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Cone JJ, Mitchell AO, Parker RK, Maunsell JHR. Stimulus-dependent differences in cortical versus subcortical contributions to visual detection in mice. Curr Biol 2024; 34:1940-1952.e5. [PMID: 38640924 PMCID: PMC11080572 DOI: 10.1016/j.cub.2024.03.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/08/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024]
Abstract
The primary visual cortex (V1) and the superior colliculus (SC) both occupy stations early in the processing of visual information. They have long been thought to perform distinct functions, with the V1 supporting the perception of visual features and the SC regulating orienting to visual inputs. However, growing evidence suggests that the SC supports the perception of many of the same visual features traditionally associated with the V1. To distinguish V1 and SC contributions to visual processing, it is critical to determine whether both areas causally contribute to the detection of specific visual stimuli. Here, mice reported changes in visual contrast or luminance near their perceptual threshold while white noise patterns of optogenetic stimulation were delivered to V1 or SC inhibitory neurons. We then performed a reverse correlation analysis on the optogenetic stimuli to estimate a neuronal-behavioral kernel (NBK), a moment-to-moment estimate of the impact of V1 or SC inhibition on stimulus detection. We show that the earliest moments of stimulus-evoked activity in the SC are critical for the detection of both luminance and contrast changes. Strikingly, there was a robust stimulus-aligned modulation in the V1 contrast-detection NBK but no sign of a comparable modulation for luminance detection. The data suggest that behavioral detection of visual contrast depends on both V1 and SC spiking, whereas mice preferentially use SC activity to detect changes in luminance. Electrophysiological recordings showed that neurons in both the SC and V1 responded strongly to both visual stimulus types, while the reverse correlation analysis reveals when these neuronal signals actually contribute to visually guided behaviors.
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Affiliation(s)
- Jackson J Cone
- Department of Neurobiology and Neuroscience Institute, University of Chicago, 5812 S. Ellis Ave. MC 0912, Suite P-400, Chicago, IL 60637, USA.
| | - Autumn O Mitchell
- Department of Neurobiology and Neuroscience Institute, University of Chicago, 5812 S. Ellis Ave. MC 0912, Suite P-400, Chicago, IL 60637, USA
| | - Rachel K Parker
- Department of Neurobiology and Neuroscience Institute, University of Chicago, 5812 S. Ellis Ave. MC 0912, Suite P-400, Chicago, IL 60637, USA
| | - John H R Maunsell
- Department of Neurobiology and Neuroscience Institute, University of Chicago, 5812 S. Ellis Ave. MC 0912, Suite P-400, Chicago, IL 60637, USA
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2
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Shang W, Xie S, Feng W, Li Z, Jia J, Cao X, Shen Y, Li J, Shi H, Gu Y, Weng SJ, Lin L, Pan YH, Yuan XB. A non-image-forming visual circuit mediates the innate fear of heights in male mice. Nat Commun 2024; 15:3746. [PMID: 38702319 PMCID: PMC11068790 DOI: 10.1038/s41467-024-48147-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 04/19/2024] [Indexed: 05/06/2024] Open
Abstract
The neural basis of fear of heights remains largely unknown. In this study, we investigated the fear response to heights in male mice and observed characteristic aversive behaviors resembling human height vertigo. We identified visual input as a critical factor in mouse reactions to heights, while peripheral vestibular input was found to be nonessential for fear of heights. Unexpectedly, we found that fear of heights in naïve mice does not rely on image-forming visual processing by the primary visual cortex. Instead, a subset of neurons in the ventral lateral geniculate nucleus (vLGN), which connects to the lateral/ventrolateral periaqueductal gray (l/vlPAG), drives the expression of fear associated with heights. Additionally, we observed that a subcortical visual pathway linking the superior colliculus to the lateral posterior thalamic nucleus inhibits the defensive response to height threats. These findings highlight a rapid fear response to height threats through a subcortical visual and defensive pathway from the vLGN to the l/vlPAG.
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Affiliation(s)
- Wei Shang
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Shuangyi Xie
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Wenbo Feng
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Zhuangzhuang Li
- Department of Otolaryngology Head & Neck Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai, 200233, China
| | - Jingyan Jia
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Xiaoxiao Cao
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Yanting Shen
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Jing Li
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Haibo Shi
- Department of Otolaryngology Head & Neck Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai, 200233, China
| | - Yiran Gu
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Shi-Jun Weng
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, China
| | - Longnian Lin
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China
| | - Yi-Hsuan Pan
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China.
| | - Xiao-Bing Yuan
- Key Laboratory of Brain Functional Genomics of Shanghai and Ministry of Education, Institute of Brain Functional Genomics, School of Life Science and the Collaborative Innovation Center for Brain Science, East China Normal University, Shanghai, 200062, China.
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3
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Lopez-Ortega E, Choi JY, Hong I, Roth RH, Cudmore RH, Huganir RL. Stimulus-dependent synaptic plasticity underlies neuronal circuitry refinement in the mouse primary visual cortex. Cell Rep 2024; 43:113966. [PMID: 38507408 DOI: 10.1016/j.celrep.2024.113966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/26/2023] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
Perceptual learning improves our ability to interpret sensory stimuli present in our environment through experience. Despite its importance, the underlying mechanisms that enable perceptual learning in our sensory cortices are still not fully understood. In this study, we used in vivo two-photon imaging to investigate the functional and structural changes induced by visual stimulation in the mouse primary visual cortex (V1). Our results demonstrate that repeated stimulation leads to a refinement of V1 circuitry by decreasing the number of responsive neurons while potentiating their response. At the synaptic level, we observe a reduction in the number of dendritic spines and an overall increase in spine AMPA receptor levels in the same subset of neurons. In addition, visual stimulation induces synaptic potentiation in neighboring spines within individual dendrites. These findings provide insights into the mechanisms of synaptic plasticity underlying information processing in the neocortex.
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Affiliation(s)
- Elena Lopez-Ortega
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jung Yoon Choi
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ingie Hong
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Richard H Roth
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Robert H Cudmore
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Richard L Huganir
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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4
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Saeedi A, Wang K, Nikpourian G, Bartels A, Logothetis NK, Totah NK, Watanabe M. Brightness illusions drive a neuronal response in the primary visual cortex under top-down modulation. Nat Commun 2024; 15:3141. [PMID: 38653975 DOI: 10.1038/s41467-024-46885-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 03/13/2024] [Indexed: 04/25/2024] Open
Abstract
Brightness illusions are a powerful tool in studying vision, yet their neural correlates are poorly understood. Based on a human paradigm, we presented illusory drifting gratings to mice. Primary visual cortex (V1) neurons responded to illusory gratings, matching their direction selectivity for real gratings, and they tracked the spatial phase offset between illusory and real gratings. Illusion responses were delayed compared to real gratings, in line with the theory that processing illusions requires feedback from higher visual areas (HVAs). We provide support for this theory by showing a reduced V1 response to illusions, but not real gratings, following HVAs optogenetic inhibition. Finally, we used the pupil response (PR) as an indirect perceptual report and showed that the mouse PR matches the human PR to perceived luminance changes. Our findings resolve debates over whether V1 neurons are involved in processing illusions and highlight the involvement of feedback from HVAs.
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Affiliation(s)
- Alireza Saeedi
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, 72076, Tübingen, Germany
- Research Group Neurobiology of Magnetoreception, Max Planck Institute for Neurobiology of Behavior - caesar, 53175, Bonn, Germany
| | - Kun Wang
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, 72076, Tübingen, Germany
- Department of Physiology of Cognitive Processes, International Center for Primate Brain Research, Songjiang District, Shanghai, 201602, China
| | - Ghazaleh Nikpourian
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, 72076, Tübingen, Germany
| | - Andreas Bartels
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, 72076, Tübingen, Germany
- Department of Psychology, Vision and Cognition Lab, Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, Tübingen, Germany
| | - Nikos K Logothetis
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, 72076, Tübingen, Germany
- Department of Physiology of Cognitive Processes, International Center for Primate Brain Research, Songjiang District, Shanghai, 201602, China
- Centre for Imaging Sciences, University of Manchester, Manchester, M139PT, UK
| | - Nelson K Totah
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, 72076, Tübingen, Germany.
- Helsinki Institute of Life Science (HILIFE), University of Helsinki, 00014, Helsinki, Finland.
- Faculty of Pharmacy, University of Helsinki, 00014, Helsinki, Finland.
- Neuroscience Center, University of Helsinki, 00014, Helsinki, Finland.
| | - Masataka Watanabe
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, 72076, Tübingen, Germany.
- Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan.
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5
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Nivinsky Margalit S, Slovin H. Encoding luminance surfaces in the visual cortex of mice and monkeys: difference in responses to edge and center. Cereb Cortex 2024; 34:bhae165. [PMID: 38652553 DOI: 10.1093/cercor/bhae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 04/25/2024] Open
Abstract
Luminance and spatial contrast provide information on the surfaces and edges of objects. We investigated neural responses to black and white surfaces in the primary visual cortex (V1) of mice and monkeys. Unlike primates that use their fovea to inspect objects with high acuity, mice lack a fovea and have low visual acuity. It thus remains unclear whether monkeys and mice share similar neural mechanisms to process surfaces. The animals were presented with white or black surfaces and the population responses were measured at high spatial and temporal resolution using voltage-sensitive dye imaging. In mice, the population response to the surface was not edge-dominated with a tendency to center-dominance, whereas in monkeys the response was edge-dominated with a "hole" in the center of the surface. The population response to the surfaces in both species exhibited suppression relative to a grating stimulus. These results reveal the differences in spatial patterns to luminance surfaces in the V1 of mice and monkeys and provide evidence for a shared suppression process relative to grating.
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Affiliation(s)
- Shany Nivinsky Margalit
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Hamutal Slovin
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
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6
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Klaver LMF, Brinkhof LP, Sikkens T, Casado-Román L, Williams AG, van Mourik-Donga L, Mejías JF, Pennartz CMA, Bosman CA. Spontaneous variations in arousal modulate subsequent visual processing and local field potential dynamics in the ferret during quiet wakefulness. Cereb Cortex 2023; 33:7564-7581. [PMID: 36935096 PMCID: PMC10267643 DOI: 10.1093/cercor/bhad061] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 03/21/2023] Open
Abstract
Behavioral states affect neuronal responses throughout the cortex and influence visual processing. Quiet wakefulness (QW) is a behavioral state during which subjects are quiescent but awake and connected to the environment. Here, we examined the effects of pre-stimulus arousal variability on post-stimulus neural activity in the primary visual cortex and posterior parietal cortex in awake ferrets, using pupil diameter as an indicator of arousal. We observed that the power of stimuli-induced alpha (8-12 Hz) decreases when the arousal level increases. The peak of alpha power shifts depending on arousal. High arousal increases inter- and intra-areal coherence. Using a simplified model of laminar circuits, we show that this connectivity pattern is compatible with feedback signals targeting infragranular layers in area posterior parietal cortex and supragranular layers in V1. During high arousal, neurons in V1 displayed higher firing rates at their preferred orientations. Broad-spiking cells in V1 are entrained to high-frequency oscillations (>80 Hz), whereas narrow-spiking neurons are phase-locked to low- (12-18 Hz) and high-frequency (>80 Hz) rhythms. These results indicate that the variability and sensitivity of post-stimulus cortical responses and coherence depend on the pre-stimulus behavioral state and account for the neuronal response variability observed during repeated stimulation.
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Affiliation(s)
- Lianne M F Klaver
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Lotte P Brinkhof
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Tom Sikkens
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Lorena Casado-Román
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Alex G Williams
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Laura van Mourik-Donga
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Jorge F Mejías
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Research Priority Program Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
| | - Cyriel M A Pennartz
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Research Priority Program Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
| | - Conrado A Bosman
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Research Priority Program Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
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7
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Kong NCL, Margalit E, Gardner JL, Norcia AM. Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity. PLoS Comput Biol 2022; 18:e1009739. [PMID: 34995280 PMCID: PMC8775238 DOI: 10.1371/journal.pcbi.1009739] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 01/20/2022] [Accepted: 01/04/2022] [Indexed: 11/18/2022] Open
Abstract
Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have power law exponents of at least one. We also find that the eigenspectra of model representations decay slowly relative to those observed in neurophysiology and that robust models have eigenspectra that decay slightly faster and have higher power law exponents than those of non-robust models. The slow decay of the eigenspectra suggests that substantial variance in the model responses is related to the encoding of fine stimulus features. We therefore investigated the spatial frequency tuning of artificial neurons and found that a large proportion of them preferred high spatial frequencies and that robust models had preferred spatial frequency distributions more aligned with the measured spatial frequency distribution of macaque V1 cells. Furthermore, robust models were quantitatively better models of V1 than non-robust models. Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.
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Affiliation(s)
- Nathan C. L. Kong
- Department of Psychology, Stanford University, Stanford, California, United States of America
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States of America
| | - Eshed Margalit
- Neurosciences Graduate Program, Stanford University, Stanford, California, United States of America
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States of America
| | - Justin L. Gardner
- Department of Psychology, Stanford University, Stanford, California, United States of America
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States of America
| | - Anthony M. Norcia
- Department of Psychology, Stanford University, Stanford, California, United States of America
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States of America
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8
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Mulholland HN, Hein B, Kaschube M, Smith GB. Tightly coupled inhibitory and excitatory functional networks in the developing primary visual cortex. eLife 2021; 10:e72456. [PMID: 34878404 PMCID: PMC8654369 DOI: 10.7554/elife.72456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/11/2021] [Indexed: 11/18/2022] Open
Abstract
Intracortical inhibition plays a critical role in shaping activity patterns in the mature cortex. However, little is known about the structure of inhibition in early development prior to the onset of sensory experience, a time when spontaneous activity exhibits long-range correlations predictive of mature functional networks. Here, using calcium imaging of GABAergic neurons in the ferret visual cortex, we show that spontaneous activity in inhibitory neurons is already highly organized into distributed modular networks before visual experience. Inhibitory neurons exhibit spatially modular activity with long-range correlations and precise local organization that is in quantitative agreement with excitatory networks. Furthermore, excitatory and inhibitory networks are strongly co-aligned at both millimeter and cellular scales. These results demonstrate a remarkable degree of organization in inhibitory networks early in the developing cortex, providing support for computational models of self-organizing networks and suggesting a mechanism for the emergence of distributed functional networks during development.
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Affiliation(s)
| | - Bettina Hein
- Center for Theoretical Neuroscience, Zuckerman Institute, Columbia UniversityNew YorkUnited States
| | - Matthias Kaschube
- Frankfurt Institute for Advanced Studies & Department of Informatics and Mathematics, Goethe UniversityFrankfurt am MainGermany
| | - Gordon B Smith
- Department of Neuroscience, University of MinnesotaMinneapolisUnited States
- Optical Imaging and Brain Sciences Medical Discovery Team, University of MinnesotaMinneapolisUnited States
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9
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Suzuki M, Aru J, Larkum ME. Double-μPeriscope, a tool for multilayer optical recordings, optogenetic stimulations or both. eLife 2021; 10:e72894. [PMID: 34878406 PMCID: PMC8654370 DOI: 10.7554/elife.72894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 11/29/2021] [Indexed: 11/28/2022] Open
Abstract
Intelligent behavior and cognitive functions in mammals depend on cortical microcircuits made up of a variety of excitatory and inhibitory cells that form a forest-like complex across six layers. Mechanistic understanding of cortical microcircuits requires both manipulation and monitoring of multiple layers and interactions between them. However, existing techniques are limited as to simultaneous monitoring and stimulation at different depths without damaging a large volume of cortical tissue. Here, we present a relatively simple and versatile method for delivering light to any two cortical layers simultaneously. The method uses a tiny optical probe consisting of two microprisms mounted on a single shaft. We demonstrate the versatility of the probe in three sets of experiments: first, two distinct cortical layers were optogenetically and independently manipulated; second, one layer was stimulated while the activity of another layer was monitored; third, the activity of thalamic axons distributed in two distinct cortical layers was simultaneously monitored in awake mice. Its simple-design, versatility, small-size, and low-cost allow the probe to be applied widely to address important biological questions.
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Affiliation(s)
- Mototaka Suzuki
- Institute of Biology, Humboldt University of BerlinBerlinGermany
| | - Jaan Aru
- Institute of Biology, Humboldt University of BerlinBerlinGermany
- Institute of Computer Science, University of TartuTartuEstonia
| | - Matthew E Larkum
- Institute of Biology, Humboldt University of BerlinBerlinGermany
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10
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Han C, Wang T, Yang Y, Wu Y, Li Y, Dai W, Zhang Y, Wang B, Yang G, Cao Z, Kang J, Wang G, Li L, Yu H, Yeh CI, Xing D. Multiple gamma rhythms carry distinct spatial frequency information in primary visual cortex. PLoS Biol 2021; 19:e3001466. [PMID: 34932558 PMCID: PMC8691622 DOI: 10.1371/journal.pbio.3001466] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 11/03/2021] [Indexed: 12/26/2022] Open
Abstract
Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.
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Affiliation(s)
- Chuanliang Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yi Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yujie Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weifeng Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yange Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Guanzhong Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ziqi Cao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jian Kang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Gang Wang
- Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Liang Li
- Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Hongbo Yu
- Vision Research Laboratory, Center for Brain Science Research and School of Life Sciences, Fudan University, Shanghai, China
| | - Chun-I Yeh
- Department of Psychology, National Taiwan University, Taipei, Taiwan, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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11
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Larisch R, Gönner L, Teichmann M, Hamker FH. Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity. PLoS Comput Biol 2021; 17:e1009566. [PMID: 34843455 PMCID: PMC8629393 DOI: 10.1371/journal.pcbi.1009566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/15/2021] [Indexed: 11/18/2022] Open
Abstract
Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network. Synaptic plasticity is crucial for the development of efficient input representation in the different sensory cortices, such as the primary visual cortex. Efficient visual representation is determined by two factors: representational efficiency, i.e. how many different input features can be represented, and metabolic efficiency, i.e. how many spikes are required to represent a specific feature. Previous research has pointed out the importance of plasticity at excitatory synapses to achieve high representational efficiency and feedback inhibition as a gain control mechanism for controlling metabolic efficiency. However, it is only partially understood how the influence of inhibitory plasticity on excitatory plasticity can lead to an efficient representation. Using a spiking neural network, we show that plasticity at feed-forward and feedback inhibitory synapses is necessary for the emergence of well-distributed neuronal selectivity to improve representational efficiency. Further, the emergent balance between excitatory and inhibitory currents improves the metabolic efficiency, and leads to contrast-invariant tuning as an inherent network property. Extending previous work, our simulation results highlight the importance of plasticity at inhibitory synapses.
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Affiliation(s)
- René Larisch
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- * E-mail: (RL); (FHH)
| | - Lorenz Gönner
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Faculty of Psychology, Lifespan Developmental Neuroscience, TU Dresden, Dresden, Germany
| | - Michael Teichmann
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
| | - Fred H. Hamker
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Bernstein Center Computational Neuroscience, Berlin, Germany
- * E-mail: (RL); (FHH)
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12
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Jia S, Xing D, Yu Z, Liu JK. Dissecting cascade computational components in spiking neural networks. PLoS Comput Biol 2021; 17:e1009640. [PMID: 34843460 PMCID: PMC8659421 DOI: 10.1371/journal.pcbi.1009640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 12/09/2021] [Accepted: 11/14/2021] [Indexed: 01/15/2023] Open
Abstract
Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity. It is well known that the computation of neuronal circuits is carried out through the staged and cascade structure of different types of neurons. Nevertheless, the information, particularly sensory information, is processed in a network primarily with feedforward connections through different pathways. A peculiar example is the early visual system, where light is transcoded by the retinal cells, routed by the lateral geniculate nucleus, and reached the primary visual cortex. One meticulous interest in recent years is to map out these physical structures of neuronal pathways. However, most methods so far are limited to taking snapshots of a static view of connections between neurons. It remains unclear how to obtain a functional and dynamical neuronal circuit beyond the simple scenarios of a few randomly sampled neurons. Using simulated spiking neural networks of visual pathways with different scenarios of multiple stages, mixed cell types, and natural image stimuli, we demonstrate that a recent computational tool, named spike-triggered non-negative matrix factorization, can resolve these issues. It enables us to recover the entire structural components of neural networks underlying the computation, together with the functional components of each individual neuron. Utilizing it for complex cells of the primary visual cortex allows us to reveal every underpinning of the nonlinear computation. Our results, together with other recent experimental and computational efforts, show that it is possible to systematically dissect neural circuitry with detailed structural and functional components.
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Affiliation(s)
- Shanshan Jia
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhaofei Yu
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
- * E-mail: (ZY); (JKL)
| | - Jian K. Liu
- School of Computing, University of Leeds, Leeds, United Kingdom
- * E-mail: (ZY); (JKL)
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13
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Uchiyama Y, Sakai H, Ando T, Tachibana A, Sadato N. BOLD signal response in primary visual cortex to flickering checkerboard increases with stimulus temporal frequency in older adults. PLoS One 2021; 16:e0259243. [PMID: 34735509 PMCID: PMC8568270 DOI: 10.1371/journal.pone.0259243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 10/15/2021] [Indexed: 11/18/2022] Open
Abstract
Many older adults have difficulty seeing brief visual stimuli which younger adults can easily recognize. The primary visual cortex (V1) may induce this difficulty. However, in neuroimaging studies, the V1 response change to the increase of temporal frequency of visual stimulus in older adults was unclear. Here we investigated the association between the temporal frequency of flickering stimuli and the BOLD activity within V1 in older adults, using surface-based fMRI analysis. The fMRI data from 29 healthy older participants stimulated by contrast-reversing checkerboard at temporal flicker frequencies of 2, 4, and 8 Hz were obtained. The participants also performed a useful field of view (UFOV) test. The slope coefficient of BOLD activity regarding the temporal frequency of the visual stimulus averaged within V1 regions of interest was positive and significantly different from zero. Group analysis in the V1 showed significant clusters with positive slope and no significant clusters with a negative slope. The correlation coefficient between the slope coefficient and UFOV performance was not significant. The results indicated that V1 BOLD response to a flickering visual stimulus increases as the stimulus temporal frequency increases from 2 to 8 Hz in older adults.
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Affiliation(s)
- Yuji Uchiyama
- Human Science Research Domain, Strategic Research Division, Toyota Central R&D Labs., Inc., Nagakute, Aichi, Japan
- * E-mail:
| | - Hiroyuki Sakai
- Human Science Research Domain, Strategic Research Division, Toyota Central R&D Labs., Inc., Nagakute, Aichi, Japan
| | - Takafumi Ando
- Human Science Research Domain, Strategic Research Division, Toyota Central R&D Labs., Inc., Nagakute, Aichi, Japan
| | - Atsumichi Tachibana
- Human Science Research Domain, Strategic Research Division, Toyota Central R&D Labs., Inc., Nagakute, Aichi, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
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14
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Klein N, Siegle JH, Teichert T, Kass RE. Cross-population coupling of neural activity based on Gaussian process current source densities. PLoS Comput Biol 2021; 17:e1009601. [PMID: 34788286 PMCID: PMC8635346 DOI: 10.1371/journal.pcbi.1009601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 12/01/2021] [Accepted: 10/29/2021] [Indexed: 11/19/2022] Open
Abstract
Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical forward models relevant to the data we analyzed. In simulation studies this approach improved identification of source signals compared to existing CSD estimation methods. Using data recorded from primate auditory cortex, we analyzed trial-to-trial fluctuations in both steady-state and task-evoked signals. We found cortical layer-specific phase coupling between two probes and showed that the same analysis applied directly to LFPs did not recover these patterns. We also found task-evoked CSDs to be correlated across probes, at specific cortical depths. Using data from Neuropixels probes in mouse visual areas, we again found evidence for depth-specific phase coupling of primary visual cortex and lateromedial area based on the CSDs.
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Affiliation(s)
- Natalie Klein
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Joshua H. Siegle
- MindScope Program, Allen Institute, Seattle, Washington, United States of America
| | - Tobias Teichert
- Departments of Psychiatry and Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Robert E. Kass
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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15
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Campbell MG, Attinger A, Ocko SA, Ganguli S, Giocomo LM. Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex. Cell Rep 2021; 36:109669. [PMID: 34496249 PMCID: PMC8437084 DOI: 10.1016/j.celrep.2021.109669] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/25/2021] [Accepted: 08/13/2021] [Indexed: 12/01/2022] Open
Abstract
During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains incompletely understood. We combine electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three navigation-relevant areas: primary visual cortex (V1), retrosplenial cortex (RSC), and medial entorhinal cortex (MEC). Compared with V1 and RSC, path integration influences position estimates more in MEC, and conflicts between path integration and landmarks trigger remapping more readily. Whereas MEC codes position prospectively, V1 codes position retrospectively, and RSC is intermediate between the two. Lowered visual contrast increases the influence of path integration on position estimates only in MEC. These properties are most pronounced in a population of MEC neurons, overlapping with grid cells, tuned to distance run in darkness. These results demonstrate the specialized role that path integration plays in MEC compared with other navigation-relevant cortical areas.
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Affiliation(s)
- Malcolm G Campbell
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Alexander Attinger
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Samuel A Ocko
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Surya Ganguli
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA.
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16
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Solomon SS, Tang H, Sussman E, Kohn A. Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans. Cereb Cortex 2021; 31:3136-3152. [PMID: 33683317 DOI: 10.1093/cercor/bhab014] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 12/06/2020] [Accepted: 01/15/2021] [Indexed: 11/14/2022] Open
Abstract
A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.
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Affiliation(s)
- Selina S Solomon
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Huizhen Tang
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Otorhinolaryngology - Head & Neck Surgery, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Elyse Sussman
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Otorhinolaryngology - Head & Neck Surgery, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Adam Kohn
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Ophthalmology and Vision Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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17
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Brown APY, Cossell L, Strom M, Tyson AL, Vélez-Fort M, Margrie TW. Analysis of segmentation ontology reveals the similarities and differences in connectivity onto L2/3 neurons in mouse V1. Sci Rep 2021; 11:4983. [PMID: 33654118 PMCID: PMC7925549 DOI: 10.1038/s41598-021-82353-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 01/15/2021] [Indexed: 12/02/2022] Open
Abstract
Quantitatively comparing brain-wide connectivity of different types of neuron is of vital importance in understanding the function of the mammalian cortex. Here we have designed an analytical approach to examine and compare datasets from hierarchical segmentation ontologies, and applied it to long-range presynaptic connectivity onto excitatory and inhibitory neurons, mainly located in layer 2/3 (L2/3), of mouse primary visual cortex (V1). We find that the origins of long-range connections onto these two general cell classes-as well as their proportions-are quite similar, in contrast to the inputs on to a cell type in L6. These anatomical data suggest that distal inputs received by the general excitatory and inhibitory classes of neuron in L2/3 overlap considerably.
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Affiliation(s)
- Alexander P Y Brown
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Lee Cossell
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Molly Strom
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Adam L Tyson
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Mateo Vélez-Fort
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Troy W Margrie
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK.
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18
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Diamanti EM, Reddy CB, Schröder S, Muzzu T, Harris KD, Saleem AB, Carandini M. Spatial modulation of visual responses arises in cortex with active navigation. eLife 2021; 10:e63705. [PMID: 33538692 PMCID: PMC7861612 DOI: 10.7554/elife.63705] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/12/2021] [Indexed: 01/01/2023] Open
Abstract
During navigation, the visual responses of neurons in mouse primary visual cortex (V1) are modulated by the animal's spatial position. Here we show that this spatial modulation is similarly present across multiple higher visual areas but negligible in the main thalamic pathway into V1. Similar to hippocampus, spatial modulation in visual cortex strengthens with experience and with active behavior. Active navigation in a familiar environment, therefore, enhances the spatial modulation of visual signals starting in the cortex.
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Affiliation(s)
- E Mika Diamanti
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
- CoMPLEX, Department of Computer Science, University College LondonLondonUnited Kingdom
| | - Charu Bai Reddy
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
| | - Sylvia Schröder
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
| | - Tomaso Muzzu
- UCL Institute of Behavioural Neuroscience, University College LondonLondonUnited Kingdom
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Aman B Saleem
- UCL Institute of Behavioural Neuroscience, University College LondonLondonUnited Kingdom
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
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19
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Goldbach HC, Akitake B, Leedy CE, Histed MH. Performance in even a simple perceptual task depends on mouse secondary visual areas. eLife 2021; 10:e62156. [PMID: 33522482 PMCID: PMC7990500 DOI: 10.7554/elife.62156] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 01/29/2021] [Indexed: 12/13/2022] Open
Abstract
Primary visual cortex (V1) in the mouse projects to numerous brain areas, including several secondary visual areas, frontal cortex, and basal ganglia. While it has been demonstrated that optogenetic silencing of V1 strongly impairs visually guided behavior, it is not known which downstream areas are required for visual behaviors. Here we trained mice to perform a contrast-increment change detection task, for which substantial stimulus information is present in V1. Optogenetic silencing of visual responses in secondary visual areas revealed that their activity is required for even this simple visual task. In vivo electrophysiology showed that, although inhibiting secondary visual areas could produce some feedback effects in V1, the principal effect was profound suppression at the location of the optogenetic light. The results show that pathways through secondary visual areas are necessary for even simple visual behaviors.
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Affiliation(s)
- Hannah C Goldbach
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of HealthBethesdaUnited States
| | - Bradley Akitake
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of HealthBethesdaUnited States
| | - Caitlin E Leedy
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of HealthBethesdaUnited States
| | - Mark H Histed
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of HealthBethesdaUnited States
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20
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Piras F, Vecchio D, Ciullo V, Gili T, Banaj N, Piras F, Spalletta G. Sense of external agency is sustained by multisensory functional integration in the somatosensory cortex. Hum Brain Mapp 2020; 41:4024-4040. [PMID: 32667099 PMCID: PMC7469779 DOI: 10.1002/hbm.25107] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/28/2020] [Accepted: 06/09/2020] [Indexed: 12/17/2022] Open
Abstract
"Sense of agency" (SoA), the feeling of control for events caused by one's own actions, is deceived by visuomotor incongruence. Sensorimotor networks are implicated in SoA, however little evidence exists on brain functionality during agency processing. Concurrently, it has been suggested that the brain's intrinsic resting-state (rs) activity has a preliminary influence on processing of agency cues. Here, we investigated the relation between performance in an agency attribution task and functional interactions among brain regions as derived by network analysis of rs functional magnetic resonance imaging. The action-effect delay was adaptively increased (range 90-1,620 ms) and behavioral measures correlated to indices of cognitive processes and appraised self-concepts. They were then regressed on local metrics of rs brain functional connectivity as to isolate the core areas enabling self-agency. Across subjects, the time window for self-agency was 90-625 ms, while the action-effect integration was impacted by self-evaluated personality traits. Neurally, the brain intrinsic organization sustaining consistency in self-agency attribution was characterized by high connectiveness in the secondary visual cortex, and regional segregation in the primary somatosensory area. Decreased connectiveness in the secondary visual area, regional segregation in the superior parietal lobule, and information control within a primary visual cortex-frontal eye fields network sustained self-agency over long-delayed effects. We thus demonstrate that self-agency is grounded on the intrinsic mode of brain function designed to organize information for visuomotor integration. Our observation is relevant for current models of psychopathology in clinical conditions in which both rs activity and sense of agency are altered.
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Affiliation(s)
- Federica Piras
- Department of Clinical and Behavioral Neurology, Neuropsychiatry LaboratoryIRCCS Santa Lucia FoundationRomeItaly
| | - Daniela Vecchio
- Department of Clinical and Behavioral Neurology, Neuropsychiatry LaboratoryIRCCS Santa Lucia FoundationRomeItaly
| | - Valentina Ciullo
- Department of Clinical and Behavioral Neurology, Neuropsychiatry LaboratoryIRCCS Santa Lucia FoundationRomeItaly
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced StudiesLuccaItaly
| | - Nerisa Banaj
- Department of Clinical and Behavioral Neurology, Neuropsychiatry LaboratoryIRCCS Santa Lucia FoundationRomeItaly
| | - Fabrizio Piras
- Department of Clinical and Behavioral Neurology, Neuropsychiatry LaboratoryIRCCS Santa Lucia FoundationRomeItaly
| | - Gianfranco Spalletta
- Department of Clinical and Behavioral Neurology, Neuropsychiatry LaboratoryIRCCS Santa Lucia FoundationRomeItaly
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
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21
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Vanni S, Hokkanen H, Werner F, Angelucci A. Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models. Cereb Cortex 2020; 30:3483-3517. [PMID: 31897474 PMCID: PMC7233004 DOI: 10.1093/cercor/bhz322] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 12/02/2019] [Indexed: 12/22/2022] Open
Abstract
The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.
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Affiliation(s)
- Simo Vanni
- HUS Neurocenter, Department of Neurology, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neurosciences, University of Helsinki, 00100 Helsinki, Finland
| | - Henri Hokkanen
- HUS Neurocenter, Department of Neurology, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neurosciences, University of Helsinki, 00100 Helsinki, Finland
| | - Francesca Werner
- HUS Neurocenter, Department of Neurology, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neurosciences, University of Helsinki, 00100 Helsinki, Finland
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy
| | - Alessandra Angelucci
- Department of Ophthalmology and Visual Sciences, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
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22
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Koch GE, Paulus JP, Coutanche MN. Neural Patterns are More Similar across Individuals during Successful Memory Encoding than during Failed Memory Encoding. Cereb Cortex 2020; 30:3872-3883. [PMID: 32147702 DOI: 10.1093/cercor/bhaa003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 12/20/2019] [Accepted: 01/06/2020] [Indexed: 12/11/2022] Open
Abstract
After experiencing the same episode, some people can recall certain details about it, whereas others cannot. We investigate how common (intersubject) neural patterns during memory encoding influence whether an episode will be subsequently remembered, and how divergence from a common organization is associated with encoding failure. Using functional magnetic resonance imaging with intersubject multivariate analyses, we measured brain activity as people viewed episodes within wildlife videos and then assessed their memory for these episodes. During encoding, greater neural similarity was observed between the people who later remembered an episode (compared with those who did not) within the regions of the declarative memory network (hippocampus, posterior medial cortex [PMC], and dorsal Default Mode Network [dDMN]). The intersubject similarity of the PMC and dDMN was episode-specific. Hippocampal encoding patterns were also more similar between subjects for memory success that was defined after one day, compared with immediately after retrieval. The neural encoding patterns were sufficiently robust and generalizable to train machine learning classifiers to predict future recall success in held-out subjects, and a subset of decodable regions formed a network of shared classifier predictions of subsequent memory success. This work suggests that common neural patterns reflect successful, rather than unsuccessful, encoding across individuals.
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Affiliation(s)
- Griffin E Koch
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15260, USA
| | - John P Paulus
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Marc N Coutanche
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15260, USA
- Brain Institute, University of Pittsburgh, Pittsburgh, PA 15260, USA
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