1
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Stoll FM, Rudebeck PH. Preferences reveal dissociable encoding across prefrontal-limbic circuits. Neuron 2024; 112:2241-2256.e8. [PMID: 38640933 PMCID: PMC11223984 DOI: 10.1016/j.neuron.2024.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 12/04/2023] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
Individual preferences for the flavor of different foods and fluids exert a strong influence on behavior. Most current theories posit that preferences are integrated with other state variables in the orbitofrontal cortex (OFC), which is thought to derive the relative subjective value of available options to guide choice behavior. Here, we report that instead of a single integrated valuation system in the OFC, another complementary one is centered in the ventrolateral prefrontal cortex (vlPFC) in macaques. Specifically, we found that the OFC and vlPFC preferentially represent outcome flavor and outcome probability, respectively, and that preferences are separately integrated into value representations in these areas. In addition, the vlPFC, but not the OFC, represented the probability of receiving the available outcome flavors separately, with the difference between these representations reflecting the degree of preference for each flavor. Thus, both the vlPFC and OFC exhibit dissociable but complementary representations of subjective value, both of which are necessary for decision-making.
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Affiliation(s)
- Frederic M Stoll
- Nash Family Department of Neuroscience, Lipschultz Center for Cognitive Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Peter H Rudebeck
- Nash Family Department of Neuroscience, Lipschultz Center for Cognitive Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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2
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Srinath R, Ni AM, Marucci C, Cohen MR, Brainard DH. Orthogonal neural representations support perceptual judgements of natural stimuli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.14.580134. [PMID: 38464018 PMCID: PMC10925131 DOI: 10.1101/2024.02.14.580134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
In natural behavior, observers must separate relevant information from a barrage of irrelevant information. Many studies have investigated the neural underpinnings of this ability using artificial stimuli presented on simple backgrounds. Natural viewing, however, carries a set of challenges that are inaccessible using artificial stimuli, including neural responses to background objects that are task-irrelevant. An emerging body of evidence suggests that the visual abilities of humans and animals can be modeled through the linear decoding of task-relevant information from visual cortex. This idea suggests the hypothesis that irrelevant features of a natural scene should impair performance on a visual task only if their neural representations intrude on the linear readout of the task relevant feature, as would occur if the representations of task-relevant and irrelevant features are not orthogonal in the underlying neural population. We tested this hypothesis using human psychophysics and monkey neurophysiology, in response to parametrically variable naturalistic stimuli. We demonstrate that 1) the neural representation of one feature (the position of a central object) in visual area V4 is orthogonal to those of several background features, 2) the ability of human observers to precisely judge object position was largely unaffected by task-irrelevant variation in those background features, and 3) many features of the object and the background are orthogonally represented by V4 neural responses. Our observations are consistent with the hypothesis that orthogonal neural representations can support stable perception of objects and features despite the tremendous richness of natural visual scenes.
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Affiliation(s)
- Ramanujan Srinath
- equal contribution
- Department of Neurobiology and Neuroscience Institute, The University of Chicago, Chicago, IL 60637, USA
| | - Amy M. Ni
- equal contribution
- Department of Neurobiology and Neuroscience Institute, The University of Chicago, Chicago, IL 60637, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Claire Marucci
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marlene R. Cohen
- Department of Neurobiology and Neuroscience Institute, The University of Chicago, Chicago, IL 60637, USA
- equal contribution
| | - David H. Brainard
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
- equal contribution
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3
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Chapman AF, Störmer VS. Representational structures as a unifying framework for attention. Trends Cogn Sci 2024; 28:416-427. [PMID: 38280837 PMCID: PMC11290436 DOI: 10.1016/j.tics.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/29/2024]
Abstract
Our visual system consciously processes only a subset of the incoming information. Selective attention allows us to prioritize relevant inputs, and can be allocated to features, locations, and objects. Recent advances in feature-based attention suggest that several selection principles are shared across these domains and that many differences between the effects of attention on perceptual processing can be explained by differences in the underlying representational structures. Moving forward, it can thus be useful to assess how attention changes the structure of the representational spaces over which it operates, which include the spatial organization, feature maps, and object-based coding in visual cortex. This will ultimately add to our understanding of how attention changes the flow of visual information processing more broadly.
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Affiliation(s)
- Angus F Chapman
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA.
| | - Viola S Störmer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
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4
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Zimnik AJ, Cora Ames K, An X, Driscoll L, Lara AH, Russo AA, Susoy V, Cunningham JP, Paninski L, Churchland MM, Glaser JI. Identifying Interpretable Latent Factors with Sparse Component Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578988. [PMID: 38370650 PMCID: PMC10871230 DOI: 10.1101/2024.02.05.578988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.
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Affiliation(s)
- Andrew J Zimnik
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - K Cora Ames
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Xinyue An
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA
| | - Laura Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Allen Institute for Neural Dynamics, Allen Institute, Seattle, CA, USA
| | - Antonio H Lara
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Abigail A Russo
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Vladislav Susoy
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Liam Paninski
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY, USA
| | - Joshua I Glaser
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
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5
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Nozari E, Bertolero MA, Stiso J, Caciagli L, Cornblath EJ, He X, Mahadevan AS, Pappas GJ, Bassett DS. Macroscopic resting-state brain dynamics are best described by linear models. Nat Biomed Eng 2024; 8:68-84. [PMID: 38082179 DOI: 10.1038/s41551-023-01117-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 09/26/2023] [Indexed: 12/22/2023]
Abstract
It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear behaviours. Here we challenge this assumption by leveraging mathematical models derived from measurements of local field potentials via intracranial electroencephalography and of whole-brain blood-oxygen-level-dependent brain activity via functional magnetic resonance imaging. We used state-of-the-art linear and nonlinear families of models to describe spontaneous resting-state activity of 700 participants in the Human Connectome Project and 122 participants in the Restoring Active Memory project. We found that linear autoregressive models provide the best fit across both data types and three performance metrics: predictive power, computational complexity and the extent of the residual dynamics unexplained by the model. To explain this observation, we show that microscopic nonlinear dynamics can be counteracted or masked by four factors associated with macroscopic dynamics: averaging over space and over time, which are inherent to aggregated macroscopic brain activity, and observation noise and limited data samples, which stem from technological limitations. We therefore argue that easier-to-interpret linear models can faithfully describe macroscopic brain dynamics during resting-state conditions.
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Affiliation(s)
- Erfan Nozari
- Department of Mechanical Engineering, University of California, Riverside, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
- Department of Bioengineering, University of California, Riverside, CA, USA
| | - Maxwell A Bertolero
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Eli J Cornblath
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaosong He
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Arun S Mahadevan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - George J Pappas
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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6
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McGinty VB, Lupkin SM. Behavioral read-out from population value signals in primate orbitofrontal cortex. Nat Neurosci 2023; 26:2203-2212. [PMID: 37932464 PMCID: PMC11006434 DOI: 10.1038/s41593-023-01473-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 09/26/2023] [Indexed: 11/08/2023]
Abstract
The primate orbitofrontal cortex (OFC) has long been recognized for its role in value-based decisions; however, the exact mechanism linking value representations in the OFC to decision outcomes has remained elusive. Here, to address this question, we show, in non-human primates, that trial-wise variability in choices can be explained by variability in value signals decoded from many simultaneously recorded OFC neurons. Mechanistically, this relationship is consistent with the projection of activity within a low-dimensional value-encoding subspace onto a potentially higher-dimensional, behaviorally potent output subspace. Identifying this neural-behavioral link answers longstanding questions about the role of the OFC in economic decision-making and suggests population-level read-out mechanisms for the OFC similar to those recently identified in sensory and motor cortex.
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Affiliation(s)
- Vincent B McGinty
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ, USA.
| | - Shira M Lupkin
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ, USA
- Behavioral and Neural Sciences Graduate Program, Rutgers University-Newark, Newark, NJ, USA
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7
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Jerjian SJ, Harsch DR, Fetsch CR. Self-motion perception and sequential decision-making: where are we heading? Philos Trans R Soc Lond B Biol Sci 2023; 378:20220333. [PMID: 37545301 PMCID: PMC10404932 DOI: 10.1098/rstb.2022.0333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/18/2023] [Indexed: 08/08/2023] Open
Abstract
To navigate and guide adaptive behaviour in a dynamic environment, animals must accurately estimate their own motion relative to the external world. This is a fundamentally multisensory process involving integration of visual, vestibular and kinesthetic inputs. Ideal observer models, paired with careful neurophysiological investigation, helped to reveal how visual and vestibular signals are combined to support perception of linear self-motion direction, or heading. Recent work has extended these findings by emphasizing the dimension of time, both with regard to stimulus dynamics and the trade-off between speed and accuracy. Both time and certainty-i.e. the degree of confidence in a multisensory decision-are essential to the ecological goals of the system: terminating a decision process is necessary for timely action, and predicting one's accuracy is critical for making multiple decisions in a sequence, as in navigation. Here, we summarize a leading model for multisensory decision-making, then show how the model can be extended to study confidence in heading discrimination. Lastly, we preview ongoing efforts to bridge self-motion perception and navigation per se, including closed-loop virtual reality and active self-motion. The design of unconstrained, ethologically inspired tasks, accompanied by large-scale neural recordings, raise promise for a deeper understanding of spatial perception and decision-making in the behaving animal. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
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Affiliation(s)
- Steven J. Jerjian
- Solomon H. Snyder Department of Neuroscience, Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Devin R. Harsch
- Solomon H. Snyder Department of Neuroscience, Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Neuroscience and Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Christopher R. Fetsch
- Solomon H. Snyder Department of Neuroscience, Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
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8
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Rezaei MR, Jeoung H, Gharamani A, Saha U, Bhat V, Popovic MR, Yousefi A, Chen R, Lankarany M. Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model. J Neural Eng 2023; 20:056016. [PMID: 37473753 DOI: 10.1088/1741-2552/ace932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/20/2023] [Indexed: 07/22/2023]
Abstract
Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g. the response time), remains to be identified.Approach. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in ten Parkinson's disease (PD) patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy populations.Main results. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies.Significance. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Haseul Jeoung
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ayda Gharamani
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- Worcester Polytechnic Institute, MA, United States of America
| | - Utpal Saha
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Venkat Bhat
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ali Yousefi
- Worcester Polytechnic Institute, MA, United States of America
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Milad Lankarany
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
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9
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Comeaux P, Clark K, Noudoost B. A recruitment through coherence theory of working memory. Prog Neurobiol 2023; 228:102491. [PMID: 37393039 PMCID: PMC10530428 DOI: 10.1016/j.pneurobio.2023.102491] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/14/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
The interactions between prefrontal cortex and other areas during working memory have been studied for decades. Here we outline a conceptual framework describing interactions between these areas during working memory, and review evidence for key elements of this model. We specifically suggest that a top-down signal sent from prefrontal to sensory areas drives oscillations in these areas. Spike timing within sensory areas becomes locked to these working-memory-driven oscillations, and the phase of spiking conveys information about the representation available within these areas. Downstream areas receiving these phase-locked spikes from sensory areas can recover this information via a combination of coherent oscillations and gating of input efficacy based on the phase of their local oscillations. Although the conceptual framework is based on prefrontal interactions with sensory areas during working memory, we also discuss the broader implications of this framework for flexible communication between brain areas in general.
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Affiliation(s)
- Phillip Comeaux
- Dept. of Biomedical Engineering, University of Utah, 36 S. Wasatch Drive, Salt Lake City, UT 84112, USA; Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Kelsey Clark
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Behrad Noudoost
- Dept. of Ophthalmology and Visual Sciences, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA.
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10
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Rouse TC, Ni AM, Huang C, Cohen MR. Topological insights into the neural basis of flexible behavior. Proc Natl Acad Sci U S A 2023; 120:e2219557120. [PMID: 37279273 PMCID: PMC10268229 DOI: 10.1073/pnas.2219557120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/28/2023] [Indexed: 06/08/2023] Open
Abstract
It is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but it is challenging to simultaneously relate all three. Here, we show that topological data analysis (TDA) provides an important bridge between these approaches to studying how brains mediate behavior. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes constrain and distinguish between competing mechanistic models, are connected to subjects' performance on a visual change detection task, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These connections provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease.
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Affiliation(s)
- Tevin C. Rouse
- Division of Biological Sciences, Department of Neurobiology, University of Chicago, Chicago, IL60637
| | - Amy M. Ni
- Dietrich School of Arts and Sciences, Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA15260
| | - Chengcheng Huang
- Dietrich School of Arts and Sciences, Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA15260
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA15260
| | - Marlene R. Cohen
- Division of Biological Sciences, Department of Neurobiology, University of Chicago, Chicago, IL60637
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11
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Kramer LE, Chen YC, Long B, Konkle T, Cohen MR. Contributions of early and mid-level visual cortex to high-level object categorization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.541514. [PMID: 37398251 PMCID: PMC10312552 DOI: 10.1101/2023.05.31.541514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The complexity of visual features for which neurons are tuned increases from early to late stages of the ventral visual stream. Thus, the standard hypothesis is that high-level functions like object categorization are primarily mediated by higher visual areas because they require more complex image formats that are not evident in early visual processing stages. However, human observers can categorize images as objects or animals or as big or small even when the images preserve only some low- and mid-level features but are rendered unidentifiable ('texforms', Long et al., 2018). This observation suggests that even the early visual cortex, in which neurons respond to simple stimulus features, may already encode signals about these more abstract high-level categorical distinctions. We tested this hypothesis by recording from populations of neurons in early and mid-level visual cortical areas while rhesus monkeys viewed texforms and their unaltered source stimuli (simultaneous recordings from areas V1 and V4 in one animal and separate recordings from V1 and V4 in two others). Using recordings from a few dozen neurons, we could decode the real-world size and animacy of both unaltered images and texforms. Furthermore, this neural decoding accuracy across stimuli was related to the ability of human observers to categorize texforms by real-world size and animacy. Our results demonstrate that neuronal populations early in the visual hierarchy contain signals useful for higher-level object perception and suggest that the responses of early visual areas to simple stimulus features display preliminary untangling of higher-level distinctions.
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Affiliation(s)
| | | | - Bria Long
- University of California, Los Angeles
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12
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Ni AM, Huang C, Doiron B, Cohen MR. A general decoding strategy explains the relationship between behavior and correlated variability. eLife 2022; 11:67258. [PMID: 35660134 PMCID: PMC9170243 DOI: 10.7554/elife.67258] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/11/2022] [Indexed: 11/16/2022] Open
Abstract
Improvements in perception are frequently accompanied by decreases in correlated variability in sensory cortex. This relationship is puzzling because overall changes in correlated variability should minimally affect optimal information coding. We hypothesize that this relationship arises because instead of using optimal strategies for decoding the specific stimuli at hand, observers prioritize generality: a single set of neuronal weights to decode any stimuli. We tested this using a combination of multineuron recordings in the visual cortex of behaving rhesus monkeys and a cortical circuit model. We found that general decoders optimized for broad rather than narrow sets of visual stimuli better matched the animals’ decoding strategy, and that their performance was more related to the magnitude of correlated variability. In conclusion, the inverse relationship between perceptual performance and correlated variability can be explained by observers using a general decoding strategy, capable of decoding neuronal responses to the variety of stimuli encountered in natural vision.
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Affiliation(s)
- Amy M Ni
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States
| | - Chengcheng Huang
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States.,Department of Mathematics, University of Pittsburgh, Pittsburgh, United States
| | - Brent Doiron
- Center for the Neural Basis of Cognition, Pittsburgh, United States.,Department of Mathematics, University of Pittsburgh, Pittsburgh, United States
| | - Marlene R Cohen
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States
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13
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Huang C, Pouget A, Doiron B. Internally generated population activity in cortical networks hinders information transmission. SCIENCE ADVANCES 2022; 8:eabg5244. [PMID: 35648863 PMCID: PMC9159697 DOI: 10.1126/sciadv.abg5244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/14/2022] [Indexed: 06/15/2023]
Abstract
How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry establishes neural codes. We study stimulus coding in networks of spiking neuron models with spatially ordered excitatory and inhibitory connectivity. The wiring structure is capable of producing rich population-wide shared neuronal variability that agrees with many features of recorded cortical activity. While both the spatial scales of feedforward and recurrent projections strongly affect noise correlations, only recurrent projections, and in particular inhibitory projections, can introduce correlations that limit the stimulus information available to a decoder. Using a spatial neural field model, we relate the recurrent circuit conditions for information limiting noise correlations to how recurrent excitation and inhibition can form spatiotemporal patterns of population-wide activity.
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Affiliation(s)
- Chengcheng Huang
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Brent Doiron
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Departments of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
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14
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Ni AM, Bowes BS, Ruff DA, Cohen MR. Methylphenidate as a causal test of translational and basic neural coding hypotheses. Proc Natl Acad Sci U S A 2022; 119:e2120529119. [PMID: 35467980 PMCID: PMC9169912 DOI: 10.1073/pnas.2120529119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/17/2022] [Indexed: 11/18/2022] Open
Abstract
Most systems neuroscience studies fall into one of two categories: basic science work aimed at understanding the relationship between neurons and behavior, or translational work aimed at developing treatments for neuropsychiatric disorders. Here we use these two approaches to inform and enhance each other. Our study both tests hypotheses about basic science neural coding principles and elucidates the neuronal mechanisms underlying clinically relevant behavioral effects of systemically administered methylphenidate (Ritalin). We discovered that orally administered methylphenidate, used clinically to treat attention deficit hyperactivity disorder (ADHD) and generally to enhance cognition, increases spatially selective visual attention, enhancing visual performance at only the attended location. Further, we found that this causal manipulation enhances vision in rhesus macaques specifically when it decreases the mean correlated variability of neurons in visual area V4. Our findings demonstrate that the visual system is a platform for understanding the neural underpinnings of both complex cognitive processes (basic science) and neuropsychiatric disorders (translation). Addressing basic science hypotheses, our results are consistent with a scenario in which methylphenidate has cognitively specific effects by working through naturally selective cognitive mechanisms. Clinically, our findings suggest that the often staggeringly specific symptoms of neuropsychiatric disorders may be caused and treated by leveraging general mechanisms.
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Affiliation(s)
- Amy M. Ni
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15260
| | - Brittany S. Bowes
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15260
| | - Douglas A. Ruff
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15260
| | - Marlene R. Cohen
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15260
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15
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16
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Singh V, Burge J, Brainard DH. Equivalent noise characterization of human lightness constancy. J Vis 2022; 22:2. [PMID: 35394508 PMCID: PMC8994201 DOI: 10.1167/jov.22.5.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
A goal of visual perception is to provide stable representations of task-relevant scene properties (e.g. object reflectance) despite variation in task-irrelevant scene properties (e.g. illumination and reflectance of other nearby objects). To study such stability in the context of the perceptual representation of lightness, we introduce a threshold-based psychophysical paradigm. We measure how thresholds for discriminating the achromatic reflectance of a target object (task-relevant property) in rendered naturalistic scenes are impacted by variation in the reflectance functions of background objects (task-irrelevant property), using a two-alternative forced-choice paradigm in which the reflectance of the background objects is randomized across the two intervals of each trial. We control the amount of background reflectance variation by manipulating a statistical model of naturally occurring surface reflectances. For low background object reflectance variation, discrimination thresholds were nearly constant, indicating that observers’ internal noise determines threshold in this regime. As background object reflectance variation increases, its effects start to dominate performance. A model based on signal detection theory allows us to express the effects of task-irrelevant variation in terms of the equivalent noise, that is relative to the intrinsic precision of the task-relevant perceptual representation. The results indicate that although naturally occurring background object reflectance variation does intrude on the perceptual representation of target object lightness, the effect is modest – within a factor of two of the equivalent noise level set by internal noise.
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Affiliation(s)
- Vijay Singh
- Department of Physics, North Carolina Agricultural and Technical State University, Greensboro, NC, USA.,Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, USA.,
| | - Johannes Burge
- Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, USA.,Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.,Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.,Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.,
| | - David H Brainard
- Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, USA.,Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.,Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.,Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.,
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17
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Rezaei MR, Hadjinicolaou AE, Cash SS, Eden UT, Yousefi A. Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data. Neural Comput 2022; 34:1100-1135. [PMID: 35344988 DOI: 10.1162/neco_a_01491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/08/2022] [Indexed: 11/04/2022]
Abstract
With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9.,Krembil Research Institute, University Health Network, Toronto, ON M5T 2S8.,KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada
| | - Alex E Hadjinicolaou
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, U.S.A.
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
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18
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Lange RD, Haefner RM. Task-induced neural covariability as a signature of approximate Bayesian learning and inference. PLoS Comput Biol 2022; 18:e1009557. [PMID: 35259152 PMCID: PMC8963539 DOI: 10.1371/journal.pcbi.1009557] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 03/29/2022] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called “differential correlations” as the observer’s internal model learns the stimulus distribution, and the observer’s behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject’s internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function. Perceptual decision-making has classically been studied in the context of feedforward encoding/ decoding models. Here, we derive predictions for the responses of sensory neurons under the assumption that the brain performs hierarchical Bayesian inference, including feedback signals that communicate task-specific prior expectations. Interestingly, those predictions stand in contrast to some of the conclusions drawn in the classic framework. In particular, we find that Bayesian learning predicts the increase of a type of correlated variability called “differential correlations” over the course of learning. Differential correlations limit information, and hence are seen as harmful in feedforward models. Since our results are also specific to the statistics of a given task, and since they hold under a wide class of theories about how Bayesian probabilities may be represented by neural responses, they constitute a strong test of the Bayesian Brain hypothesis. Our results can explain the task-dependence of correlated variability in prior studies and suggest a reason why these kinds of correlations are surprisingly common in empirical data. Interpreted in a probabilistic framework, correlated variability provides a window into an observer’s task-related beliefs.
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Affiliation(s)
- Richard D. Lange
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Center for Visual Science, University of Rochester, Rochester, New York, United States of America
- * E-mail: (RDL); (RMH)
| | - Ralf M. Haefner
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Center for Visual Science, University of Rochester, Rochester, New York, United States of America
- * E-mail: (RDL); (RMH)
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19
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Wang L, Herman JP, Krauzlis RJ. Neuronal modulation in the mouse superior colliculus during covert visual selective attention. Sci Rep 2022; 12:2482. [PMID: 35169189 PMCID: PMC8847498 DOI: 10.1038/s41598-022-06410-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
Covert visual attention is accomplished by a cascade of mechanisms distributed across multiple brain regions. Visual cortex is associated with enhanced representations of relevant stimulus features, whereas the contributions of subcortical circuits are less well understood but have been associated with selection of relevant spatial locations and suppression of distracting stimuli. As a step toward understanding these subcortical circuits, here we identified how neuronal activity in the intermediate layers of the superior colliculus (SC) of head-fixed mice is modulated during covert visual attention. We found that spatial cues modulated both firing rate and spike-count correlations. Crucially, the cue-related modulation in firing rate was due to enhancement of activity at the cued spatial location rather than suppression at the uncued location, indicating that SC neurons in our task were modulated by an excitatory or disinhibitory circuit mechanism focused on the relevant location, rather than broad inhibition of irrelevant locations. This modulation improved the neuronal discriminability of visual-change-evoked activity, but only when assessed for neuronal activity between the contralateral and ipsilateral SC. Together, our findings indicate that neurons in the mouse SC can contribute to covert visual selective attention by biasing processing in favor of locations expected to contain task-relevant information.
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Affiliation(s)
- Lupeng Wang
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA.
| | - James P Herman
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Richard J Krauzlis
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA.
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20
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Srinath R, Ruff DA, Cohen MR. Attention improves information flow between neuronal populations without changing the communication subspace. Curr Biol 2021; 31:5299-5313.e4. [PMID: 34699782 PMCID: PMC8665027 DOI: 10.1016/j.cub.2021.09.076] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 10/20/2022]
Abstract
Visual attention allows observers to change the influence of different parts of a visual scene on their behavior, suggesting that information can be flexibly shared between visual cortex and neurons involved in decision making. We investigated the neural substrate of flexible information routing by analyzing the activity of populations of visual neurons in the medial temporal area (MT) and oculo-motor neurons in the superior colliculus (SC) while rhesus monkeys switched spatial attention. We demonstrated that attention increases the efficacy of visuomotor communication: trial-to-trial variability in SC population activity could be better predicted by the activity of the MT population (and vice versa) when attention was directed toward their joint receptive fields. Surprisingly, this improvement in prediction was not explained by changes in the dimensionality of the shared subspace or in the magnitude of local or shared pairwise noise correlations. These results lay a foundation for future theoretical and experimental studies into how visual attention can flexibly change information flow between sensory and decision neurons.
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Affiliation(s)
- Ramanujan Srinath
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Douglas A Ruff
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marlene R Cohen
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
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21
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Silent Synapses in Cocaine-Associated Memory and Beyond. J Neurosci 2021; 41:9275-9285. [PMID: 34759051 DOI: 10.1523/jneurosci.1559-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/22/2021] [Accepted: 09/27/2021] [Indexed: 11/21/2022] Open
Abstract
Glutamatergic synapses are key cellular sites where cocaine experience creates memory traces that subsequently promote cocaine craving and seeking. In addition to making across-the-board synaptic adaptations, cocaine experience also generates a discrete population of new synapses that selectively encode cocaine memories. These new synapses are glutamatergic synapses that lack functionally stable AMPARs, often referred to as AMPAR-silent synapses or, simply, silent synapses. They are generated de novo in the NAc by cocaine experience. After drug withdrawal, some of these synapses mature by recruiting AMPARs, contributing to the consolidation of cocaine-associated memory. After cue-induced retrieval of cocaine memories, matured silent synapses alternate between two dynamic states (AMPAR-absent vs AMPAR-containing) that correspond with the behavioral manifestations of destabilization and reconsolidation of these memories. Here, we review the molecular mechanisms underlying silent synapse dynamics during behavior, discuss their contributions to circuit remodeling, and analyze their role in cocaine-memory-driven behaviors. We also propose several mechanisms through which silent synapses can form neuronal ensembles as well as cross-region circuit engrams for cocaine-specific behaviors. These perspectives lead to our hypothesis that cocaine-generated silent synapses stand as a distinct set of synaptic substrates encoding key aspects of cocaine memory that drive cocaine relapse.
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22
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Learning nonnative speech sounds changes local encoding in the adult human cortex. Proc Natl Acad Sci U S A 2021; 118:2101777118. [PMID: 34475209 DOI: 10.1073/pnas.2101777118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 07/12/2021] [Indexed: 11/18/2022] Open
Abstract
Adults can learn to identify nonnative speech sounds with training, albeit with substantial variability in learning behavior. Increases in behavioral accuracy are associated with increased separability for sound representations in cortical speech areas. However, it remains unclear whether individual auditory neural populations all show the same types of changes with learning, or whether there are heterogeneous encoding patterns. Here, we used high-resolution direct neural recordings to examine local population response patterns, while native English listeners learned to recognize unfamiliar vocal pitch patterns in Mandarin Chinese tones. We found a distributed set of neural populations in bilateral superior temporal gyrus and ventrolateral frontal cortex, where the encoding of Mandarin tones changed throughout training as a function of trial-by-trial accuracy ("learning effect"), including both increases and decreases in the separability of tones. These populations were distinct from populations that showed changes as a function of exposure to the stimuli regardless of trial-by-trial accuracy. These learning effects were driven in part by more variable neural responses to repeated presentations of acoustically identical stimuli. Finally, learning effects could be predicted from speech-evoked activity even before training, suggesting that intrinsic properties of these populations make them amenable to behavior-related changes. Together, these results demonstrate that nonnative speech sound learning involves a wide array of changes in neural representations across a distributed set of brain regions.
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23
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Quinn KR, Seillier L, Butts DA, Nienborg H. Decision-related feedback in visual cortex lacks spatial selectivity. Nat Commun 2021; 12:4473. [PMID: 34294703 PMCID: PMC8298450 DOI: 10.1038/s41467-021-24629-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/22/2021] [Indexed: 11/21/2022] Open
Abstract
Feedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity. Feedback modulates visual neurons, thought to help achieve flexible task performance. Here, the authors show decision-related feedback is not only relayed to task-relevant neurons, suggesting a broader mechanism and supporting a previously hypothesized link to feature-based attention.
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Affiliation(s)
| | | | - Daniel A Butts
- Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Hendrikje Nienborg
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
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24
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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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25
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Jang AI, Sharma R, Drugowitsch J. Optimal policy for attention-modulated decisions explains human fixation behavior. eLife 2021; 10:e63436. [PMID: 33769284 PMCID: PMC8064754 DOI: 10.7554/elife.63436] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 03/17/2021] [Indexed: 01/23/2023] Open
Abstract
Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.
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Affiliation(s)
- Anthony I Jang
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Ravi Sharma
- Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, UC San Diego School of MedicineLa JollaUnited States
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
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26
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Ruff DA, Xue C, Kramer LE, Baqai F, Cohen MR. Low rank mechanisms underlying flexible visual representations. Proc Natl Acad Sci U S A 2020; 117:29321-29329. [PMID: 33229536 PMCID: PMC7703603 DOI: 10.1073/pnas.2005797117] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Neuronal population responses to sensory stimuli are remarkably flexible. The responses of neurons in visual cortex have heterogeneous dependence on stimulus properties (e.g., contrast), processes that affect all stages of visual processing (e.g., adaptation), and cognitive processes (e.g., attention or task switching). Understanding whether these processes affect similar neuronal populations and whether they have similar effects on entire populations can provide insight into whether they utilize analogous mechanisms. In particular, it has recently been demonstrated that attention has low rank effects on the covariability of populations of visual neurons, which impacts perception and strongly constrains mechanistic models. We hypothesized that measuring changes in population covariability associated with other sensory and cognitive processes could clarify whether they utilize similar mechanisms or computations. Our experimental design included measurements in multiple visual areas using four distinct sensory and cognitive processes. We found that contrast, adaptation, attention, and task switching affect the variability of responses of populations of neurons in primate visual cortex in a similarly low rank way. These results suggest that a given circuit may use similar mechanisms to perform many forms of modulation and likely reflects a general principle that applies to a wide range of brain areas and sensory, cognitive, and motor processes.
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Affiliation(s)
- Douglas A Ruff
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
| | - Cheng Xue
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
| | - Lily E Kramer
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260
| | - Faisal Baqai
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15260
| | - Marlene R Cohen
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260;
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15260
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27
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Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity. Nat Biomed Eng 2020; 4:672-685. [PMID: 32313100 DOI: 10.1038/s41551-020-0542-9] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 02/21/2020] [Indexed: 12/31/2022]
Abstract
The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.
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28
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Abstract
Neural activity and behavior are both notoriously variable, with responses differing widely between repeated presentation of identical stimuli or trials. Recent results in humans and animals reveal that these variations are not random in their nature, but may in fact be due in large part to rapid shifts in neural, cognitive, and behavioral states. Here we review recent advances in the understanding of rapid variations in the waking state, how variations are generated, and how they modulate neural and behavioral responses in both mice and humans. We propose that the brain has an identifiable set of states through which it wanders continuously in a nonrandom fashion, owing to the activity of both ascending modulatory and fast-acting corticocortical and subcortical-cortical neural pathways. These state variations provide the backdrop upon which the brain operates, and understanding them is critical to making progress in revealing the neural mechanisms underlying cognition and behavior.
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Affiliation(s)
- David A McCormick
- Institute of Neuroscience, University of Oregon, Eugene, Oregon 97403, USA;
| | - Dennis B Nestvogel
- Institute of Neuroscience, University of Oregon, Eugene, Oregon 97403, USA;
| | - Biyu J He
- Departments of Neurology, Neuroscience and Physiology, and Radiology, Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA
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29
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Mechanisms underlying gain modulation in the cortex. Nat Rev Neurosci 2020; 21:80-92. [PMID: 31911627 DOI: 10.1038/s41583-019-0253-y] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2019] [Indexed: 01/19/2023]
Abstract
Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.
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Bányai M, Orbán G. Noise correlations and perceptual inference. Curr Opin Neurobiol 2019; 58:209-217. [PMID: 31593872 DOI: 10.1016/j.conb.2019.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 07/23/2019] [Accepted: 09/04/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Mihály Bányai
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary; Center for Cognitive Computation, Central European University, Budapest, Hungary
| | - Gergő Orbán
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary; Center for Cognitive Computation, Central European University, Budapest, Hungary.
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Ruff DA, Cohen MR. Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations. Nat Neurosci 2019; 22:1669-1676. [PMID: 31477898 PMCID: PMC6760994 DOI: 10.1038/s41593-019-0477-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 07/24/2019] [Indexed: 12/23/2022]
Abstract
Visual attention dramatically improves individuals' ability to see and modulates the responses of neurons in every known visual and oculomotor area, but whether such modulations can account for perceptual improvements is unclear. We measured the relationship between populations of visual neurons, oculomotor neurons and behavior during detection and discrimination tasks. We found that neither of the two prominent hypothesized neuronal mechanisms underlying attention (which concern changes in information coding and the way sensory information is read out) provide a satisfying account of the observed behavioral improvements. Instead, our results are more consistent with the hypothesis that attention reshapes the representation of attended stimuli to more effectively influence behavior. Our results suggest a path toward understanding the neural underpinnings of perception and cognition in health and disease by analyzing neuronal responses in ways that are constrained by behavior and interactions between brain areas.
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Affiliation(s)
- Douglas A Ruff
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Marlene R Cohen
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
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Williamson RC, Doiron B, Smith MA, Yu BM. Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction. Curr Opin Neurobiol 2019; 55:40-47. [PMID: 30677702 DOI: 10.1016/j.conb.2018.12.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 12/16/2018] [Accepted: 12/17/2018] [Indexed: 12/21/2022]
Abstract
A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.
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Affiliation(s)
- Ryan C Williamson
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA; School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brent Doiron
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew A Smith
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Byron M Yu
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Electrical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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33
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Roth N, Rust NC. Rethinking assumptions about how trial and nuisance variability impact neural task performance in a fast-processing regime. J Neurophysiol 2019; 121:115-130. [PMID: 30403544 DOI: 10.1152/jn.00503.2018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Task performance is determined not only by the amount of task-relevant signal present in our brains but also by the presence of noise, which can arise from multiple sources. Internal noise, or "trial variability," manifests as trial-by-trial variations in neural responses under seemingly identical conditions. External factors can also translate into noise, particularly when a task requires extraction of a particular type of information from our environment amid changes in other task-irrelevant "nuisance" parameters. To better understand how signal, trial variability, and nuisance variability combine to determine neural task performance, we explored their interactions, both in simulation and when applied to recorded neural data. This exploration revealed that trial variability is typically larger than a neuron's task-relevant signal for tasks with fast reaction times, where spike count integration windows are short. In this low signal-to-trial variability regime, nuisance variability has the counterintuitive property of having a negligible impact on single-neuron task performance, even when it dominates the task-relevant signal. The inconsequential impact of nuisance variability on individual neurons also extends to descriptions of population performance, under the assumption that both trial and nuisance variability are uncorrelated between neurons. These results demonstrate that some basic intuitions about neural coding are misguided in the context of a fast-processing, low-spike-count regime. NEW & NOTEWORTHY Many everyday tasks require us to extract specific information from our environment while ignoring other things. When the neurons in our brains that carry task-relevant signals are also modulated by task-irrelevant "nuisance" information, nuisance modulation is expected to act as performance-limiting noise. Using both simulated and recorded neural data, we demonstrate that these intuitions are misguided when the brain operates in a fast-processing, low-spike-count regime, where nuisance variability is largely inconsequential for performance.
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Affiliation(s)
- Noam Roth
- Department of Psychology, University of Pennsylvania , Philadelphia, Pennsylvania
| | - Nicole C Rust
- Department of Psychology, University of Pennsylvania , Philadelphia, Pennsylvania
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