(s)
- Loren Koçillari
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy.
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany.
| | - Marco Celotto
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany
- Department of Pharmacy and Biotechnology, University of Bologna, 40126, Bologna, Italy
| | - Nikolas A Francis
- Department of Biology and Brain and Behavior Institute, University of Maryland, College Park, MD, 20742, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering and Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
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23
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Casartelli L, Maronati C, Cavallo A. From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability. Phys Life Rev 2023; 47:245-263. [PMID: 37976727 DOI: 10.1016/j.plrev.2023.10.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/19/2023]
Abstract
In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.
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Affiliation(s)
- Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS E. MEDEA, Italy
| | - Camilla Maronati
- Move'n'Brains Lab, Department of Psychology, Università degli Studi di Torino, Italy
| | - Andrea Cavallo
- Move'n'Brains Lab, Department of Psychology, Università degli Studi di Torino, Italy; C'MoN Unit, Fondazione Istituto Italiano di Tecnologia, Genova, Italy.
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24
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Nardin M, Csicsvari J, Tkačik G, Savin C. The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience. J Neurosci 2023; 43:8140-8156. [PMID: 37758476 PMCID: PMC10697404 DOI: 10.1523/jneurosci.0194-23.2023] [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: 02/09/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 10/03/2023] Open
Abstract
Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.SIGNIFICANCE STATEMENT Local circuit interactions play a key role in neural computation and are dynamically shaped by experience. However, measuring and assessing their effects during behavior remains a challenge. Here, we combine techniques from statistical physics and machine learning to develop new tools for determining the effects of local network interactions on neural population activity. This approach reveals highly structured local interactions between hippocampal neurons, which make the neural code more precise and easier to read out by downstream circuits, across different levels of experience. More generally, the novel combination of theory and data analysis in the framework of maximum entropy models enables traditional neural coding questions to be asked in naturalistic settings.
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Affiliation(s)
- Michele Nardin
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147
| | - Jozsef Csicsvari
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
| | - Gašper Tkačik
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
| | - Cristina Savin
- Center for Neural Science, New York University, New York, New York 10003
- Center for Data Science, New York University, New York, New York 10011
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25
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Rowland JM, van der Plas TL, Loidolt M, Lees RM, Keeling J, Dehning J, Akam T, Priesemann V, Packer AM. Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Nat Neurosci 2023; 26:1584-1594. [PMID: 37640911 PMCID: PMC10471496 DOI: 10.1038/s41593-023-01413-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions-the primary and secondary somatosensory cortex (S1 and S2)-in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.
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Affiliation(s)
- James M Rowland
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Thijs L van der Plas
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Matthias Loidolt
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Laboratory for Molecular Cell Biology, University College London, London, UK
| | - Robert M Lees
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Science and Technology Facilities Council, Octopus Imaging Facility, Research Complex at Harwell, Harwell Campus, Oxfordshire, UK
| | - Joshua Keeling
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Jonas Dehning
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Thomas Akam
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Adam M Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK.
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26
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Safaai H, Wang AY, Kira S, Malerba SB, Panzeri S, Harvey CD. Specialized structure of neural population codes in parietal cortex outputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554635. [PMID: 37662297 PMCID: PMC10473762 DOI: 10.1101/2023.08.24.554635] [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
Do cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.
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Affiliation(s)
- Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, USA
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Alice Y. Wang
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Simone Blanco Malerba
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, 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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27
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Noel JP, Balzani E, Savin C, Angelaki DE. Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.30.551169. [PMID: 37577498 PMCID: PMC10418097 DOI: 10.1101/2023.07.30.551169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here we have macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the area's ability for cross-context decoding. Lastly, correlation analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between prefrontal cortex neurons maintains a stable population code and context-invariant beliefs during naturalistic behavior with closed action-perception loops.
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Affiliation(s)
- Jean-Paul Noel
- Center for Neural Science, New York University, New York City, NY, USA
| | - Edoardo Balzani
- Center for Neural Science, New York University, New York City, NY, USA
| | - Cristina Savin
- Center for Neural Science, New York University, New York City, NY, USA
| | - Dora E. Angelaki
- Center for Neural Science, New York University, New York City, NY, USA
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28
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Scaliti E, Pullar K, Borghini G, Cavallo A, Panzeri S, Becchio C. Kinematic priming of action predictions. Curr Biol 2023:S0960-9822(23)00687-5. [PMID: 37339628 DOI: 10.1016/j.cub.2023.05.055] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 04/06/2023] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
The ability to anticipate what others will do next is crucial for navigating social, interactive environments. Here, we develop an experimental and analytical framework to measure the implicit readout of prospective intention information from movement kinematics. Using a primed action categorization task, we first demonstrate implicit access to intention information by establishing a novel form of priming, which we term kinematic priming: subtle differences in movement kinematics prime action prediction. Next, using data collected from the same participants in a forced-choice intention discrimination task 1 h later, we quantify single-trial intention readout-the amount of intention information read by individual perceivers in individual kinematic primes-and assess whether it can be used to predict the amount of kinematic priming. We demonstrate that the amount of kinematic priming, as indexed by both response times (RTs) and initial fixations to a given probe, is directly proportional to the amount of intention information read by the individual perceiver at the single-trial level. These results demonstrate that human perceivers have rapid, implicit access to intention information encoded in movement kinematics and highlight the potential of our approach to reveal the computations that permit the readout of this information with single-subject, single-trial resolution.
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Affiliation(s)
- Eugenio Scaliti
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Martinistrasse 52, 20246 Hamburg, Germany
| | - Kiri Pullar
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Giulia Borghini
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Andrea Cavallo
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Psychology, Università degli Studi di Torino, Via Giuseppe Verdi, 10, 10124 Torino, Italy
| | - Stefano Panzeri
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany.
| | - Cristina Becchio
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Martinistrasse 52, 20246 Hamburg, Germany.
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29
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Bonato J, Curreli S, Romanzi S, Panzeri S, Fellin T. ASTRA: a deep learning algorithm for fast semantic segmentation of large-scale astrocytic networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.03.539211. [PMID: 37205519 PMCID: PMC10187152 DOI: 10.1101/2023.05.03.539211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Changes in the intracellular calcium concentration are a fundamental fingerprint of astrocytes, the main type of glial cell. Astrocyte calcium signals can be measured with two-photon microscopy, occur in anatomically restricted subcellular regions, and are coordinated across astrocytic networks. However, current analytical tools to identify the astrocytic subcellular regions where calcium signals occur are time-consuming and extensively rely on user-defined parameters. These limitations limit reproducibility and prevent scalability to large datasets and fields-of-view. Here, we present Astrocytic calcium Spatio-Temporal Rapid Analysis (ASTRA), a novel software combining deep learning with image feature engineering for fast and fully automated semantic segmentation of two-photon calcium imaging recordings of astrocytes. We applied ASTRA to several two-photon microscopy datasets and found that ASTRA performed rapid detection and segmentation of astrocytic cell somata and processes with performance close to that of human experts, outperformed state-of-the-art algorithms for the analysis of astrocytic and neuronal calcium data, and generalized across indicators and acquisition parameters. We also applied ASTRA to the first report of two-photon mesoscopic imaging of hundreds of astrocytes in awake mice, documenting large-scale redundant and synergistic interactions in extended astrocytic networks. ASTRA is a powerful tool enabling closed-loop and large-scale reproducible investigation of astrocytic morphology and function.
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Affiliation(s)
- Jacopo Bonato
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Department of Pharmacy and Biotechnology, University of Bologna; 40126 Bologna, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany
| | - Sebastiano Curreli
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
| | - Sara Romanzi
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- University of Genova; 16126 Genova, Italy
| | - Stefano Panzeri
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany
| | - Tommaso Fellin
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
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30
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Khoury CF, Fala NG, Runyan CA. Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex. eNeuro 2023; 10:ENEURO.0136-23.2023. [PMID: 37169583 PMCID: PMC10216262 DOI: 10.1523/eneuro.0136-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/13/2023] Open
Abstract
Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.
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Affiliation(s)
- Christine F Khoury
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Noelle G Fala
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Caroline A Runyan
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
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31
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Jacob M, Ford J, Deacon T. Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI. Front Hum Neurosci 2023; 17:976036. [PMID: 37113322 PMCID: PMC10126302 DOI: 10.3389/fnhum.2023.976036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/02/2023] [Indexed: 04/29/2023] Open
Abstract
The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.
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Affiliation(s)
- Michael Jacob
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Judith Ford
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Terrence Deacon
- Department of Anthropology, University of California, Berkeley, Berkeley, CA, United States
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32
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Daume J, Kaminski J, Schjetnan AGP, Salimpour Y, Khan U, Reed C, Anderson W, Valiante TA, Mamelak AN, Rutishauser U. Control of working memory maintenance by theta-gamma phase amplitude coupling of human hippocampal neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.05.535772. [PMID: 37066145 PMCID: PMC10104113 DOI: 10.1101/2023.04.05.535772] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Retaining information in working memory (WM) is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference. How cognitive control regulates WM storage, however, remains unknown. We hypothesized that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in WM. In the hippocampus, TG-PAC was indicative of WM load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. These PAC neurons were more strongly coordinated with frontal theta activity when cognitive control demand was high, and they introduced information-enhancing and behaviorally relevant noise correlations with persistently active neurons in the hippocampus. We show that TG-PAC integrates cognitive control and WM storage to improve the fidelity of WM representations and facilitate behavior.
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Affiliation(s)
- Jonathan Daume
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jan Kaminski
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Nencki Institute of Experimental Biology, Warsaw, Poland
| | - Andrea G P Schjetnan
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, ON, Canada
| | - Yousef Salimpour
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Umais Khan
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Chrystal Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - William Anderson
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Taufik A Valiante
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, ON, Canada
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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33
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Haggard M, Chacron MJ. Coding of object location by heterogeneous neural populations with spatially dependent correlations in weakly electric fish. PLoS Comput Biol 2023; 19:e1010938. [PMID: 36867650 PMCID: PMC10016687 DOI: 10.1371/journal.pcbi.1010938] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 03/15/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Understanding how neural populations encode sensory stimuli remains a central problem in neuroscience. Here we performed multi-unit recordings from sensory neural populations in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus in response to stimuli located at different positions along the rostro-caudal axis. Our results reveal that the spatial dependence of correlated activity along receptive fields can help mitigate the deleterious effects that these correlations would otherwise have if they were spatially independent. Moreover, using mathematical modeling, we show that experimentally observed heterogeneities in the receptive fields of neurons help optimize information transmission as to object location. Taken together, our results have important implications for understanding how sensory neurons whose receptive fields display antagonistic center-surround organization encode location. Important similarities between the electrosensory system and other sensory systems suggest that our results will be applicable elsewhere.
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Affiliation(s)
- Myriah Haggard
- Quantitative Life Sciences, McGill University, Montreal, Canada
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34
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van den Brink RL, Hagena K, Wilming N, Murphy PR, Büchel C, Donner TH. Flexible sensory-motor mapping rules manifest in correlated variability of stimulus and action codes across the brain. Neuron 2023; 111:571-584.e9. [PMID: 36476977 DOI: 10.1016/j.neuron.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022]
Abstract
Humans and non-human primates can flexibly switch between different arbitrary mappings from sensation to action to solve a cognitive task. It has remained unknown how the brain implements such flexible sensory-motor mapping rules. Here, we uncovered a dynamic reconfiguration of task-specific correlated variability between sensory and motor brain regions. Human participants switched between two rules for reporting visual orientation judgments during fMRI recordings. Rule switches were either signaled explicitly or inferred by the participants from ambiguous cues. We used behavioral modeling to reconstruct the time course of their belief about the active rule. In both contexts, the patterns of correlations between ongoing fluctuations in stimulus- and action-selective activity across visual- and action-related brain regions tracked participants' belief about the active rule. The rule-specific correlation patterns broke down around the time of behavioral errors. We conclude that internal beliefs about task state are instantiated in brain-wide, selective patterns of correlated variability.
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Affiliation(s)
- Ruud L van den Brink
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
| | - Keno Hagena
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Niklas Wilming
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Peter R Murphy
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, D02 PN40 Dublin, Ireland; Department of Psychology, Maynooth University, Maynooth, Co. Kildare, Ireland
| | - Christian Büchel
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Tobias H Donner
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
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35
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Nogueira R, Rodgers CC, Bruno RM, Fusi S. The geometry of cortical representations of touch in rodents. Nat Neurosci 2023; 26:239-250. [PMID: 36624277 DOI: 10.1038/s41593-022-01237-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/16/2022] [Indexed: 01/11/2023]
Abstract
Neurons often encode highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects touched by their whiskers. High-speed videos of the whiskers revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects non-linear integration of spatio-temporal features of the sensory inputs. Although the responses at first appeared disorganized, we identified an interesting structure in the representational geometry: different whisker contacts are disentangled variables represented in approximately, but not fully, orthogonal subspaces of the neural activity space. This geometry allows linear readouts to perform a broad class of tasks of different complexities without compromising the ability to generalize to novel situations.
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Affiliation(s)
- Ramon Nogueira
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Department of Neuroscience, Columbia University, New York, NY, USA.
| | - Chris C Rodgers
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - Randy M Bruno
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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36
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Koren V, Bondanelli G, Panzeri S. Computational methods to study information processing in neural circuits. Comput Struct Biotechnol J 2023; 21:910-922. [PMID: 36698970 PMCID: PMC9851868 DOI: 10.1016/j.csbj.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
The brain is an information processing machine and thus naturally lends itself to be studied using computational tools based on the principles of information theory. For this reason, computational methods based on or inspired by information theory have been a cornerstone of practical and conceptual progress in neuroscience. In this Review, we address how concepts and computational tools related to information theory are spurring the development of principled theories of information processing in neural circuits and the development of influential mathematical methods for the analyses of neural population recordings. We review how these computational approaches reveal mechanisms of essential functions performed by neural circuits. These functions include efficiently encoding sensory information and facilitating the transmission of information to downstream brain areas to inform and guide behavior. Finally, we discuss how further progress and insights can be achieved, in particular by studying how competing requirements of neural encoding and readout may be optimally traded off to optimize neural information processing.
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Affiliation(s)
- Veronika Koren
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany
| | | | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany
- Istituto Italiano di Tecnologia, Via Melen 83, Genova 16152, Italy
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37
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Nigam S, Milton R, Pojoga S, Dragoi V. Adaptive coding across visual features during free-viewing and fixation conditions. Nat Commun 2023; 14:87. [PMID: 36604422 PMCID: PMC9816177 DOI: 10.1038/s41467-022-35656-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 12/15/2022] [Indexed: 01/07/2023] Open
Abstract
Theoretical studies have long proposed that adaptation allows the brain to effectively use the limited response range of sensory neurons to encode widely varying natural inputs. However, despite this influential view, experimental studies have exclusively focused on how the neural code adapts to a range of stimuli lying along a single feature axis, such as orientation or contrast. Here, we performed electrical recordings in macaque visual cortex (area V4) to reveal significant adaptive changes in the neural code of single cells and populations across multiple feature axes. Both during free viewing and passive fixation, populations of cells improved their ability to encode image features after rapid exposure to stimuli lying on orthogonal feature axes even in the absence of initial tuning to these stimuli. These results reveal a remarkable adaptive capacity of visual cortical populations to improve network computations relevant for natural viewing despite the modularity of the functional cortical architecture.
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Affiliation(s)
- Sunny Nigam
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US.
| | - Russell Milton
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US
| | - Sorin Pojoga
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, US.
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38
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Chadwick A, Khan AG, Poort J, Blot A, Hofer SB, Mrsic-Flogel TD, Sahani M. Learning shapes cortical dynamics to enhance integration of relevant sensory input. Neuron 2023; 111:106-120.e10. [PMID: 36283408 PMCID: PMC7614688 DOI: 10.1016/j.neuron.2022.10.001] [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: 08/01/2021] [Revised: 07/14/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.
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Affiliation(s)
- Angus Chadwick
- Gatsby Computational Neuroscience Unit, University College London, London, UK; Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Adil G Khan
- Centre for Developmental Neurobiology, King's College London, London, UK
| | - Jasper Poort
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Antonin Blot
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Thomas D Mrsic-Flogel
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK.
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39
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Khoury CF, Fala NG, Runyan CA. The spatial scale of somatostatin subnetworks increases from sensory to association cortex. Cell Rep 2022; 40:111319. [PMID: 36070697 DOI: 10.1016/j.celrep.2022.111319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 07/01/2022] [Accepted: 08/15/2022] [Indexed: 11/24/2022] Open
Abstract
Incoming signals interact with rich, ongoing population activity dynamics in cortical circuits. These intrinsic dynamics are the consequence of interactions among local excitatory and inhibitory neurons and affect inter-region communication and information coding. It is unclear whether specializations in the patterns of interactions among excitatory and inhibitory neurons underlie systematic differences in activity dynamics across the cortex. Here, in mice, we compare the functional interactions among somatostatin (SOM)-expressing inhibitory interneurons and the rest of the neural population in auditory cortex (AC), a sensory region of the cortex, and posterior parietal cortex (PPC), an association region. The spatial structure of shared variability among SOM and non-SOM neurons differs across regions: correlations decay rapidly with distance in AC but not in PPC. However, in both regions, activity of SOM neurons is more highly correlated than non-SOM neurons' activity. Our results imply both generalization and specialization in the functional structure of inhibitory subnetworks across the cortex.
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Affiliation(s)
- Christine F Khoury
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Noelle G Fala
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Caroline A Runyan
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260, USA.
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40
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Chini M, Pfeffer T, Hanganu-Opatz I. An increase of inhibition drives the developmental decorrelation of neural activity. eLife 2022; 11:78811. [PMID: 35975980 PMCID: PMC9448324 DOI: 10.7554/elife.78811] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/16/2022] [Indexed: 11/23/2022] Open
Abstract
Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are poorly understood. The developmental transition has important functional consequences, as the latter state is thought to allow for more efficient storage, retrieval, and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation-inhibition (E-I) ratio toward inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in mice mimicking the etiology of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.
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Affiliation(s)
- Mattia Chini
- Institute of Developmental Neurophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Pfeffer
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ileana Hanganu-Opatz
- Institute of Developmental Neurophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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41
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Moroni M, Brondi M, Fellin T, Panzeri S. SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging. Brain Inform 2022; 9:18. [PMID: 35927517 PMCID: PMC9352634 DOI: 10.1186/s40708-022-00166-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/01/2022] [Indexed: 11/17/2022] Open
Abstract
Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.
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Affiliation(s)
- Monica Moroni
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems, UniTn, Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy.
| | - Marco Brondi
- Optical Approaches to Brain Function Laboratory, Istituto Italiano Di Tecnologia, 16163, Genoa, Italy.,Department of Biomedical Sciences-UNIPD, Università Degli Studi Di Padova, 35121, Padua, Italy.,Padova Neuroscience Center (PNC), Università Degli Studi Di Padova, 35129, Padua, Italy
| | - Tommaso Fellin
- Optical Approaches to Brain Function Laboratory, Istituto Italiano Di Tecnologia, 16163, Genoa, Italy
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems, UniTn, Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy. .,Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251, Hamburg, Germany.
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42
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Grillo M, Geminiani A, Alessandro C, D'Angelo E, Pedrocchi A, Casellato C. Bayesian Integration in a Spiking Neural System for Sensorimotor Control. Neural Comput 2022; 34:1893-1914. [PMID: 35896162 DOI: 10.1162/neco_a_01525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 04/30/2022] [Indexed: 11/04/2022]
Abstract
The brain continuously estimates the state of body and environment, with specific regions that are thought to act as Bayesian estimator, optimally integrating noisy and delayed sensory feedback with sensory predictions generated by the cerebellum. In control theory, Bayesian estimators are usually implemented using high-level representations. In this work, we designed a new spike-based computational model of a Bayesian estimator. The state estimator receives spiking activity from two neural populations encoding the sensory feedback and the cerebellar prediction, and it continuously computes the spike variability within each population as a reliability index of the signal these populations encode. The state estimator output encodes the current state estimate. We simulated a reaching task at different stages of cerebellar learning. The activity of the sensory feedback neurons encoded a noisy version of the trajectory after actual movement, with an almost constant intrapopulation spiking variability. Conversely, the activity of the cerebellar output neurons depended on the phase of the learning process. Before learning, they fired at their baseline not encoding any relevant information, and the variability was set to be higher than that of the sensory feedback (more reliable, albeit delayed). When learning was complete, their activity encoded the trajectory before the actual execution, providing an accurate sensory prediction; in this case, the variability was set to be lower than that of the sensory feedback. The state estimator model optimally integrated the neural activities of the afferent populations, so that the output state estimate was primarily driven by sensory feedback in prelearning and by the cerebellar prediction in postlearning. It was able to deal even with more complex scenarios, for example, by shifting the dominant source during the movement execution if information availability suddenly changed. The proposed tool will be a critical block within integrated spiking, brain-inspired control systems for simulations of sensorimotor tasks.
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Affiliation(s)
- Massimo Grillo
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, Italy
| | - Alice Geminiani
- Department of Brain and Behavioral Sciences, University of Pavia 27100, Italy
| | - Cristiano Alessandro
- Department of Brain and Behavioral Sciences, University of Pavia 27100, Italy.,School of Medicine and Surgery/Sport and Exercise Science, University of Milano-Bicocca, 20126 Milan, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia 27100, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia 27100, Italy
| | - Alessandra Pedrocchi
- Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, Italy
| | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia 27100, Italy
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43
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Li M, Chen S, Liu X, Song Z, Wang Y. Modeling Neural Connectivity in a Point-Process Analogue of Kalman Filter. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:768-771. [PMID: 36085743 DOI: 10.1109/embc48229.2022.9871283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A neural encoding model describes how single neuron tunes to external stimuli as well as its connectivity with other neurons. The connectivity illustrates the neuronal interaction within populations in response to the shared latent brain states. Understanding these interactions is crucial to computationally predict the neural activity, which elucidates the neural encoding mechanism A computational analysis on the neural connectivity also facilitates developing more point process decoding model to interpret movement state from neural spike observations for brain machine interfaces (BMI). Most of the previous point process models only consider single neural tuning property and assumes conditional independence among multiple neurons. The connectivity among neurons is not considered in such a Bayesian approach to derive the state. In this work, we propose a point-process analogue of Kalman Filter to model the neural connectivity in a closed-form Bayesian filter. Neural connectivity corrects the posterior of the state given the multi-dimension observation, and a Gaussian distribution is used to approximate the updated posterior distribution. We validate the proposed method on simulation data and compared with traditional point process filtering with conditional independent assumption. The result shows that our method models the neural connectivity information and the single neuronal tuning property at the same time and achieves a better performance of the state decoding. Clinical Relevance - This paper proposes a closed-form derivation of a point process filter based on Gaussian approximations. It can model both single neuronal tuning property and the neural connectivity, which is potential to understanding the neural connectivity computationally.
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44
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Panzeri S, Moroni M, Safaai H, Harvey CD. The structures and functions of correlations in neural population codes. Nat Rev Neurosci 2022; 23:551-567. [PMID: 35732917 DOI: 10.1038/s41583-022-00606-4] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2022] [Indexed: 12/17/2022]
Abstract
The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.
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Affiliation(s)
- Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany. .,Istituto Italiano di Tecnologia, Rovereto, Italy.
| | | | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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45
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Francis NA, Mukherjee S, Koçillari L, Panzeri S, Babadi B, Kanold PO. Sequential transmission of task-relevant information in cortical neuronal networks. Cell Rep 2022; 39:110878. [PMID: 35649366 PMCID: PMC9387204 DOI: 10.1016/j.celrep.2022.110878] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/28/2022] [Accepted: 05/05/2022] [Indexed: 11/10/2022] Open
Abstract
Cortical processing of task-relevant information enables recognition of behaviorally meaningful sensory events. It is unclear how task-related information is represented within cortical networks by the activity of individual neurons and their functional interactions. Here, we use two-photon imaging to record neuronal activity from the primary auditory cortex of mice during a pure-tone discrimination task. We find that a subset of neurons transiently encode sensory information used to inform behavioral choice. Using Granger causality analysis, we show that these neurons form functional networks in which information transmits sequentially. Network structures differ for target versus non-target tones, encode behavioral choice, and differ between correct versus incorrect behavioral choices. Correct behavioral choices are associated with shorter communication timescales, larger functional correlations, and greater information redundancy. In summary, specialized neurons in primary auditory cortex integrate task-related information and form functional networks whose structures encode both sensory input and behavioral choice. Francis et al. find that, as mice perform an auditory discrimination task, cortical neurons form functional networks in which task-relevant information transmits sequentially between neurons. Network structures encode behavioral choice, and correct behavioral choices are associated with shorter communication timescales, larger functional correlations, and greater information redundancy between neurons.
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Affiliation(s)
- Nikolas A Francis
- Department of Biology & Brain and Behavior Institute, University of Maryland, College Park, MD 20742, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering & Institute for Systems Research, University of Maryland, College Park, MD 20742, USA
| | - Loren Koçillari
- Laboratory of Neural Computation, Istituto Italiano di Tecnologia, Rovereto 38068, Italy; Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany
| | - Stefano Panzeri
- Laboratory of Neural Computation, Istituto Italiano di Tecnologia, Rovereto 38068, Italy; Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany.
| | - Behtash Babadi
- Department of Electrical and Computer Engineering & Institute for Systems Research, University of Maryland, College Park, MD 20742, USA.
| | - Patrick O Kanold
- Department of Biology & Brain and Behavior Institute, University of Maryland, College Park, MD 20742, USA; Department of Biomedical Engineering & Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21205, USA.
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46
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Johnston R, Snyder AC, Schibler RS, Smith MA. EEG Signals Index a Global Signature of Arousal Embedded in Neuronal Population Recordings. eNeuro 2022; 9:ENEURO.0012-22.2022. [PMID: 35606150 PMCID: PMC9186107 DOI: 10.1523/eneuro.0012-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 01/01/2023] Open
Abstract
Electroencephalography (EEG) has long been used to index brain states, from early studies describing activity in the presence and absence of visual stimulation to modern work employing complex perceptual tasks. These studies have shed light on brain-wide signals but often lack explanatory power at the single neuron level. Similarly, single neuron recordings can suffer from an inability to measure brain-wide signals accessible using EEG. Here, we combined these techniques while monkeys performed a change detection task and discovered a novel link between spontaneous EEG activity and a neural signal embedded in the spiking responses of neuronal populations. This "slow drift" was associated with fluctuations in the subjects' arousal levels over time: decreases in prestimulus α power were accompanied by increases in pupil size and decreases in microsaccade rate. These results show that brain-wide EEG signals can be used to index modes of activity present in single neuron recordings, that in turn reflect global changes in brain state that influence perception and behavior.
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Affiliation(s)
- Richard Johnston
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Adam C Snyder
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, PA 14627
- Department of Neuroscience, University of Rochester, Rochester, NY 14642
- Center for Visual Science, University of Rochester, Rochester, NY 14627
| | | | - Matthew A Smith
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
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47
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Intersecting kinematic encoding and readout of intention in autism. Proc Natl Acad Sci U S A 2022; 119:2114648119. [PMID: 35101921 PMCID: PMC8812545 DOI: 10.1073/pnas.2114648119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/17/2021] [Indexed: 01/10/2023] Open
Abstract
Observers with autism spectrum disorders (ASDs) find it difficult to read intentions from movements. However, the computational bases of these difficulties are unknown. Do these difficulties reflect an intention readout deficit, or are they more likely rooted in kinematic (dis-)similarities between typical and ASD kinematics? We combined motion tracking, psychophysics, and computational analyses to uncover single-trial intention readout computations in typically developing (TD) children (n = 35) and children with ASD (n = 35) who observed actions performed by TD children and children with ASD. Average intention discrimination performance was above chance for TD observers but not for ASD observers. However, single-trial analysis showed that both TD and ASD observers read single-trial variations in movement kinematics. TD readers were better able to identify intention-informative kinematic features during observation of TD actions; conversely, ASD readers were better able to identify intention-informative features during observation of ASD actions. Crucially, while TD observers were generally able to extract the intention information encoded in movement kinematics, those with autism were unable to do so. These results extend existing conceptions of mind reading in ASD by suggesting that intention reading difficulties reflect both an interaction failure, rooted in kinematic dissimilarity between TD and ASD kinematics (at the level of feature identification), and an individual readout deficit (at the level of information extraction), accompanied by an overall reduced sensitivity of intention readout to single-trial variations in movement kinematics.
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48
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Joshi S, Gold JI. Context-dependent relationships between locus coeruleus firing patterns and coordinated neural activity in the anterior cingulate cortex. eLife 2022; 11:63490. [PMID: 34994344 PMCID: PMC8765756 DOI: 10.7554/elife.63490] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/16/2021] [Indexed: 01/30/2023] Open
Abstract
Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these neuromodulatory effects on neural activity patterns in vivo is not well understood. Here, we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These relationships, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations tend to be reduced when ongoing (baseline) LC activity increases but enhanced when external events evoke transient LC responses. Both relationships covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can result partly from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.
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Affiliation(s)
- Siddhartha Joshi
- Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
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49
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Chelaru MI, Eagleman S, Andrei AR, Milton R, Kharas N, Dragoi V. High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas. Neuron 2021; 109:3954-3961.e5. [PMID: 34665999 PMCID: PMC8678300 DOI: 10.1016/j.neuron.2021.09.042] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 08/09/2021] [Accepted: 09/22/2021] [Indexed: 02/04/2023]
Abstract
One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown. Here, we performed multi-area electrical recordings to find that 2nd-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured ∼90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from the prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions.
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Affiliation(s)
- Mircea I Chelaru
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX 77030, USA
| | - Sarah Eagleman
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX 77030, USA; Department of Anesthesiology, Stanford School of Medicine, Palo Alto, CA 94304, USA
| | - Ariana R Andrei
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX 77030, USA
| | - Russell Milton
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX 77030, USA
| | - Natasha Kharas
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX 77030, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77026, USA.
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50
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Bondanelli G, Panzeri S. Neuroscience: Turbulent times for brain information processing. Curr Biol 2021; 31:R1400-R1402. [PMID: 34699808 DOI: 10.1016/j.cub.2021.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
A recent study shows that rare long-range connections between brain areas may considerably improve transmission of information between areas. The study suggests that information may propagate better through long-range connections when neural activity exhibits turbulent dynamics.
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Affiliation(s)
- Giulio Bondanelli
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy; Department of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
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