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Goris RLT, Coen-Cagli R, Miller KD, Priebe NJ, Lengyel M. Response sub-additivity and variability quenching in visual cortex. Nat Rev Neurosci 2024; 25:237-252. [PMID: 38374462 DOI: 10.1038/s41583-024-00795-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2024] [Indexed: 02/21/2024]
Abstract
Sub-additivity and variability are ubiquitous response motifs in the primary visual cortex (V1). Response sub-additivity enables the construction of useful interpretations of the visual environment, whereas response variability indicates the factors that limit the precision with which the brain can do this. There is increasing evidence that experimental manipulations that elicit response sub-additivity often also quench response variability. Here, we provide an overview of these phenomena and suggest that they may have common origins. We discuss empirical findings and recent model-based insights into the functional operations, computational objectives and circuit mechanisms underlying V1 activity. These different modelling approaches all predict that response sub-additivity and variability quenching often co-occur. The phenomenology of these two response motifs, as well as many of the insights obtained about them in V1, generalize to other cortical areas. Thus, the connection between response sub-additivity and variability quenching may be a canonical motif across the cortex.
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Affiliation(s)
- Robbe L T Goris
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Dept. of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Swartz Program in Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Nicholas J Priebe
- Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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2
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Ziemba CM, Goris RLT, Stine GM, Perez RK, Simoncelli EP, Movshon JA. Neuronal and behavioral responses to naturalistic texture images in macaque monkeys. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581645. [PMID: 38464304 PMCID: PMC10925125 DOI: 10.1101/2024.02.22.581645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and thus unlikely to reflect a critical aspect of the formation of perceptual decisions. Significance statement As visual signals propagate along the cortical hierarchy, they encode increasingly complex aspects of the sensory environment and likely have a more direct relationship with perceptual experience. We replicate and extend previous results from anesthetized monkeys differentiating the selectivity of neurons along the first step in cortical vision from area V1 to V2. However, our results further complicate efforts to establish neural signatures that reveal the relationship between perception and the neuronal activity of sensory populations. We find that choice-correlated activity in V1 and V2 is unstable across different observers and tasks, and also untethered from neuronal sensitivity and other features of nonsensory response modulation.
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3
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Chandrasekaran AN, Vermani A, Gupta P, Steinmetz N, Moore T, Sridharan D. Dissociable components of attention exhibit distinct neuronal signatures in primate visual cortex. SCIENCE ADVANCES 2024; 10:eadi0645. [PMID: 38306428 PMCID: PMC10836731 DOI: 10.1126/sciadv.adi0645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Attention can be deployed in multiple forms and facilitates behavior by influencing perceptual sensitivity and choice bias. Attention is also associated with a myriad of changes in sensory neural activity. Yet, the relationship between the behavioral components of attention and the accompanying changes in neural activity remains largely unresolved. We examined this relationship by quantifying sensitivity and bias in monkeys performing a task that dissociated eye movement responses from the focus of covert attention. Unexpectedly, bias, not sensitivity, increased at the focus of covert attention, whereas sensitivity increased at the location of planned eye movements. Furthermore, neuronal activity within visual area V4 varied robustly with bias, but not sensitivity, at the focus of covert attention. In contrast, correlated variability between neuronal pairs was lowest at the location of planned eye movements, and varied with sensitivity, but not bias. Thus, dissociable behavioral components of attention exhibit distinct neuronal signatures within the visual cortex.
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Affiliation(s)
| | - Ayesha Vermani
- Centre for Neuroscience, Indian Institute of Science, Bangalore, KA, India
| | - Priyanka Gupta
- Centre for Neuroscience, Indian Institute of Science, Bangalore, KA, India
| | - Nicholas Steinmetz
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Tirin Moore
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Devarajan Sridharan
- Centre for Neuroscience, Indian Institute of Science, Bangalore, KA, India
- Computer Science and Automation, Indian Institute of Science, Bangalore, KA, India
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4
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Liu Z, Yan Y, Wang DH. Category representation in primary visual cortex after visual perceptual learning. Cogn Neurodyn 2024; 18:23-35. [PMID: 38406201 PMCID: PMC10881456 DOI: 10.1007/s11571-022-09926-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/15/2022] [Accepted: 12/19/2022] [Indexed: 01/31/2023] Open
Abstract
The visual perceptual learning (VPL) leads to long-term enhancement of visual task performance. The subjects are often trained to link different visual stimuli to several options, such as the widely used two-alternative forced choice (2AFC) task, which involves an implicit categorical decision. The enhancement of performance has been related to the specific changes of neural activities, but few studies investigate the effects of categorical responding on the changes of neural activities. Here we investigated whether the neural activities would exhibit the categorical characteristics if the subjects are requested to respond visual stimuli in a categorical manner during VPL. We analyzed the neural activities of two monkeys in a contour detection VPL. We found that the neural activities in primary visual cortex (V1) converge to one pattern if the contour can be detected by monkey and another pattern if the contour cannot be detected, exhibiting a kind of category learning that the neural representations of detectable contour become less selective for number of bars forming contour and diverge from the representations of undetectable contour. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09926-8.
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Affiliation(s)
- Zhaofan Liu
- School of Systems Science, Beijing Normal University, Beijing, 100875 China
| | - Yin Yan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Xinjiekouwaidajie 19, Haidian, Beijing, 100875 China
- Chinese Institute for Brain Research, Beijing, China
| | - Da-Hui Wang
- School of Systems Science, Beijing Normal University, Beijing, 100875 China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Xinjiekouwaidajie 19, Haidian, Beijing, 100875 China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875 China
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5
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Lestang JH, Cai H, Averbeck BB, Cohen YE. Functional network properties of the auditory cortex. Hear Res 2023; 433:108768. [PMID: 37075536 PMCID: PMC10205700 DOI: 10.1016/j.heares.2023.108768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/27/2023] [Accepted: 04/11/2023] [Indexed: 04/21/2023]
Abstract
The auditory system transforms auditory stimuli from the external environment into perceptual auditory objects. Recent studies have focused on the contribution of the auditory cortex to this transformation. Other studies have yielded important insights into the contributions of neural activity in the auditory cortex to cognition and decision-making. However, despite this important work, the relationship between auditory-cortex activity and behavior/perception has not been fully elucidated. Two of the more important gaps in our understanding are (1) the specific and differential contributions of different fields of the auditory cortex to auditory perception and behavior and (2) the way networks of auditory neurons impact and facilitate auditory information processing. Here, we focus on recent work from non-human-primate models of hearing and review work related to these gaps and put forth challenges to further our understanding of how single-unit activity and network activity in different cortical fields contribution to behavior and perception.
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Affiliation(s)
- Jean-Hugues Lestang
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Huaizhen Cai
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bruno B Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Yale E Cohen
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA; Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA; Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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6
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Orlandi JG, Abdolrahmani M, Aoki R, Lyamzin DR, Benucci A. Distributed context-dependent choice information in mouse posterior cortex. Nat Commun 2023; 14:192. [PMID: 36635318 PMCID: PMC9837177 DOI: 10.1038/s41467-023-35824-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex-traditionally implicated in decision computations-the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals' attention state. A recurrent neural network trained with animals' choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal-environment interactions.
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Affiliation(s)
- Javier G Orlandi
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.,Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, T2N 1N4, Canada
| | | | - Ryo Aoki
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Dmitry R Lyamzin
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Andrea Benucci
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan. .,University of Tokyo, Graduate School of Information Science and Technology, Department of Mathematical Informatics, 1-1-1 Yayoi, Bunkyo City, Tokyo, 113-0032, Japan.
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7
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Niemeyer JE, Akers-Campbell S, Gregoire A, Paradiso MA. Perceptual enhancement and suppression correlate with V1 neural activity during active sensing. Curr Biol 2022; 32:2654-2667.e4. [PMID: 35584697 PMCID: PMC9233080 DOI: 10.1016/j.cub.2022.04.067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 03/03/2022] [Accepted: 04/22/2022] [Indexed: 11/26/2022]
Abstract
Perception in multiple sensory modalities is an active process that involves exploratory behaviors. In humans and other primates, vision results from sensory sampling guided by saccadic eye movements. Saccades are known to modulate visual perception, and a corollary discharge signal associated with saccades appears to establish a sense of visual stability. Neural recordings have shown that saccades also modulate activity widely across the brain. To investigate the neural basis of saccadic effects on perception, simultaneous recordings from multiple neurons in area V1 were made as animals performed a contrast detection task. Perceptual and neural measures were compared when the animal made real saccades that brought a stimulus into V1 receptive fields and when simulated saccades were made (identical retinal stimulation but no eye movement). When real saccades were made and low spatial frequency stimuli were presented, we observed a reduction in both perceptual sensitivity and neural activity compared with simulated saccades; conversely, with higher spatial frequency stimuli, saccades increased visual sensitivity and neural activity. The performance of neural decoders, which used the activity of the population of simultaneously recorded neurons, showed saccade effects on sensitivity that mirrored the frequency-dependent perceptual changes, suggesting that the V1 population activity could support the perceptual effects. A minority of V1 neurons had significant choice probabilities, and the saccades decreased both average choice probability and pairwise noise correlations. Taken together, the findings suggest that a signal related to saccadic eye movements alters V1 spiking to increase the independence of spiking neurons and bias the system toward processing higher spatial frequencies, presumably to enhance object recognition. The effects of saccades on visual perception and noise correlations appear to parallel effects observed in other sensory modalities, suggesting a general principle of active sensory processing.
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Affiliation(s)
- James E Niemeyer
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY, USA; Department of Neuroscience, Brown University, Providence, RI, USA
| | | | - Aaron Gregoire
- School of Engineering, Brown University, Providence, RI, USA
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8
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Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nat Commun 2022; 13:1099. [PMID: 35232956 PMCID: PMC8888615 DOI: 10.1038/s41467-022-28552-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/19/2022] [Indexed: 12/19/2022] Open
Abstract
Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", which allows feedback signals to not directly affect activity that is fed forward.
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9
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Jing R, Yang C, Huang X, Li W. Perceptual learning as a result of concerted changes in prefrontal and visual cortex. Curr Biol 2021; 31:4521-4533.e3. [PMID: 34450086 DOI: 10.1016/j.cub.2021.08.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 07/12/2021] [Accepted: 08/02/2021] [Indexed: 01/05/2023]
Abstract
Our perceptual ability remarkably improves with training. Some studies on visual perceptual learning have shown refined neural representation of the trained stimulus in the visual cortex, whereas others have exclusively argued for improved readout and decision-making processes in the frontoparietal cortex. The mixed results have rendered the underlying neural mechanisms puzzling and hotly debated. By simultaneously recording from monkey visual area V4 and ventrolateral prefrontal cortex (PFC) implanted with microelectrode arrays, we dissected learning-induced cortical changes over the course of training the monkeys in a global form detection task. Decoding analysis dissociated two distinct components of neuronal population codes that were progressively and markedly enhanced in both V4 and PFC. One component was closely related to the target stimulus feature and was subject to task-dependent top-down modulation; it emerged earlier in V4 than PFC and its enhancement was specific to the trained configuration of the target stimulus. The other component of the neural code was entirely related to the animal's behavioral choice; it emerged earlier in PFC than V4 and its enhancement completely generalized to an untrained stimulus configuration. These results implicate two concurrent and synergistic learning processes: a perceptual process that is specific to the details of the trained stimulus feature and a cognitive process that is dependent on the total amount of learning experience in the trained task. When combined, these two learning processes were well predictive of the animal's learning behavior.
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Affiliation(s)
- Rui Jing
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chen Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xin Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Wu Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; College of Life Sciences, Beijing Normal University, Beijing 100875, China.
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10
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Chicharro D, Panzeri S, Haefner RM. Stimulus-dependent relationships between behavioral choice and sensory neural responses. eLife 2021; 10:e54858. [PMID: 33825683 PMCID: PMC8184215 DOI: 10.7554/elife.54858] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/06/2021] [Indexed: 01/16/2023] Open
Abstract
Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.
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Affiliation(s)
- Daniel Chicharro
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di TecnologiaRoveretoItaly
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di TecnologiaRoveretoItaly
| | - Ralf M Haefner
- Brain and Cognitive Sciences, Center for Visual Science, University of RochesterRochesterUnited States
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11
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Solomon SS, Tang H, Sussman E, Kohn A. Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans. Cereb Cortex 2021; 31:3136-3152. [PMID: 33683317 DOI: 10.1093/cercor/bhab014] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 12/06/2020] [Accepted: 01/15/2021] [Indexed: 11/14/2022] Open
Abstract
A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.
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Affiliation(s)
- Selina S Solomon
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Huizhen Tang
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Otorhinolaryngology - Head & Neck Surgery, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Elyse Sussman
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Otorhinolaryngology - Head & Neck Surgery, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Adam Kohn
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Ophthalmology and Vision Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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12
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Mochol G, Kiani R, Moreno-Bote R. Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior. Curr Biol 2021; 31:1234-1244.e6. [PMID: 33639107 DOI: 10.1016/j.cub.2021.01.068] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 10/01/2020] [Accepted: 01/20/2021] [Indexed: 02/07/2023]
Abstract
Goal-directed behavior requires integrating sensory information with prior knowledge about the environment. Behavioral biases that arise from these priors could increase positive outcomes when the priors match the true structure of the environment, but mismatches also happen frequently and could cause unfavorable outcomes. Biases that reduce gains and fail to vanish with training indicate fundamental suboptimalities arising from ingrained heuristics of the brain. Here, we report systematic, gain-reducing choice biases in highly trained monkeys performing a motion direction discrimination task where only the current stimulus is behaviorally relevant. The monkey's bias fluctuated at two distinct time scales: slow, spanning tens to hundreds of trials, and fast, arising from choices and outcomes of the most recent trials. Our findings enabled single trial prediction of biases, which influenced the choice especially on trials with weak stimuli. The pre-stimulus activity of neuronal ensembles in the monkey prearcuate gyrus represented these biases as an offset along the decision axis in the state space. This offset persisted throughout the stimulus viewing period, when sensory information was integrated, leading to a biased choice. The pre-stimulus representation of history-dependent bias was functionally indistinguishable from the neural representation of upcoming choice before stimulus onset, validating our model of single-trial biases and suggesting that pre-stimulus representation of choice could be fully defined by biases inferred from behavioral history. Our results indicate that the prearcuate gyrus reflects intrinsic heuristics that compute bias signals, as well as the mechanisms that integrate them into the oculomotor decision-making process.
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Affiliation(s)
- Gabriela Mochol
- Center for Brain and Cognition and Department of Information and Communications Technologies, Pompeu Fabra University, Barcelona, Spain.
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute, NYU Langone Medical Center, New York, NY 10016, USA; Department of Psychology, New York University, New York, NY 10003, USA
| | - Rubén Moreno-Bote
- Center for Brain and Cognition and Department of Information and Communications Technologies, Pompeu Fabra University, Barcelona, Spain
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13
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Kafashan M, Jaffe AW, Chettih SN, Nogueira R, Arandia-Romero I, Harvey CD, Moreno-Bote R, Drugowitsch J. Scaling of sensory information in large neural populations shows signatures of information-limiting correlations. Nat Commun 2021; 12:473. [PMID: 33473113 PMCID: PMC7817840 DOI: 10.1038/s41467-020-20722-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 12/16/2020] [Indexed: 01/29/2023] Open
Abstract
How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.
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Affiliation(s)
| | - Anna W Jaffe
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Selmaan N Chettih
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Iñigo Arandia-Romero
- ISAAC Lab, Aragón Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain
- IAS-Research Center for Life, Mind, and Society, Department of Logic and Philosophy of Science, University of the Basque Country, UPV-EHU, Donostia-San Sebastián, Spain
| | | | - Rubén Moreno-Bote
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Serra Húnter Fellow Programme and ICREA Academia, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA.
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14
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Krishna A, Tanabe S, Kohn A. Decision Signals in the Local Field Potentials of Early and Mid-Level Macaque Visual Cortex. Cereb Cortex 2021; 31:169-183. [PMID: 32852540 PMCID: PMC7727373 DOI: 10.1093/cercor/bhaa218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 06/12/2020] [Accepted: 07/14/2020] [Indexed: 12/28/2022] Open
Abstract
The neural basis of perceptual decision making has typically been studied using measurements of single neuron activity, though decisions are likely based on the activity of large neuronal ensembles. Local field potentials (LFPs) may, in some cases, serve as a useful proxy for population activity and thus be useful for understanding the neural basis of perceptual decision making. However, little is known about whether LFPs in sensory areas include decision-related signals. We therefore analyzed LFPs recorded using two 48-electrode arrays implanted in primary visual cortex (V1) and area V4 of macaque monkeys trained to perform a fine orientation discrimination task. We found significant choice information in low (0-30 Hz) and higher (70-500 Hz) frequency components of the LFP, but little information in gamma frequencies (30-70 Hz). Choice information was more robust in V4 than V1 and stronger in LFPs than in simultaneously measured spiking activity. LFP-based choice information included a global component, common across electrodes within an area. Our findings reveal the presence of robust choice-related signals in the LFPs recorded in V1 and V4 and suggest that LFPs may be a useful complement to spike-based analyses of decision making.
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Affiliation(s)
- Aravind Krishna
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Bioengineering, School of Chemical and Biotechnology, SASTRA University, Thanjavur 613401, India
| | - Seiji Tanabe
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Adam Kohn
- Dominick 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
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Cowley BR, Snyder AC, Acar K, Williamson RC, Yu BM, Smith MA. Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex. Neuron 2020; 108:551-567.e8. [PMID: 32810433 PMCID: PMC7822647 DOI: 10.1016/j.neuron.2020.07.021] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 06/15/2020] [Accepted: 07/17/2020] [Indexed: 12/22/2022]
Abstract
An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.
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Affiliation(s)
- Benjamin R Cowley
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Adam C Snyder
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA; Department of Neuroscience, University of Rochester, Rochester, NY 14642, USA; Center for Visual Science, University of Rochester, Rochester, NY 14642, USA
| | - Katerina Acar
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ryan C Williamson
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA; University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Byron M Yu
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Matthew A Smith
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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