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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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Ghosh S, Maunsell JHR. Locus coeruleus norepinephrine contributes to visual-spatial attention by selectively enhancing perceptual sensitivity. Neuron 2024:S0896-6273(24)00239-3. [PMID: 38701788 DOI: 10.1016/j.neuron.2024.04.001] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/07/2024] [Accepted: 04/02/2024] [Indexed: 05/05/2024]
Abstract
Selectively focusing on a behaviorally relevant stimulus while ignoring irrelevant stimuli improves perception. Enhanced neuronal response gain is thought to support attention-related improvements in detection and discrimination. However, understanding of the neuronal pathways regulating perceptual sensitivity remains limited. Here, we report that responses of norepinephrine (NE) neurons in the locus coeruleus (LC) of non-human primates to behaviorally relevant sensory stimuli promote visual discrimination in a spatially selective way. LC-NE neurons spike in response to a visual stimulus appearing in the contralateral hemifield only when that stimulus is attended. This spiking is associated with enhanced behavioral sensitivity, is independent of motor control, and is absent on error trials. Furthermore, optogenetically activating LC-NE neurons selectively improves monkeys' contralateral stimulus detection without affecting motor criteria, supporting NE's causal role in granular cognitive control of selective attention at a cellular level, beyond its known diffuse and non-selective functions.
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Affiliation(s)
- Supriya Ghosh
- Department of Neurobiology and Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA.
| | - John H R Maunsell
- Department of Neurobiology and Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
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3
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Zayyad ZA, Maunsell JHR, MacLean JN. Normalization in mouse primary visual cortex. PLoS One 2023; 18:e0295140. [PMID: 38109430 PMCID: PMC10727357 DOI: 10.1371/journal.pone.0295140] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/16/2023] [Indexed: 12/20/2023] Open
Abstract
When multiple stimuli appear together in the receptive field of a visual cortical neuron, the response is typically close to the average of that neuron's response to each individual stimulus. The departure from a linear sum of each individual response is referred to as normalization. In mammals, normalization has been best characterized in the visual cortex of macaques and cats. Here we study visually evoked normalization in the visual cortex of awake mice using imaging of calcium indicators in large populations of layer 2/3 (L2/3) V1 excitatory neurons and electrophysiological recordings across layers in V1. Regardless of recording method, mouse visual cortical neurons exhibit normalization to varying degrees. The distributions of normalization strength are similar to those described in cats and macaques, albeit slightly weaker on average.
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Affiliation(s)
- Zaina A. Zayyad
- Interdisciplinary Scientist Training Program, University of Chicago Pritzker School of Medicine, Chicago, IL, United States of America
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
| | - John H. R. Maunsell
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
- Department of Neurobiology, University of Chicago, Chicago, IL, United States of America
- Neuroscience Institute, University of Chicago, Chicago, IL, United States of America
| | - Jason N. MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
- Department of Neurobiology, University of Chicago, Chicago, IL, United States of America
- Neuroscience Institute, University of Chicago, Chicago, IL, United States of America
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4
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Ghosh S, Maunsell JHR. Rodent attention: Probing the mouse mind with reverse correlation. Curr Biol 2023; 33:R916-R918. [PMID: 37699352 DOI: 10.1016/j.cub.2023.07.015] [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] [Indexed: 09/14/2023]
Abstract
A novel approach to studying attention in mice reveals processes similar to those in humans and lays out an efficient way to explore its neuronal correlates in a genetically tractable animal model.
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Affiliation(s)
- Supriya Ghosh
- Department of Neurobiology and Neuroscience Institute, University of Chicago, 5841 S. Maryland Avenue, Chicago, IL 60637, USA
| | - John H R Maunsell
- Department of Neurobiology and Neuroscience Institute, University of Chicago, 5841 S. Maryland Avenue, Chicago, IL 60637, USA.
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5
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Cone JJ, Mitchell AO, Parker RK, Maunsell JHR. Temporal weighting of cortical and subcortical spikes reveals stimulus dependent differences in their contributions to behavior. bioRxiv 2023:2023.08.23.554473. [PMID: 37662213 PMCID: PMC10473714 DOI: 10.1101/2023.08.23.554473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
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 V1 supporting perception of visual features and the SC regulating orienting to visual inputs. However, growing evidence suggests that the SC supports perception of many of the same visual features traditionally associated with V1. To distinguish V1 and SC contributions to visual processing, it is critical to determine whether both areas causally contribute to perception of specific visual stimuli. Here, mice reported changes in visual contrast or luminance near perceptual threshold while we presented white noise patterns of optogenetic stimulation 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 SC are critical for detection of both luminance or 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 perception 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 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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6
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Wei L, Mitchell AO, Maunsell JHR. Increments in visual motion coherence are more readily detected than decrements. J Vis 2023; 23:18. [PMID: 37223942 DOI: 10.1167/jov.23.5.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023] Open
Abstract
Understanding the circuits that access and read out information in the cerebral cortex to guide behavior remains a challenge for systems-level neuroscience. Recent optogenetic experiments targeting specific cell classes in mouse primary visual cortex (V1) have shown that mice are sensitive to optically-induced increases in V1 spiking but are relatively insensitive to decreases in neuronal spiking of similar magnitude and time course. This asymmetry suggests that the readout of signals from cortex depends preferentially on increases in spike rate. We investigated whether humans display a similar asymmetry by measuring thresholds for detecting changes in the motion coherence of dynamic random dot stimuli. The middle temporal visual area (MT) has been shown to play an important role in discriminating random dot stimuli, and the responses of its individual neurons to dynamic random dots are well characterized. Although both increments and decrements in motion coherence have heterogeneous effects on MT responses, increments cause on average more increases in firing rates. Consistent with this, we found that subjects are more sensitive to increments of random dot motion coherence than to decrements of coherence. The magnitude of the difference in detectability was consistent with the expected difference in neuronal signal-to-noise associated with MT spike rate increases driven by coherence increments and decrements. The results add strength to the notion that the circuit mechanisms that read out cortical signals are relatively insensitive to decrements in cortical spiking.
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Affiliation(s)
- Lai Wei
- Department of Neuroscience and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - Autumn O Mitchell
- Department of Neuroscience and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - John H R Maunsell
- Department of Neuroscience and Neuroscience Institute, University of Chicago, Chicago, IL, USA
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7
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Zayyad ZA, Maunsell JHR, MacLean JN. Normalization in mouse primary visual cortex. bioRxiv 2023:2023.04.18.537260. [PMID: 37131716 PMCID: PMC10153131 DOI: 10.1101/2023.04.18.537260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
When multiple stimuli appear together in the receptive field of a visual cortical neuron, the response is typically close to the average of that neuron's response to each individual stimulus. The departure from a linear sum of each individual response is referred to as normalization. In mammals, normalization has been best characterized in the visual cortex of macaques and cats. Here we study visually evoked normalization in the visual cortex of awake mice using optical imaging of calcium indicators in large populations of layer 2/3 (L2/3) V1 excitatory neurons and electrophysiological recordings across layers in V1. Regardless of recording method, mouse visual cortical neurons exhibit normalization to varying degrees. The distributions of normalization strength are similar to those described in cats and macaques, albeit slightly weaker on average.
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Affiliation(s)
- Zaina A. Zayyad
- Interdisciplinary Scientist Training Program, University of Chicago Pritzker School of Medicine, Chicago, IL, United States of America
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
| | - John H. R. Maunsell
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
- Department of Neurobiology, University of Chicago, Chicago, IL, United States of America
- Neuroscience Institute, University of Chicago, Chicago, IL, United States of America
| | - Jason N. MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
- Department of Neurobiology, University of Chicago, Chicago, IL, United States of America
- Neuroscience Institute, University of Chicago, Chicago, IL, United States of America
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8
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Ghosh S, Maunsell JHR. Neuronal correlates of selective attention and effort in visual area V4 are invariant of motivational context. Sci Adv 2022; 8:eabc8812. [PMID: 35687684 PMCID: PMC9187239 DOI: 10.1126/sciadv.abc8812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 04/28/2022] [Indexed: 06/15/2023]
Abstract
Task demands can differentially engage two fundamental attention components: selectivity (spatial bias) and effort (total nonselective attentional intensity). The relative contributions and interactions of these components in modulating neuronal signals remain unknown. We recorded V4 neurons while monkeys' spatially selective attention and effort were independently controlled by adjusting either task difficulty or reward size at two locations. Neurons were robustly modulated by either selective attention or effort. Notably, increasing overall effort to improve performance at a distant site reduced neuronal responses even when performance was unchanged for receptive field stimuli. This interaction between attentional selectivity and effort was evident in single-trial spiking and can be explained by divisive normalization of spatially distributed behavioral performance at the single-neuron level. Changing motivation using task difficulty or reward produced indistinguishable effects. These results provide a cellular-level mechanism of how attention components integrate to modulate sensory processing in different motivational contexts.
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9
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Day-Cooney J, Cone JJ, Maunsell JHR. Perceptual Weighting of V1 Spikes Revealed by Optogenetic White Noise Stimulation. J Neurosci 2022; 42:3122-3132. [PMID: 35232760 PMCID: PMC8994541 DOI: 10.1523/jneurosci.1736-21.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 08/26/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 11/21/2022] Open
Abstract
During visually guided behaviors, mere hundreds of milliseconds can elapse between a sensory input and its associated behavioral response. How spikes occurring at different times are integrated to drive perception and action remains poorly understood. We delivered random trains of optogenetic stimulation (white noise) to excite inhibitory interneurons in V1 of mice of both sexes while they performed a visual detection task. We then performed a reverse correlation analysis on the optogenetic stimuli to generate a neuronal-behavioral kernel, an unbiased, temporally precise estimate of how suppression of V1 spiking at different moments around the onset of a visual stimulus affects detection of that stimulus. Electrophysiological recordings enabled us to capture the effects of optogenetic stimuli on V1 responsivity and revealed that the earliest stimulus-evoked spikes are preferentially weighted for guiding behavior. These data demonstrate that white noise optogenetic stimulation is a powerful tool for understanding how patterns of spiking in neuronal populations are decoded in generating perception and action.SIGNIFICANCE STATEMENT During visually guided actions, continuous chains of neurons connect our retinas to our motoneurons. To unravel circuit contributions to behavior, it is crucial to establish the relative functional position(s) that different neural structures occupy in processing and relaying the signals that support rapid, precise responses. To address this question, we randomly inhibited activity in mouse V1 throughout the stimulus-response cycle while the animals did many repetitions of a visual task. The period that led to impaired performance corresponded to the earliest stimulus-driven response in V1, with no effect of inhibition immediately before or during late stages of the stimulus-driven response. This approach offers experimenters a powerful method for uncovering the temporal weighting of spikes from stimulus to response.
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Affiliation(s)
- Julian Day-Cooney
- Department of Neurobiology and Neuroscience Institute, University of Chicago, Chicago, Illinois 60637
| | - Jackson J Cone
- Department of Neurobiology and Neuroscience Institute, University of Chicago, Chicago, Illinois 60637
| | - John H R Maunsell
- Department of Neurobiology and Neuroscience Institute, University of Chicago, Chicago, Illinois 60637
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10
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Abbott LF, Bock DD, Callaway EM, Denk W, Dulac C, Fairhall AL, Fiete I, Harris KM, Helmstaedter M, Jain V, Kasthuri N, LeCun Y, Lichtman JW, Littlewood PB, Luo L, Maunsell JHR, Reid RC, Rosen BR, Rubin GM, Sejnowski TJ, Seung HS, Svoboda K, Tank DW, Tsao D, Van Essen DC. The Mind of a Mouse. Cell 2021; 182:1372-1376. [PMID: 32946777 DOI: 10.1016/j.cell.2020.08.010] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.
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Affiliation(s)
- Larry F Abbott
- Zuckerman Mind, Brain and Behavior Institute, Department of Neuroscience, Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA
| | - Davi D Bock
- Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | | | - Winfried Denk
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Catherine Dulac
- Howard Hughes Medical Institute and Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics and Computational Neuroscience Center, University of Washington, Seattle, WA, USA
| | - Ila Fiete
- Department of Brain and Cognitive Sciences and McGovern Institute, MIT, Cambridge, MA, USA
| | - Kristen M Harris
- Center for Learning and Memory, Institute for Neuroscience, University of Texas - Austin, Austin, TX, USA
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Viren Jain
- Google Research, Mountain View, CA, USA.
| | - Narayanan Kasthuri
- Argonne National Laboratory and Department of Neurobiology, University of Chicago, Chicago, IL, USA
| | - Yann LeCun
- Courant Institute, Center for Data Science and Center for Neural Science, New York University and Facebook AI Research, New York, NY, USA
| | - Jeff W Lichtman
- Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| | - Peter B Littlewood
- Department of Physics and James Franck Institute, University of Chicago, Chicago, IL, USA
| | - Liqun Luo
- Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, CA, USA
| | - John H R Maunsell
- Department of Neurobiology and Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging and Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Terrence J Sejnowski
- Salk Institute for Biological Studies, La Jolla, CA, USA; Division of Biological Sciences, University of California, San Diego, San Diego, CA, USA
| | - H Sebastian Seung
- Department of Computer Science and Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - David W Tank
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Doris Tsao
- Division of Biology and Biological Engineering, Tianqiao and Chrissy Chen Institute for Neuroscience and Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA, USA
| | - David C Van Essen
- Neuroscience Department, Washington University School of Medicine, St. Louis, MO, USA
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11
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Abstract
Understanding how activity of visual neurons represents distinct components of attention and their dynamics that account for improved visual performance remains elusive because single-unit experiments have not isolated the intensive aspect of attention from attentional selectivity. We isolated attentional intensity and its single trial dynamics as determined by spatially non-selective attentional performance in an orientation discrimination task while recording from neurons in monkey visual area V4. We found that attentional intensity is a distinct cognitive signal that can be distinguished from spatial selectivity, reward expectations and motor actions. V4 spiking on single trials encodes a combination of sensory and cognitive signals on different time scales. Attentional intensity and the detection of behaviorally relevant sensory signals are well represented, but immediate reward expectation and behavioral choices are poorly represented in V4 spiking. These results provide a detailed representation of perceptual and cognitive signals in V4 that are crucial for attentional performance.
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Affiliation(s)
- Supriya Ghosh
- Department of Neurobiology and Neuroscience Institute, The University of Chicago, Chicago, IL, USA.
| | - John H R Maunsell
- Department of Neurobiology and Neuroscience Institute, The University of Chicago, Chicago, IL, USA
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12
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Tremblay S, Acker L, Afraz A, Albaugh DL, Amita H, Andrei AR, Angelucci A, Aschner A, Balan PF, Basso MA, Benvenuti G, Bohlen MO, Caiola MJ, Calcedo R, Cavanaugh J, Chen Y, Chen S, Chernov MM, Clark AM, Dai J, Debes SR, Deisseroth K, Desimone R, Dragoi V, Egger SW, Eldridge MAG, El-Nahal HG, Fabbrini F, Federer F, Fetsch CR, Fortuna MG, Friedman RM, Fujii N, Gail A, Galvan A, Ghosh S, Gieselmann MA, Gulli RA, Hikosaka O, Hosseini EA, Hu X, Hüer J, Inoue KI, Janz R, Jazayeri M, Jiang R, Ju N, Kar K, Klein C, Kohn A, Komatsu M, Maeda K, Martinez-Trujillo JC, Matsumoto M, Maunsell JHR, Mendoza-Halliday D, Monosov IE, Muers RS, Nurminen L, Ortiz-Rios M, O'Shea DJ, Palfi S, Petkov CI, Pojoga S, Rajalingham R, Ramakrishnan C, Remington ED, Revsine C, Roe AW, Sabes PN, Saunders RC, Scherberger H, Schmid MC, Schultz W, Seidemann E, Senova YS, Shadlen MN, Sheinberg DL, Siu C, Smith Y, Solomon SS, Sommer MA, Spudich JL, Stauffer WR, Takada M, Tang S, Thiele A, Treue S, Vanduffel W, Vogels R, Whitmire MP, Wichmann T, Wurtz RH, Xu H, Yazdan-Shahmorad A, Shenoy KV, DiCarlo JJ, Platt ML. An Open Resource for Non-human Primate Optogenetics. Neuron 2020; 108:1075-1090.e6. [PMID: 33080229 PMCID: PMC7962465 DOI: 10.1016/j.neuron.2020.09.027] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [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: 04/30/2020] [Revised: 07/28/2020] [Accepted: 09/21/2020] [Indexed: 12/26/2022]
Abstract
Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.
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Affiliation(s)
- Sébastien Tremblay
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Leah Acker
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Arash Afraz
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Daniel L Albaugh
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Hidetoshi Amita
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ariana R Andrei
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Alessandra Angelucci
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Amir Aschner
- Dominik P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Puiu F Balan
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium
| | - Michele A Basso
- Departments of Psychiatry and Biobehavioral Sciences and Neurobiology, UCLA, Los Angeles, CA 90095, USA
| | - Giacomo Benvenuti
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Martin O Bohlen
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Michael J Caiola
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Roberto Calcedo
- Gene Therapy Program, Department of Medicine, University of Pennsylvania, Philadelphia, PA 19014, USA
| | - James Cavanaugh
- Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD 20982, USA
| | - Yuzhi Chen
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Spencer Chen
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Mykyta M Chernov
- Division of Neuroscience, Oregon National Primate Resource Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Andrew M Clark
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Ji Dai
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen 518055, China
| | - Samantha R Debes
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Karl Deisseroth
- Neuroscience Program, Departments of Bioengineering, Psychiatry, and Behavioral Science, Wu Tsai Neurosciences Institute, Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Robert Desimone
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Seth W Egger
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Mark A G Eldridge
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA
| | - Hala G El-Nahal
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Francesco Fabbrini
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Frederick Federer
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Christopher R Fetsch
- The Solomon H. Snyder Department of Neuroscience & Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Michal G Fortuna
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany
| | - Robert M Friedman
- Division of Neuroscience, Oregon National Primate Resource Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Naotaka Fujii
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Alexander Gail
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany; Faculty for Biology and Psychology, University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Adriana Galvan
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Supriya Ghosh
- Department of Neurobiology and Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL 60637, USA
| | - Marc Alwin Gieselmann
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Roberto A Gulli
- Zuckerman Institute, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - Okihide Hikosaka
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eghbal A Hosseini
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xing Hu
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Janina Hüer
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany
| | - Ken-Ichi Inoue
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan; PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
| | - Roger Janz
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Mehrdad Jazayeri
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rundong Jiang
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Niansheng Ju
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Kohitij Kar
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Carsten Klein
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Adam Kohn
- Dominik P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Ophthalmology and Visual 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
| | - Misako Komatsu
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Kazutaka Maeda
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Julio C Martinez-Trujillo
- Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Masayuki Matsumoto
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan; Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - John H R Maunsell
- Department of Neurobiology and Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL 60637, USA
| | - Diego Mendoza-Halliday
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ilya E Monosov
- Department of Neuroscience, Biomedical Engineering, Electrical Engineering, Neurosurgery and Pain Center, Washington University, St. Louis, MO 63110, USA
| | - Ross S Muers
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Lauri Nurminen
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Michael Ortiz-Rios
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany; Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Daniel J O'Shea
- Department of Electrical Engineering, Wu Tsai Neurosciences Institute, and Bio-X Institute, and Neuroscience Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Stéphane Palfi
- Department of Neurosurgery, Assistance Publique-Hopitaux de Paris (APHP), U955 INSERM IMRB eq.15, University of Paris 12 UPEC, Faculté de Médecine, Créteil 94010, France
| | - Christopher I Petkov
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Sorin Pojoga
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Rishi Rajalingham
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Charu Ramakrishnan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Evan D Remington
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cambria Revsine
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA; Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA
| | - Anna W Roe
- Division of Neuroscience, Oregon National Primate Resource Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Interdisciplinary Institute of Neuroscience and Technology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310029, China; Key Laboratory of Biomedical Engineering of the Ministry of Education, Zhejiang University, Hangzhou 310029, China
| | - Philip N Sabes
- Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Richard C Saunders
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA
| | - Hansjörg Scherberger
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany; Faculty for Biology and Psychology, University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Michael C Schmid
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK; Department of Neurosciences and Movement Sciences, Faculty of Science and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Wolfram Schultz
- Department of Physiology, Development of Neuroscience, University of Cambridge, Cambridge CB3 0LT, UK
| | - Eyal Seidemann
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Yann-Suhan Senova
- Department of Neurosurgery, Assistance Publique-Hopitaux de Paris (APHP), U955 INSERM IMRB eq.15, University of Paris 12 UPEC, Faculté de Médecine, Créteil 94010, France
| | - Michael N Shadlen
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science & Howard Hughes Medical Institute, Columbia University, NY 10027, USA
| | - David L Sheinberg
- Department of Neuroscience and Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA
| | - Caitlin Siu
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Yoland Smith
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Selina S Solomon
- Dominik P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Marc A Sommer
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - John L Spudich
- Department of Biochemistry and Molecular Biology, McGovern Medical School, The University of Texas-Houston, Houston, TX 77030, USA
| | - William R Stauffer
- Systems Neuroscience Institute, Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Masahiko Takada
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Shiming Tang
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Alexander Thiele
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Stefan Treue
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany; Faculty for Biology and Psychology, University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Wim Vanduffel
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium; MGH Martinos Center, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02144, USA
| | - Rufin Vogels
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Matthew P Whitmire
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Thomas Wichmann
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Robert H Wurtz
- Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD 20982, USA
| | - Haoran Xu
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Azadeh Yazdan-Shahmorad
- Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Departments of Bioengineering and Electrical and Computer Engineering, Washington National Primate Research Center, University of Washington, Seattle, WA 98105, USA
| | - Krishna V Shenoy
- Departments of Electrical Engineering, Bioengineering, and Neurobiology, Wu Tsai Neurosciences Institute and Bio-X Institute, Neuroscience Graduate Program, and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - James J DiCarlo
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Michael L Platt
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Marketing, Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
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13
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Cone JJ, Scantlen MD, Histed MH, Maunsell JHR. Different Inhibitory Interneuron Cell Classes Make Distinct Contributions to Visual Contrast Perception. eNeuro 2019; 6:ENEURO.0337-18.2019. [PMID: 30868104 PMCID: PMC6414440 DOI: 10.1523/eneuro.0337-18.2019] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [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: 08/12/2018] [Revised: 12/21/2018] [Accepted: 01/12/2019] [Indexed: 01/19/2023] Open
Abstract
While recent work has revealed how different inhibitory interneurons influence responses of cortical neurons to sensory stimuli, little is known about their distinct contributions to sensory perception. Here, we optogenetically activated different genetically defined interneurons [parvalbumin (PV), somatostatin (SST), vasoactive intestinal peptide (VIP)] in visual cortex (V1) of mice working at threshold in a contrast increment detection task. The visual stimulus was paired with optogenetic stimulation to assess how enhancing V1 inhibitory neuron activity during visual processing altered task performance. PV or SST activation impaired, while VIP stimulation improved, contrast increment detection. The impairment produced by PV or SST activation persisted over several weeks of testing. In contrast, mice learned to reliably detect VIP activation in the absence of any natural visual stimulus. Thus, different inhibitory signals make distinct contributions to visual contrast perception.
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Affiliation(s)
- Jackson J. Cone
- Department of Neurobiology, University of Chicago, Chicago, IL 60637
| | - Megan D. Scantlen
- Department of Neurobiology, University of Chicago, Chicago, IL 60637
| | - Mark H. Histed
- Unit on Neural Computation and Behavior, National Institute of Mental Health, Bethesda, MD 20814
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14
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Ni AM, Maunsell JHR. Spatially tuned normalization explains attention modulation variance within neurons. J Neurophysiol 2017; 118:1903-1913. [PMID: 28701536 DOI: 10.1152/jn.00218.2017] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 07/11/2017] [Accepted: 07/11/2017] [Indexed: 11/22/2022] Open
Abstract
Spatial attention improves perception of attended parts of a scene, a behavioral enhancement accompanied by modulations of neuronal firing rates. These modulations vary in size across neurons in the same brain area. Models of normalization explain much of this variance in attention modulation with differences in tuned normalization across neurons (Lee J, Maunsell JHR. PLoS One 4: e4651, 2009; Ni AM, Ray S, Maunsell JHR. Neuron 73: 803-813, 2012). However, recent studies suggest that normalization tuning varies with spatial location both across and within neurons (Ruff DA, Alberts JJ, Cohen MR. J Neurophysiol 116: 1375-1386, 2016; Verhoef BE, Maunsell JHR. eLife 5: e17256, 2016). Here we show directly that attention modulation and normalization tuning do in fact covary within individual neurons, in addition to across neurons as previously demonstrated. We recorded the activity of isolated neurons in the middle temporal area of two rhesus monkeys as they performed a change-detection task that controlled the focus of spatial attention. Using the same two drifting Gabor stimuli and the same two receptive field locations for each neuron, we found that switching which stimulus was presented at which location affected both attention modulation and normalization in a correlated way within neurons. We present an equal-maximum-suppression spatially tuned normalization model that explains this covariance both across and within neurons: each stimulus generates equally strong suppression of its own excitatory drive, but its suppression of distant stimuli is typically less. This new model specifies how the tuned normalization associated with each stimulus location varies across space both within and across neurons, changing our understanding of the normalization mechanism and how attention modulations depend on this mechanism.NEW & NOTEWORTHY Tuned normalization studies have demonstrated that the variance in attention modulation size seen across neurons from the same cortical area can be largely explained by between-neuron differences in normalization strength. Here we demonstrate that attention modulation size varies within neurons as well and that this variance is largely explained by within-neuron differences in normalization strength. We provide a new spatially tuned normalization model that explains this broad range of observed normalization and attention effects.
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Affiliation(s)
- Amy M Ni
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania.,Department of Neurobiology, Harvard Medical School, Boston, Massachusetts; and
| | - John H R Maunsell
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts; and .,Department of Neurobiology, The University of Chicago, Chicago, Illinois
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15
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Verhoef BE, Maunsell JHR. Attention-related changes in correlated neuronal activity arise from normalization mechanisms. Nat Neurosci 2017; 20:969-977. [PMID: 28553943 PMCID: PMC5507208 DOI: 10.1038/nn.4572] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 05/02/2017] [Indexed: 12/11/2022]
Abstract
Attention is believed to enhance perception by altering the correlations
between pairs of neurons. How attention changes neuronal correlations is
unknown. Using multi-electrodes in primate visual cortex, we measured
spike-count correlations when single or multiple stimuli were presented, and
stimuli were attended or unattended. When stimuli were unattended, adding a
suppressive, non-preferred, stimulus beside a preferred stimulus
increased spike-count correlations between pairs of
similarly-tuned neurons, but decreased
spike-count correlations between pairs of oppositely-tuned
neurons. These changes are explained by a stochastic normalization model
containing populations of oppositely-tuned, mutually-suppressive neurons.
Importantly, this model also explains why attention decreased
(attend preferred stimulus) or increased (attend non-preferred
stimulus) correlations: as an indirect consequence of attention-related changes
in the inputs to normalization mechanisms. Our findings link normalization
mechanisms to correlated neuronal activity and attention, showing that
normalization mechanisms shape response correlations and that these correlations
change when attention biases normalization mechanisms.
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Affiliation(s)
- Bram-Ernst Verhoef
- Department of Neurobiology, The University of Chicago, Chicago, Illinois, USA.,Laboratorium voor Neuro- en Psychofysiologie, KU Leuven, Leuven, Belgium
| | - John H R Maunsell
- Department of Neurobiology, The University of Chicago, Chicago, Illinois, USA
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16
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Abstract
Advances on several fronts have refined our understanding of the neuronal mechanisms of attention. This review focuses on recent progress in understanding visual attention through single-neuron recordings made in behaving subjects. Simultaneous recordings from populations of individual cells have shown that attention is associated with changes in the correlated firing of neurons that can enhance the quality of sensory representations. Other work has shown that sensory normalization mechanisms are important for explaining many aspects of how visual representations change with attention, and these mechanisms must be taken into account when evaluating attention-related neuronal modulations. Studies comparing different brain structures suggest that attention is composed of several cognitive processes, which might be controlled by different brain regions. Collectively, these and other recent findings provide a clearer picture of how representations in the visual system change when attention shifts from one target to another.
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Affiliation(s)
- John H R Maunsell
- Department of Neurobiology, University of Chicago, Chicago, Illinois 60637;
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17
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Luo TZ, Maunsell JHR. Neuronal Modulations in Visual Cortex Are Associated with Only One of Multiple Components of Attention. Neuron 2015; 86:1182-8. [PMID: 26050038 DOI: 10.1016/j.neuron.2015.05.007] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Revised: 03/24/2015] [Accepted: 04/20/2015] [Indexed: 11/30/2022]
Abstract
Neuronal signals related to visual attention are found in widespread brain regions, and these signals are generally assumed to participate in a common mechanism of attention. However, the behavioral effects of attention in detection can be separated into two distinct components: spatially selective shifts in either the criterion or sensitivity of the subject. Here we show that a paradigm used by many single-neuron studies of attention conflates behavioral changes in the subject's criterion and sensitivity. Then, using a task designed to dissociate these two components, we found that multiple aspects of attention-related neuronal modulations in area V4 of monkey visual cortex corresponded to behavioral shifts in sensitivity, but not criterion. This result suggests that separate components of attention are associated with signals in different brain regions and that attention is not a unitary process in the brain, but instead consists of distinct neurobiological mechanisms.
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Affiliation(s)
- Thomas Zhihao Luo
- Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA; Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA.
| | - John H R Maunsell
- Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA
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18
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Abstract
Gamma rhythm (which has a center frequency between 30 and 80 Hz) is modulated by cognitive mechanisms such as attention and memory, and has been hypothesized to play a role in mediating these processes by supporting communication channels between cortical areas or encoding information in its phase. We highlight several issues related to gamma rhythms, such as low and inconsistent power, its dependence on low-level stimulus features, problems due to conduction delays, and contamination due to spike-related activity that makes accurate estimation of gamma phase difficult. Gamma rhythm could be a potentially useful signature of excitation-inhibition interactions in the brain, but whether it also provides a mechanism for information processing or coding remains an open question.
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Affiliation(s)
- Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bangalore 560012, India
| | - John H R Maunsell
- Department of Neurobiology, University of Chicago, 5812 South Ellis Avenue, MC0912 Chicago, IL 60637, USA.
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19
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Abstract
Manipulating a divisive normalization mechanism independently of attention in monkeys suggests that gamma power reflects excitation-inhibition interactions rather than plays a functional role in attentional processing. Neuronal assemblies often exhibit stimulus-induced rhythmic activity in the gamma range (30–80 Hz), whose magnitude depends on the attentional load. This has led to the suggestion that gamma rhythms form dynamic communication channels across cortical areas processing the features of behaviorally relevant stimuli. Recently, attention has been linked to a normalization mechanism, in which the response of a neuron is suppressed (normalized) by the overall activity of a large pool of neighboring neurons. In this model, attention increases the excitatory drive received by the neuron, which in turn also increases the strength of normalization, thereby changing the balance of excitation and inhibition. Recent studies have shown that gamma power also depends on such excitatory–inhibitory interactions. Could modulation in gamma power during an attention task be a reflection of the changes in the underlying excitation–inhibition interactions? By manipulating the normalization strength independent of attentional load in macaque monkeys, we show that gamma power increases with increasing normalization, even when the attentional load is fixed. Further, manipulations of attention that increase normalization increase gamma power, even when they decrease the firing rate. Thus, gamma rhythms could be a reflection of changes in the relative strengths of excitation and normalization rather than playing a functional role in communication or control. Brain signals often show a stimulus-induced rhythm in the “gamma” band (30–80 Hz) whose magnitude depends on attentional load, leading to suggestions that gamma rhythm plays a functional role in routing signals across cortical areas. However, gamma power also depends on simple stimulus features such as size or contrast, which suggests that gamma could arise from basic cortical processes involving excitation–inhibition interactions. One such process is divisive normalization, a mechanism that suppresses the response of a neuron by the overall activity of a large pool of neighboring neurons. Recent studies have shown that attention increases the strength of both excitation and normalization. We hypothesized that the increase in gamma power in an attention task is due to the effect of attention on excitation and normalization. By manipulating the normalization strength independent of attentional load in macaque monkeys, we show that gamma power increases with increasing normalization, even when attentional load is held fixed. Thus, gamma rhythms could be a reflection of changes in the relative strengths of excitation and normalization rather than playing a functional role in communication or control.
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Affiliation(s)
- Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India.
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20
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Kang I, Maunsell JHR. Potential confounds in estimating trial-to-trial correlations between neuronal response and behavior using choice probabilities. J Neurophysiol 2012; 108:3403-15. [PMID: 22993262 DOI: 10.1152/jn.00471.2012] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Correlations between trial-to-trial fluctuations in the responses of individual sensory neurons and perceptual reports, commonly quantified with choice probability (CP), have been widely used as an important tool for assessing the contributions of neurons to behavior. These correlations are usually weak and often require a large number of trials for a reliable estimate. Therefore, working with measures such as CP warrants care in data analysis as well as rigorous controls during data collection. Here we identify potential confounds that can arise in data analysis and lead to biased estimates of CP, and suggest methods to avoid the bias. In particular, we show that the common practice of combining neuronal responses across different stimulus conditions with z-score normalization can result in an underestimation of CP when the ratio of the numbers of trials for the two behavioral response categories differs across the stimulus conditions. We also discuss the effects of using variable time intervals for quantifying neuronal response on CP measurements. Finally, we demonstrate that serious artifacts can arise in reaction time tasks that use varying measurement intervals if the mean neuronal response and mean behavioral performance vary over time within trials. To emphasize the importance of addressing these concerns in neurophysiological data, we present a set of data collected from V1 cells in macaque monkeys while the animals performed a detection task.
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Affiliation(s)
- Incheol Kang
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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21
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Abstract
The effect of attention on firing rates varies considerably within a single cortical area. The firing rate of some neurons is greatly modulated by attention while others are hardly affected. The reason for this variability across neurons is unknown. We found that the variability in attention modulation across neurons in area MT of macaques can be well explained by variability in the strength of tuned normalization across neurons. The presence of tuned normalization also explains a striking asymmetry in attention effects within neurons: when two stimuli are in a neuron's receptive field, directing attention to the preferred stimulus modulates firing rates more than directing attention to the nonpreferred stimulus. These findings show that much of the neuron-to-neuron variability in modulation of responses by attention depends on variability in the way the neurons process multiple stimuli, rather than differences in the influence of top-down signals related to attention.
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Affiliation(s)
- Amy M Ni
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
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22
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Histed MH, Ni AM, Maunsell JHR. Insights into cortical mechanisms of behavior from microstimulation experiments. Prog Neurobiol 2012; 103:115-30. [PMID: 22307059 DOI: 10.1016/j.pneurobio.2012.01.006] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2011] [Revised: 01/06/2012] [Accepted: 01/19/2012] [Indexed: 11/15/2022]
Abstract
Even the simplest behaviors depend on a large number of neurons that are distributed across many brain regions. Because electrical microstimulation can change the activity of localized subsets of neurons, it has provided valuable evidence that specific neurons contribute to particular behaviors. Here we review what has been learned about cortical function from behavioral studies using microstimulation in animals and humans. Experiments that examine how microstimulation affects the perception of stimuli have shown that the effects of microstimulation are usually highly specific and can be related to the stimuli preferred by neurons at the stimulated site. Experiments that ask subjects to detect cortical microstimulation in the absence of other stimuli have provided further insights. Although subjects typically can detect microstimulation of primary sensory or motor cortex, they are generally unable to detect stimulation of most of cortex without extensive practice. With practice, however, stimulation of any part of cortex can become detected. These training effects suggest that some patterns of cortical activity cannot be readily accessed to guide behavior, but that the adult brain retains enough plasticity to learn to process novel patterns of neuronal activity arising anywhere in cortex.
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Affiliation(s)
- Mark H Histed
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
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23
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Abstract
Scientific research produces new knowledge, technologies, and clinical treatments that can lead to enormous returns. Often, the path from basic research to new paradigms and direct impact on society takes time. Precise quantification of scientific output in the short-term is not an easy task but is critical for evaluating scientists, laboratories, departments, and institutions. While there have been attempts to quantifying scientific output, we argue that current methods are not ideal and suffer from solvable difficulties. Here we propose criteria that a metric should have to be considered a good index of scientific output. Specifically, we argue that such an index should be quantitative, based on robust data, rapidly updated and retrospective, presented with confidence intervals, normalized by number of contributors, career stage and discipline, impractical to manipulate, and focused on quality over quantity. Such an index should be validated through empirical testing. The purpose of quantitatively evaluating scientific output is not to replace careful, rigorous review by experts but rather to complement those efforts. Because it has the potential to greatly influence the efficiency of scientific research, we have a duty to reflect upon and implement novel and rigorous ways of evaluating scientific output. The criteria proposed here provide initial steps toward the systematic development and validation of a metric to evaluate scientific output.
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Affiliation(s)
- Gabriel Kreiman
- Department of Ophthalmology, Children's Hospital, Harvard Medical School Boston, MA, USA
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24
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Abstract
To understand how activity in mammalian neural circuits controls behavior, the mouse is a promising model system due to the convergence of genetic, optical, and physiological methods. The ability to control and quantify behavior precisely is also essential for these studies. We developed an operant visual detection paradigm to make visual psychophysical measurements: head-fixed mice make responses by pressing a lever. We designed this task to permit neurophysiological studies of behavior in cerebral cortex, where activity is variable from trial to trial and neurons encode many types of information simultaneously. To study neural responses in the face of this complexity, we trained mice to do a task where they perform hundreds of trials daily and perceptual thresholds can be measured. We used this task to measure both visual acuity and the minimum detectable contrast in behaving mice. We found that the mouse contrast response function is similar in shape to other species. They can detect low-contrast stimuli, with a peak contrast threshold of 2%, equivalent to ∼15° eccentric in human vision. Mouse acuity is modest, with an upper limit near 0.5 cycles/°, consistent with prior data.
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Affiliation(s)
- Mark H Histed
- Dept. of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
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25
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Abstract
Visual attention affects both perception and neuronal responses. Whether the same neuronal mechanisms mediate spatial attention, which improves perception of attended locations, and nonspatial forms of attention has been a subject of considerable debate. Spatial and feature attention have similar effects on individual neurons. Because visual cortex is retinotopically organized, however, spatial attention can comodulate local neuronal populations, whereas feature attention generally requires more selective modulation. We compared the effects of feature and spatial attention on local and spatially separated populations by recording simultaneously from dozens of neurons in both hemispheres of V4. Feature and spatial attention affect the activity of local populations similarly, modulating both firing rates and correlations between pairs of nearby neurons. However, whereas spatial attention appears to act on local populations, feature attention is coordinated across hemispheres. Our results are consistent with a unified attentional mechanism that can modulate the responses of arbitrary subgroups of neurons.
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Affiliation(s)
- Marlene R Cohen
- Harvard Medical School Department of Neurobiology and Howard Hughes Medical Institute, Boston, MA 02115, USA.
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26
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Abstract
During cognitive tasks electrical activity in the brain shows changes in power in specific frequency ranges, such as the alpha (8-12 Hz) or gamma (30-80 Hz) bands, as well as in a broad range above ∼80 Hz, called the high-gamma band. The role or significance of this broadband high-gamma activity is unclear. One hypothesis states that high-gamma oscillations serve just like gamma oscillations, operating at a higher frequency and consequently at a faster timescale. Another hypothesis states that high-gamma power is related to spiking activity. Because gamma power and spiking activity tend to co-vary during most stimulus manipulations (such as contrast modulations) or cognitive tasks (such as attentional modulation), it is difficult to dissociate these two hypotheses. We studied the relationship between high-gamma power, gamma rhythm, and spiking activity in the primary visual cortex (V1) of awake monkeys while varying the stimulus size, which increased the gamma power but decreased the firing rate, permitting a dissociation. We found that gamma power became anti-correlated with the high-gamma power, suggesting that the two phenomena are distinct and have different origins. On the other hand, high-gamma power remained tightly correlated with spiking activity under a wide range of stimulus manipulations. We studied this relationship using a signal processing technique called Matching Pursuit and found that action potentials are associated with sharp transients in the LFP with broadband power, which is visible at frequencies as low as ∼50 Hz. These results distinguish broadband high-gamma activity from gamma rhythms as an easily obtained and reliable electrophysiological index of neuronal firing near the microelectrode. Further, they highlight the importance of making a careful dissociation between gamma rhythms and spike-related transients that could be incorrectly decomposed as rhythms using traditional signal processing methods.
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Affiliation(s)
- Supratim Ray
- Department of Neurobiology & Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, United States of America.
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27
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Abstract
It remains unclear how attention affects the tuning of individual neurons in visual cerebral cortex. Some observations suggest that attention preferentially enhances responses to low contrast stimuli, whereas others suggest that attention proportionally affects responses to all stimuli. Resolving how attention affects responses to different stimuli is essential for understanding the mechanism by which it acts. To explore the effects of attention on stimuli of different contrasts, we recorded from individual neurons in the middle temporal visual area (MT) of rhesus monkeys while shifting their attention between preferred and nonpreferred stimuli within their receptive fields. This configuration results in robust attentional modulation that makes it possible to readily distinguish whether attention acts preferentially on low contrast stimuli. We found no evidence for greater enhancement of low contrast stimuli. Instead, the strong attentional modulations were well explained by a model in which attention proportionally enhances responses to stimuli of all contrasts. These data, together with observations on the effects of attention on responses to other stimulus dimensions, suggest that the primary effect of attention in visual cortex may be to simply increase the strength of responses to all stimuli by the same proportion.
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Affiliation(s)
- Joonyeol Lee
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
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28
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Ni AM, Maunsell JHR. Microstimulation reveals limits in detecting different signals from a local cortical region. Curr Biol 2010; 20:824-8. [PMID: 20381351 DOI: 10.1016/j.cub.2010.02.065] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2009] [Revised: 02/04/2010] [Accepted: 02/23/2010] [Indexed: 12/01/2022]
Abstract
Behavioral performance depends on the activity of neurons in sensory cortex, but little is known about the brain's capacity to access specific neuronal signals to guide behavior. Even the individual sensory neurons that are most sensitive to a relevant stimulus are only weakly correlated with behavior, suggesting that behavioral decisions are based on the combined activity of groups of neurons with sensitivities well matched to task demands. To explore how flexibly different patterns of activity can be accessed from a given cortical region, we trained animals to detect electrical microstimulation of local V1 sites. By allowing the animals to become expert at the detection of microstimulation of specific V1 sites that corresponded to particular retinotopic locations, we could measure the effects of that training on the ability of those sites to support the detection of visual stimuli. Training to detect electrical activation caused a large, reversible, retinotopically localized impairment of thresholds for detecting visual stimuli. Retraining on visual detection restored normal thresholds and in turn impaired thresholds for detecting microstimulation. These results suggest that there are substantial limits to the types of signals for which a local cortical region can be simultaneously optimized.
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Affiliation(s)
- Amy M Ni
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
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29
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Cohen MR, Maunsell JHR. Attention improves performance primarily by reducing interneuronal correlations. Nat Neurosci 2009; 12:1594-600. [PMID: 19915566 PMCID: PMC2820564 DOI: 10.1038/nn.2439] [Citation(s) in RCA: 716] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2009] [Accepted: 09/30/2009] [Indexed: 11/16/2022]
Abstract
Visual attention can dramatically improve behavioural performance by allowing observers to focus on the important information in a complex scene. Attention also typically increases the firing rates of cortical sensory neurons. Rate increases improve the signal-to-noise ratio of individual neurons, and this improvement has been assumed to underlie attention-related improvements in behaviour. We recorded dozens of neurons simultaneously in visual area V4 and found that changes in single neurons accounted for only a small fraction of the improvement in the sensitivity of the population. Instead, over 80% of the attentional improvement in the population signal was caused by decreases in the correlations between the trial-to-trial fluctuations in the responses of pairs of neurons. These results suggest that the representation of sensory information in populations of neurons and the way attention affects the sensitivity of the population may only be understood by considering the interactions between neurons.
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Affiliation(s)
- Marlene R Cohen
- Howard Hughes Medical Institute and Harvard Medical School Department of Neurobiology, Boston, Massachusetts, USA.
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30
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Saper CB, Maunsell JHR. The Neuroscience Peer Review Consortium. Brain Struct Funct 2009; 213:359-61. [DOI: 10.1007/s00429-009-0206-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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31
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Dulay MF, Murphey DK, Sun P, David YB, Maunsell JHR, Beauchamp MS, Yoshor D. Computer-controlled electrical stimulation for quantitative mapping of human cortical function. J Neurosurg 2009; 110:1300-3. [PMID: 19061348 DOI: 10.3171/2008.2.jns17666] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Cortical mapping with electrical stimulation (ES) in neurosurgical patients typically involves the manually controlled delivery of suprathreshold electrical current to a discrete area of the brain. Limited numbers of trials and imprecise current delivery methods increase the variability of the behavioral response and make it difficult to collect quantitative mapping data, which is especially important in research studies of human cortical function. To overcome these limitations, the authors developed a method for computer-controlled delivery of defined electrical current to implanted intracranial electrodes. They demonstrate that stimulation can be time locked to a behavioral task to rapidly and systematically measure the detection threshold for ES in human visual cortex over many trials. Computer-controlled ES is well suited for the systematic and quantitative study of the function of virtually any region of cerebral cortex. It may be especially useful for studying human cortical regions that are not well characterized and for verifying the presence of stimulation-evoked percepts that are difficult to objectively confirm.
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Affiliation(s)
- Mario F Dulay
- Departments of Neurosurgery, Texas Children's Hospital, Houston Texas, USA
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32
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Murphey DK, Maunsell JHR, Yoshor D, Beauchamp MS. Perceiving Electrical Stimulation of Identified Human Visual Areas. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)70632-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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33
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Saper CB, Maunsell JHR. The Neuroscience Peer Review Consortium. Neural Dev 2009; 4:10. [PMID: 19284614 PMCID: PMC2660324 DOI: 10.1186/1749-8104-4-10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2009] [Accepted: 03/12/2009] [Indexed: 11/26/2022] Open
Abstract
As the Neuroscience Peer Review Consortium (NPRC) ends its first year, it is worth looking back to see how the experiment has worked. In order to encourage dissemination of the details outlined in this Editorial, it will also be published in other journals in the Neuroscience Peer Review Consortium.
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34
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Abstract
Although many studies have shown that attention to a stimulus can enhance the responses of individual cortical sensory neurons, little is known about how attention accomplishes this change in response. Here, we propose that attention-based changes in neuronal responses depend on the same response normalization mechanism that adjusts sensory responses whenever multiple stimuli are present. We have implemented a model of attention that assumes that attention works only through this normalization mechanism, and show that it can replicate key effects of attention. The model successfully explains how attention changes the gain of responses to individual stimuli and also why modulation by attention is more robust and not a simple gain change when multiple stimuli are present inside a neuron's receptive field. Additionally, the model accounts well for physiological data that measure separately attentional modulation and sensory normalization of the responses of individual neurons in area MT in visual cortex. The proposal that attention works through a normalization mechanism sheds new light a broad range of observations on how attention alters the representation of sensory information in cerebral cortex.
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Affiliation(s)
- Joonyeol Lee
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America
| | - John H. R. Maunsell
- Howard Hughes Medical Institute and Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, United States of America
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35
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Saper CB, Maunsell JHR. The Neuroscience Peer Review Consortium. Eur J Neurosci 2009; 29:435-6. [PMID: 19222555 PMCID: PMC2704555 DOI: 10.1111/j.1460-9568.2009.06643.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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36
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Saper CB, Maunsell JHR. Neuroendocrinologies membership of the Neuroscience Peer Review Consortium. Neuroendocrinology 2009; 89:1-2. [PMID: 19176976 DOI: 10.1159/000190668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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37
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Yoshor D, Bosking WH, Lega BC, Sun P, Maunsell JHR. Local cortical function after uncomplicated subdural electrode implantation. Laboratory investigation. J Neurosurg 2008; 108:139-44. [PMID: 18173323 DOI: 10.3171/jns/2008/108/01/0139] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVES Although subdural electrodes are routinely used to map regional brain function, it is unknown if the presence of these implants hinders local cortical function. The authors used psychophysical methods to measure the effect of uncomplicated electrode implantation on local cortical function. METHODS Local field potentials were used to map receptive fields (RFs) for subdural electrodes that were unilaterally implanted on early visual cortex in 4 patients. After electrode implantation, patients did a task that required them to detect an orientation change in a flashing visual stimulus that was presented either inside the mapped RF or outside the RF in the diametrically opposite portion of the other hemifield. The size of the orientation change was varied to span a wide range of behavioral performance. Psychometric curves were generated by fitting behavioral responses to a logistic function. The threshold was defined as the point at which the fitted function crossed 50% detection. RESULTS Data were well fit by the logistic function in all 4 patients for both RF and non-RF conditions. None of the volunteers tested showed a statistically significant difference in detection threshold, reaction time, or in the slope of the psychometric function for stimuli presented inside or outside the RF. CONCLUSIONS Subdural electrodes implanted for extraoperative monitoring do not impair psychophysical performance for a task based on stimuli lying within the RF for recording electrodes. This finding suggests that these electrodes can be used reliably for accurate assessment of regional neurological function.
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Affiliation(s)
- Daniel Yoshor
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas 77030, USA.
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38
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Abstract
The effects of attention on neuronal responses in visual cortex have been likened to a change in stimulus contrast. Attention and stimulus contrast both modulate the magnitude of neuronal responses. However, changes in stimulus contrast also affect the latency of visual responses. Although many neurophysiological studies have examined how attention affects the strength of neuronal responses, few have considered whether attention affects neuronal latencies. To compare directly the effects of stimulus contrast and attention, we recorded responses from individual neurons in area V4 of macaque monkeys while they performed a task that independently controlled spatial attention and stimulus contrast. As expected, changes in stimulus contrast affected both the magnitude and latency of neuronal responses. Although attention had the expected effects on the magnitudes of neuronal responses, we did not detect statistically reliable changes in neuronal latency. A direct comparison of the effects of contrast and attention revealed a reliable difference. When a shift in spatial attention decreased response magnitude, response latency increased much less than when the same magnitude change was caused by reducing stimulus contrast. Thus, attention is distinct from contrast in the way it affects the relationship between neuronal response magnitude and latency.
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Affiliation(s)
| | | | - John H. R. Maunsell
- Department of Neuroscience and
- Howard Hughes Medical Institute, Baylor College of Medicine, Houston, Texas 77030
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39
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Murphey DK, Maunsell JHR. Behavioral detection of electrical microstimulation in different cortical visual areas. Curr Biol 2007; 17:862-7. [PMID: 17462895 PMCID: PMC2034326 DOI: 10.1016/j.cub.2007.03.066] [Citation(s) in RCA: 81] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2007] [Revised: 03/03/2007] [Accepted: 03/29/2007] [Indexed: 11/18/2022]
Abstract
The extent to which areas in the visual cerebral cortex differ in their ability to support perceptions has been the subject of considerable speculation. Experiments examining the activity of individual neurons have suggested that activity in later stages of the visual cortex is more closely linked to perception than that in earlier stages [1-9]. In contrast, results from functional imaging, transcranial magnetic stimulation, and lesion studies have been interpreted as showing that earlier stages are more closely coupled to perception [10-15]. We examined whether neuronal activity in early and later stages differs in its ability to support detectable signals by measuring behavioral thresholds for detecting electrical microstimulation in different cortical areas in two monkeys. By training the animals to perform a two-alternative temporal forced-choice task, we obtained criterion-free thresholds from five visual areas--V1, V2, V3A, MT, and the inferotemporal cortex. Every site tested yielded a reliable threshold. Thresholds varied little within and between visual areas, rising gradually from early to later stages. We similarly found no systematic differences in the slopes of the psychometric detection functions from different areas. These results suggest that neuronal signals of similar magnitude evoked in any part of visual cortex can generate percepts.
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Affiliation(s)
- Dona K Murphey
- Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA
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40
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Abstract
Most of our understanding of the functional organization of human visual cortex comes from lesion and functional imaging studies and by extrapolation from results obtained by neuroanatomical and neurophysiological studies in nonhuman primates. Although some single-unit and field potential recordings have been made in human visual cortex, none has provided quantitative characterization of spatial receptive fields (RFs) of individual sites. Here we use subdural electrodes implanted for clinical purposes to quantitatively measure response properties in different regions of human visual cortex. We find significant differences in RF size, response latency, and response magnitude for sites in early visual areas, versus sites in later stages of both the dorsal and ventral streams. In addition, we use this technique to estimate the cortical magnification factor in early human visual cortex. The spatial and temporal resolution of cortical surface recordings suggest that this technique is well suited to examine further issues in visual processing in humans.
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Affiliation(s)
- Daniel Yoshor
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
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41
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Abstract
Spatial attention improves performance at attended locations and correspondingly modulates firing rates of cortical neurons. The size of these behavioral and neuronal effects depends on the difficulty of the task performed at the attended location. Psychological theorists have attributed this to a tighter focus of a fixed amount of processing resource at the attended location, but the effects of task difficulty on the distribution of neuronal effects of attention across the visual field have not been fully explored. We trained rhesus monkeys to do a detection task in which difficulty and spatial attention were manipulated independently. Probe stimuli were used to measure behavioral performance in different conditions of attention and difficulty. Animals performed better at attended locations and this advantage increased with difficulty, consistent with data from human psychophysics. Neuronal modulation by spatial attention was larger with greater difficulty. In two animals, increasing difficulty caused a modest increase in neuronal responses to visual stimuli regardless of the locus of spatial attention. In a third animal, which was previously trained to ignore multiple distracting stimuli, increasing task difficulty increased responses at the focus of attention and suppressed responses away from the focus of attention. The results show that difficulty can modulate effects of spatial attention in V4; it can alter the distribution of sensory responses across the visual scene in ways that may depend on the subject's behavioral strategy.
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Affiliation(s)
- C Elizabeth Boudreau
- Howard Hughes Medical Institute, Baylor College of Medicine, Houston, Texas, USA
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42
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Abstract
Previous single-unit studies of visual cortex have reported that spatial attention modulates responses to different orientations and directions proportionally, such that it does not change the width of tuning functions for these properties. Other studies have suggested that spatial attention causes a leftward shift in contrast response functions, such that its effects on responses to stimuli of different contrasts are not proportional. We have further explored the effects of attention on stimulus-response functions by measuring the responses of 131 individual V4 neurons in two monkeys while they did a task that controlled their spatial attention. Each neuron was tested with a set of stimuli that spanned complete ranges of orientation and contrast during different states of attention. Consistent with earlier reports, attention scaled responses to preferred and nonpreferred orientations proportionally. However, we did not find compelling evidence that the effects were best described by a leftward shift of the contrast response function. The modulation of neuronal responses by attention was well described by either a leftward shift or proportional scaling of the contrast response function. Consideration of differences in experimental design and analysis that may have contributed to this discrepancy suggests that it was premature to exclude a proportional scaling of responses to different contrasts by attention in favor of a leftward shift of contrast response functions. The current results reopen the possibility that the effects of attention on stimulus-response functions are well described by a single proportional increase in a neuron's response to all stimuli.
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Affiliation(s)
- Tori Williford
- Department of Neuroscience, Howard Hughes Medical Institute and Baylor College, Houston, Texas, USA
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43
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Abstract
Although most studies of visual attention have examined the effects of shifting attention between different locations in the visual field, attention can also be directed to particular visual features, such as a color, orientation or a direction of motion. Single-unit studies have shown that attention to a feature modulates neuronal signals in a range of areas in monkey visual cortex. The location-independent property of feature-based attention makes it particularly well suited to modify selectively the neural representations of stimuli or parts within complex visual scenes that match the currently attended feature. This review is part of the TINS special issue on The Neural Substrates of Cognition.
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Affiliation(s)
- John H R Maunsell
- HHMI and Baylor College of Medicine, Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza S-603, Houston, TX 77030, USA.
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44
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45
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Abstract
We describe a new technique that uses the timing of neuronal and behavioral responses to explore the contributions of individual neurons to specific behaviors. The approach uses both the mean neuronal latency and the trial-by-trial covariance between neuronal latency and behavioral response. Reliable measurements of these values were obtained from single-unit recordings made from anterior inferotemporal (AIT) cortex and the frontal eye fields (FEF) in monkeys while they performed a choice reaction time task. These neurophysiological data show that the responses of AIT neurons and some FEF neurons have little covariance with behavioral response, consistent with a largely “sensory” response. The responses of another group of FEF neurons with longer mean latency covary tightly with behavioral response, consistent with a largely “motor” response. A very small fraction of FEF neurons had responses consistent with an intermediate position in the sensory-motor pathway. These results suggest that this technique is a valuable tool for exploring the functional organization of neuronal circuits that underlie specific behaviors.
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Affiliation(s)
- James J DiCarlo
- Howard Hughes Medical Institute and Division of Neuroscience, Balyor College of Medicine, Houston, Texas, USA.
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46
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Abstract
We examined how spatially directed attention affected the integration of motion in neurons of the middle temporal (MT) area of visual cortex. We recorded from single MT neurons while monkeys performed a motion detection task under two attentional states. Using 0% coherent random dot motion, we estimated the optimal linear transfer function (or kernel) between the global motion and the neuronal response. This linear kernel filtered the random dot motion across direction, speed, and time. Slightly less than one-half of the neurons produced reasonably well defined kernels that also tended to account for both the directional selectivity and responses to coherent motion of different strengths. This subpopulation of cells had faster, more transient, and more robust responses to visual stimuli than neurons with kernels that did not contain well defined regions of integration. For those neurons that had large attentional modulation and produced well defined kernels, we found attention scaled the temporal profile of the transfer function with no appreciable shift in time or change in shape. Thus, for MT neurons described by a linear transfer function, attention produced a multiplicative scaling of the temporal integration window.
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Affiliation(s)
- Erik P Cook
- Howard Hughes Medical Institute and Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.
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47
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Yang T, Maunsell JHR. The effect of perceptual learning on neuronal responses in monkey visual area V4. J Neurosci 2004; 24:1617-26. [PMID: 14973244 PMCID: PMC6730469 DOI: 10.1523/jneurosci.4442-03.2004] [Citation(s) in RCA: 295] [Impact Index Per Article: 14.8] [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: 09/30/2003] [Revised: 12/15/2003] [Accepted: 12/18/2003] [Indexed: 11/21/2022] Open
Abstract
Previous studies have shown that perceptual learning can substantially alter the response properties of neurons in the primary somatosensory and auditory cortices. Although psychophysical studies suggest that perceptual learning induces similar changes in primary visual cortex (V1), studies that have measured the response properties of individual neurons have failed to find effects of the size described for the other sensory systems. We have examined the effect of learning on neuronal response properties in a visual area that lies at a later stage of cortical processing, area V4. Adult macaque monkeys were trained extensively on orientation discrimination at a specific retinal location using a narrow range of orientations. During the course of training, the subjects achieved substantial improvement in orientation discrimination that was primarily restricted to the trained location. After training, neurons in V4 with receptive fields overlapping the trained location had stronger responses and narrower orientation tuning curves than neurons with receptive fields in the opposite, untrained hemifield. The changes were most prominent for neurons that preferred orientations close to the trained range of orientations. These results provide the first demonstration of perceptual learning modifying basic neuronal response properties at an intermediate level of visual cortex and give insights into the distribution of plasticity across adult visual cortex.
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Affiliation(s)
- Tianming Yang
- Baylor College of Medicine and Howard Hughes Medical Institute, Houston, Texas 77030, USA.
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48
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DiCarlo JJ, Maunsell JHR. Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. J Neurophysiol 2003; 89:3264-78. [PMID: 12783959 DOI: 10.1152/jn.00358.2002] [Citation(s) in RCA: 143] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Visual object recognition is computationally difficult because changes in an object's position, distance, pose, or setting may cause it to produce a different retinal image on each encounter. To robustly recognize objects, the primate brain must have mechanisms to compensate for these variations. Although these mechanisms are poorly understood, it is thought that they elaborate neuronal representations in the inferotemporal cortex that are sensitive to object form but substantially invariant to other image variations. This study examines this hypothesis for image variation resulting from changes in object position. We studied the effect of small differences (+/-1.5 degrees ) in the retinal position of small (0.6 degrees wide) visual forms on both the behavior of monkeys trained to identify those forms and the responses of 146 anterior IT (AIT) neurons collected during that behavior. Behavioral accuracy and speed were largely unaffected by these small changes in position. Consistent with previous studies, many AIT responses were highly selective for the forms. However, AIT responses showed far greater sensitivity to retinal position than predicted from their reported receptive field (RF) sizes. The median AIT neuron showed a approximately 60% response decrease between positions within +/-1.5 degrees of the center of gaze, and 52% of neurons were unresponsive to one or more of these positions. Consistent with previous studies, each neuron's rank order of target preferences was largely unaffected across position changes. Although we have not yet determined the conditions necessary to observe this marked position sensitivity in AIT responses, we rule out effects of spatial-frequency content, eye movements, and failures to include the RF center. To reconcile this observation with previous studies, we hypothesize that either AIT position sensitivity strongly depends on object size or that position sensitivity is sharpened by extensive visual experience at fixed retinal positions or by the presence of flanking distractors.
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Affiliation(s)
- James J DiCarlo
- Howard Hughes Medical Institute and Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.
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49
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Abstract
Paying attention to a stimulus selectively increases the ability to process it. For example, when subjects attend to a specific region of a visual scene, their sensitivity to changes at that location increases. A large number of studies describe the behavioural consequences and neurophysiological correlates of attending to spatial locations. There has, in contrast, been little study of the allocation of attention over time. Because subjects can anticipate predictable events with great temporal precision, it seems probable that they might dynamically shift their attention when performing a familiar perceptual task whose constraints changed over time. We trained monkeys to respond to a stimulus change where the probability of occurrence changed over time. Recording from area V4 of the visual cortex in these animals, we found that the modulation of neuronal responses changed according to the probability of the change occurring at that instant. Thus, we show that the attentional modulation of sensory neurons reflects a subject's anticipation of the timing of behaviourally relevant events.
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Affiliation(s)
- Geoffrey M Ghose
- Division of Neuroscience and Howard Hughes Medical Institute, Baylor College of Medicine, Houston, Texas 77030, USA.
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50
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Cook EP, Maunsell JHR. Dynamics of neuronal responses in macaque MT and VIP during motion detection. Nat Neurosci 2002; 5:985-94. [PMID: 12244324 DOI: 10.1038/nn924] [Citation(s) in RCA: 208] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2001] [Accepted: 08/28/2002] [Indexed: 11/09/2022]
Abstract
We examined how the relationship between neuronal activity and behavior evolved over time during a motion-detection task. Recording from two regions of visual cortex that process motion, the middle temporal (MT) and ventral intraparietal (VIP) areas, we used the time it took subjects to detect a motion stimulus to evaluate the dynamics of the underlying neuronal signals. Single-neuron activity was correlated with stimulus detection and reaction time (RT) in both areas. The rising edge of the population response from both areas was highly predictive of RT using a simple threshold-detection model. The time course of the population responses, however, differed between MT and VIP. For MT, the onset of the neuronal response was relatively constant, whereas for VIP the onset of the neuronal responses increased with RT. In contrast to previous studies, we found that single neurons were not reliable detectors of the motion signal when constrained by realistic detection times.
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Affiliation(s)
- Erik P Cook
- Howard Hughes Medical Institute and Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.
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