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Su C, Mendes-Platt RF, Alonso JM, Swadlow HA, Bereshpolova Y. Visual Corticotectal Neurons in Awake Rabbits: Receptive Fields and Driving Monosynaptic Thalamocortical Inputs. J Neurosci 2024; 44:e1945232024. [PMID: 38485258 PMCID: PMC11079980 DOI: 10.1523/jneurosci.1945-23.2024] [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: 10/13/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
The superior colliculus receives powerful synaptic inputs from corticotectal neurons in the visual cortex. The function of these corticotectal neurons remains largely unknown due to a limited understanding of their response properties and connectivity. Here, we use antidromic methods to identify corticotectal neurons in awake male and female rabbits, and measure their axonal conduction times, thalamic inputs and receptive field properties. All corticotectal neurons responded to sinusoidal drifting gratings with a nonlinear (nonsinusoidal) increase in mean firing rate but showed pronounced differences in their ON-OFF receptive field structures that we classified into three groups, Cx, S2, and S1. Cx receptive fields had highly overlapping ON and OFF subfields as classical complex cells, S2 had largely separated ON and OFF subfields as classical simple cells, and S1 had a single ON or OFF subfield. Thus, all corticotectal neurons are homogeneous in their nonlinear spatial summation but very heterogeneous in their spatial integration of ON and OFF inputs. The Cx type had the fastest conducting axons, the highest spontaneous activity, and the strongest and fastest visual responses. The S2 type had the highest orientation selectivity, and the S1 type had the slowest conducting axons. Moreover, our cross-correlation analyses found that a subpopulation of corticotectal neurons with very fast conducting axons and high spontaneous firing rates (largely "Cx" type) receives monosynaptic input from retinotopically aligned thalamic neurons. This previously unrecognized fast-conducting thalamic-mediated corticotectal pathway may provide specialized information to superior colliculus and prime recipient neurons for subsequent corticotectal or retinal synaptic input.
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Affiliation(s)
- Chuyi Su
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut
| | | | - Jose-Manuel Alonso
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut
- Department of Biological Sciences, SUNY-Optometry, New York, New York
| | - Harvey A Swadlow
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut
- Department of Biological Sciences, SUNY-Optometry, New York, New York
| | - Yulia Bereshpolova
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut
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2
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Wang X, Nandy AS, Jadi MP. Laminar compartmentalization of attention modulation in area V4 aligns with the demands of visual processing hierarchy in the cortex. Sci Rep 2023; 13:19558. [PMID: 37945642 PMCID: PMC10636153 DOI: 10.1038/s41598-023-46722-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023] Open
Abstract
Attention selectively enhances neural responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encoding higher level features benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is unknown. We found that attentional modulation in V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial layers show enhanced boosting of low contrast information, while those of deep layers exhibit contrast-independent scaling. Computational modeling suggested the extent of spatial integration of inhibitory neurons as the mechanism behind such laminar differences. Considering that superficial neurons are known to project to higher areas and deep layers to early visual areas, our findings suggest that the interactions between attention and contrast in V4 are compartmentalized, in alignment with the demands of the visual processing hierarchy.
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Affiliation(s)
- Xiang Wang
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
| | - Anirvan S Nandy
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
- Department of Neuroscience, Yale University, New Haven, CT, 06511, USA
| | - Monika P Jadi
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA.
- Department of Psychiatry, Yale University, New Haven, CT, 06511, USA.
- Department of Neuroscience, Yale University, New Haven, CT, 06511, USA.
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3
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Sachse EM, Snyder AC. Dynamic attention signalling in V4: Relation to fast-spiking/non-fast-spiking cell class and population coupling. Eur J Neurosci 2023; 57:918-939. [PMID: 36732934 DOI: 10.1111/ejn.15928] [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: 08/03/2022] [Revised: 01/09/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023]
Abstract
The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time-varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time-varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so-called 'fast-spiking' [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast-spiking (more likely to be non-fast-spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.
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Affiliation(s)
- Elizabeth M Sachse
- Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA
- Neuroscience, University of Minnesota, Minneapolis, Minnesota, USA
| | - Adam C Snyder
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA
- Neuroscience, University of Rochester, Rochester, New York, USA
- Center for Visual Sciences, University of Rochester, Rochester, New York, USA
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4
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Hu W, Zhu S, Briggs F, Doyley MM. Functional ultrasound imaging reveals 3D structure of orientation domains in ferret primary visual cortex. Neuroimage 2023; 268:119889. [PMID: 36681137 PMCID: PMC9999292 DOI: 10.1016/j.neuroimage.2023.119889] [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: 09/23/2022] [Revised: 12/14/2022] [Accepted: 01/17/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND AND PURPOSE The sensory cortex is organized into "maps" that represent sensory space across cortical space. In primary visual cortex (V1) of highly visual mammals, multiple visual feature maps are organized into a functional architecture anchored by orientation domains: regions containing neurons preferring the same stimulus orientation. Although the pinwheel-like structure of orientation domains is well-characterized in the superficial cortical layers in dorsal regions of V1, the 3D shape of orientation domains spanning all 6 cortical layers and across dorsal and ventral regions of V1 has never been revealed. METHODS We utilized an emerging research method in neuroscience, functional ultrasound imaging (fUS), to resolve the 3D structure of orientation domains throughout V1 in anesthetized female ferrets. fUS measures blood flow from which neuronal population activity is inferred with improved spatial resolution over fMRI. RESULTS fUS activations in response to drifting gratings placed at multiple locations in visual space generated unique activation patterns in V1 and visual thalamus, confirming prior observations that fUS can resolve retinotopy. Iso-orientation domains, determined from clusters of activations driven by large oriented gratings, were cone-shaped and present in both dorsal and ventral regions of V1. The spacing between iso-orientation domains was consistent with spacing measured previously using optical imaging methods. CONCLUSIONS Orientation domains are cones rather than columns. Their width and intra-domain distances may vary across dorsal and ventral regions of V1. These findings demonstrate the power of fUS at revealing 3D functional architecture in cortical regions not accessible to traditional surface imaging methods.
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Affiliation(s)
- Wentao Hu
- Department of Electrical and Computer Engineering, University of Rochester, 518 Computer Studies Building, Box 270231, Rochester, NY 14627-2031, USA
| | - Silei Zhu
- Neuroscience Graduate Program, University of Rochester, Rochester, NY, USA
| | - Farran Briggs
- Neuroscience Graduate Program, University of Rochester, Rochester, NY, USA; Ernest J. Del Monte Institute for Neuroscience, University of Rochester, NY, USA; Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester NY, USA; Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA; Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Marvin M Doyley
- Department of Electrical and Computer Engineering, University of Rochester, 518 Computer Studies Building, Box 270231, Rochester, NY 14627-2031, USA.
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Srivastava M, Angel C, Kisvárday RE, Kocsis Z, Stelescu A, Talapka P, Kisvárday Z. Form, synapses and orientation topography of a new cell type in layer 6 of the cat’s primary visual cortex. Sci Rep 2022; 12:15428. [PMID: 36104476 PMCID: PMC9474457 DOI: 10.1038/s41598-022-19746-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/02/2022] [Indexed: 11/19/2022] Open
Abstract
Here we report the morpho-functional features of a novel type of deep-layer neuron. The neuron was selected from a large pool of intracellularly labelled cells based on the large cell body, numerous spine-free dendrites with an overall interneuron morphology. However, the axon gave off long-range axons up to 2.8 mm from the parent soma in layers 5/6 before entering the white matter. The boutons were uniformly distributed along the axon without forming distinct clusters. Dendritic length, surface area and volume values were at least 3 times larger than any known cortical neuron types with the exception of giant pyramidal cells of layer 5. Electron microscopy of the boutons revealed that they targeted dendritic spines (78%) and less frequently dendritic shafts (22%). Nearly half of the postsynaptic dendrites were immunopositive to GABA. Superimposing the axonal field on the orientation map obtained with optical imaging showed a preponderance of boutons to cross-orientations (38%) and an equal representation of iso- and oblique orientations (31%). The results suggest an integrating role for the layer 6 stellate neuron which projects to a functionally broad range of neurons in the deep cortical layers and to other cortical and/or subcortical regions.
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6
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Dual counterstream architecture may support separation between vision and predictions. Conscious Cogn 2022; 103:103375. [DOI: 10.1016/j.concog.2022.103375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/03/2021] [Accepted: 06/28/2022] [Indexed: 11/24/2022]
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Kim YJ, Peterson BB, Crook JD, Joo HR, Wu J, Puller C, Robinson FR, Gamlin PD, Yau KW, Viana F, Troy JB, Smith RG, Packer OS, Detwiler PB, Dacey DM. Origins of direction selectivity in the primate retina. Nat Commun 2022; 13:2862. [PMID: 35606344 PMCID: PMC9126974 DOI: 10.1038/s41467-022-30405-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/27/2022] [Indexed: 12/22/2022] Open
Abstract
From mouse to primate, there is a striking discontinuity in our current understanding of the neural coding of motion direction. In non-primate mammals, directionally selective cell types and circuits are a signature feature of the retina, situated at the earliest stage of the visual process. In primates, by contrast, direction selectivity is a hallmark of motion processing areas in visual cortex, but has not been found in the retina, despite significant effort. Here we combined functional recordings of light-evoked responses and connectomic reconstruction to identify diverse direction-selective cell types in the macaque monkey retina with distinctive physiological properties and synaptic motifs. This circuitry includes an ON-OFF ganglion cell type, a spiking, ON-OFF polyaxonal amacrine cell and the starburst amacrine cell, all of which show direction selectivity. Moreover, we discovered that macaque starburst cells possess a strong, non-GABAergic, antagonistic surround mediated by input from excitatory bipolar cells that is critical for the generation of radial motion sensitivity in these cells. Our findings open a door to investigation of a precortical circuitry that computes motion direction in the primate visual system.
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Affiliation(s)
- Yeon Jin Kim
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA
| | - Beth B Peterson
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA
| | - Joanna D Crook
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA
| | - Hannah R Joo
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA
| | - Jiajia Wu
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Christian Puller
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA
| | - Farrel R Robinson
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA
- Washington National Primate Research Center, Seattle, WA, 98195, USA
| | - Paul D Gamlin
- Department of Ophthalmology and Vision Sciences, University of Alabama at Birmingham, Birmingham, AL, 35294-4390, USA
| | - King-Wai Yau
- Departments of Neuroscience and Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205-2185, USA
| | - Felix Viana
- Institute of Neuroscience, UMH-CSIC, San Juan de Alicante, 03550, Spain
| | - John B Troy
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Robert G Smith
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Orin S Packer
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA
| | - Peter B Detwiler
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, 98195, USA
| | - Dennis M Dacey
- Department of Biological Structure, University of Washington, Seattle, WA, 98195, USA.
- Washington National Primate Research Center, Seattle, WA, 98195, USA.
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8
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Rockland KS. Notes on Visual Cortical Feedback and Feedforward Connections. Front Syst Neurosci 2022; 16:784310. [PMID: 35153685 PMCID: PMC8831541 DOI: 10.3389/fnsys.2022.784310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/06/2022] [Indexed: 11/29/2022] Open
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9
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Yang Y, Wang T, Li Y, Dai W, Yang G, Han C, Wu Y, Xing D. Coding strategy for surface luminance switches in the primary visual cortex of the awake monkey. Nat Commun 2022; 13:286. [PMID: 35022404 PMCID: PMC8755737 DOI: 10.1038/s41467-021-27892-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 12/22/2021] [Indexed: 11/17/2022] Open
Abstract
Both surface luminance and edge contrast of an object are essential features for object identification. However, cortical processing of surface luminance remains unclear. In this study, we aim to understand how the primary visual cortex (V1) processes surface luminance information across its different layers. We report that edge-driven responses are stronger than surface-driven responses in V1 input layers, but luminance information is coded more accurately by surface responses. In V1 output layers, the advantage of edge over surface responses increased eight times and luminance information was coded more accurately at edges. Further analysis of neural dynamics shows that such substantial changes for neural responses and luminance coding are mainly due to non-local cortical inhibition in V1’s output layers. Our results suggest that non-local cortical inhibition modulates the responses elicited by the surfaces and edges of objects, and that switching the coding strategy in V1 promotes efficient coding for luminance. How brightness is encoded in the visual cortex remains incompletely understood. By recording from macaque V1, the authors revealed a switch from surface to edge encoding that is mediated by widespread inhibition in the output layers of the cortex.
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Affiliation(s)
- Yi Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Tian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, College of Life Sciences, 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, 100875, China
| | - Weifeng Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Guanzhong Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Chuanliang Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yujie Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
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10
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Rockland KS. Cytochrome oxidase "blobs": a call for more anatomy. Brain Struct Funct 2021; 226:2793-2806. [PMID: 34382115 PMCID: PMC8778949 DOI: 10.1007/s00429-021-02360-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 07/31/2021] [Indexed: 11/29/2022]
Abstract
An ordered relation of structure and function has been a cornerstone in thinking about brain organization. Like the brain itself, however, this is not straightforward and is confounded both by functional intricacy and structural plasticity (many routes to a given outcome). As a striking case of putative structure-function correlation, this mini-review focuses on the relatively well-characterized pattern of cytochrome oxidase (CO) blobs (aka "patches" or "puffs") in the supragranular layers of macaque monkey visual cortex. The pattern is without doubt visually compelling, and the semi-dichotomous array of CO+ blobs and CO- interblobs is consistent with multiple studies reporting compartment-specific preferential connectivity and distinctive physiological response properties. Nevertheless, as briefly reviewed here, the finer anatomical organization of this system is surprisingly under-investigated, and the relation to functional aspects, therefore, unclear. Microcircuitry, cell type, and three-dimensional spatiotemporal level investigations of the CO+ CO- pattern are needed and may open new views to structure-function organization of visual cortex, and to phylogenetic and ontogenetic comparisons across nonhuman primates (NHP), and between NHP and humans.
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Affiliation(s)
- Kathleen S Rockland
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 72 East Concord St., Boston, MA, 02118, USA.
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11
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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: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [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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12
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Contribution of the slow motion mechanism to global motion revealed by an MAE technique. Sci Rep 2021; 11:3995. [PMID: 33597567 PMCID: PMC7889884 DOI: 10.1038/s41598-021-82900-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 01/21/2021] [Indexed: 11/08/2022] Open
Abstract
Two different motion mechanisms have been identified with motion aftereffect (MAE). (1) A slow motion mechanism, accessed by a static MAE, is sensitive to high-spatial and low-temporal frequency; (2) a fast motion mechanism, accessed by a flicker MAE, is sensitive to low-spatial and high-temporal frequency. We examined their respective responses to global motion after adapting to a global motion pattern constructed of multiple compound Gabor patches arranged circularly. Each compound Gabor patch contained two gratings at different spatial frequencies (0.53 and 2.13 cpd) drifting in opposite directions. The participants reported the direction and duration of the MAE for a variety of global motion patterns. We discovered that static MAE durations depended on the global motion patterns, e.g., longer MAE duration to patches arranged to see rotation than to random motion (Exp 1), and increase with global motion strength (patch number in Exp 2). In contrast, flicker MAEs durations are similar across different patterns and adaptation strength. Further, the global integration occurred at the adaptation stage, rather than at the test stage (Exp 3). These results suggest that slow motion mechanism, assessed by static MAE, integrate motion signals over space while fast motion mechanisms do not, at least under the conditions used.
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13
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Rockland KS. A Closer Look at Corticothalamic "Loops". Front Neural Circuits 2021; 15:632668. [PMID: 33603649 PMCID: PMC7884447 DOI: 10.3389/fncir.2021.632668] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 01/13/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Kathleen S Rockland
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, United States
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14
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Abstract
The response to contrast is one of the most important functions of the macaque primary visual cortex, V1, but up to now there has not been an adequate theory for it. To fill this gap in our understanding of cortical function, we built and analyzed a new large-scale, biologically constrained model of the input layer, 4Cα, of macaque V1. We called the new model CSY2. We challenged CSY2 with a three-parameter family of visual stimuli that varied in contrast, orientation, and spatial frequency. CSY2 accurately simulated experimental data and made many new predictions. It accounted for 1) the shapes of firing-rate-versus-contrast functions, 2) orientation and spatial frequency tuning versus contrast, and 3) the approximate contrast-invariance of cortical activity maps. Post-analysis revealed that the mechanisms that were needed to produce the successful simulations of contrast response included strong recurrent excitation and inhibition that find dynamic equilibria across the cortical surface, dynamic feedback between L6 and L4, and synaptic dynamics like inhibitory synaptic depression.
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15
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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: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [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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