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Chang YT, Finkel EA, Xu D, O'Connor DH. Rule-based modulation of a sensorimotor transformation across cortical areas. eLife 2024; 12:RP92620. [PMID: 38842277 PMCID: PMC11156468 DOI: 10.7554/elife.92620] [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] [Indexed: 06/07/2024] Open
Abstract
Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes and uses rule information to guide behavior. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task where they switched between two rules: licking in response to tactile stimuli while rejecting visual stimuli, or vice versa. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, single-neuron activity distinguished between the two rules both prior to and in response to the tactile stimulus. We hypothesized that neural populations in these areas would show rule-dependent preparatory states, which would shape the subsequent sensory processing and behavior. This hypothesis was supported for the motor cortical areas (MM and ALM) by findings that (1) the current task rule could be decoded from pre-stimulus population activity; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states impaired task performance. Our findings indicate that flexible action selection in response to sensory input can occur via configuration of preparatory states in the motor cortex.
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Affiliation(s)
- Yi-Ting Chang
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
| | - Eric A Finkel
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Duo Xu
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
| | - Daniel H O'Connor
- Solomon H. Snyder Department of Neuroscience, Kavli Neuroscience Discovery Institute, Brain Science Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
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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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Marrero K, Aruljothi K, Delgadillo C, Kabbara S, Swatch L, Zagha E. Goal-Directed Learning is Multidimensional and Accompanied by Diverse and Widespread Changes in Neocortical Signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.13.528412. [PMID: 36824924 PMCID: PMC9948952 DOI: 10.1101/2023.02.13.528412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of licking-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.
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Affiliation(s)
- Krista Marrero
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Krithiga Aruljothi
- Department of Psychology, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Christian Delgadillo
- Division of Biomedical Sciences, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Sarah Kabbara
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Lovleen Swatch
- College of Natural & Agricultural Sciences, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Edward Zagha
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
- Department of Psychology, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
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4
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Johnston R, Smith MA. Brain-wide arousal signals are segregated from movement planning in the superior colliculus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591284. [PMID: 38746466 PMCID: PMC11092505 DOI: 10.1101/2024.04.26.591284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The superior colliculus (SC) is traditionally considered a brain region that functions as an interface between processing visual inputs and generating eye movement outputs. Although its role as a primary reflex center is thought to be conserved across vertebrate species, evidence suggests that the SC has evolved to support higher-order cognitive functions including spatial attention. When it comes to oculomotor areas such as the SC, it is critical that high precision fixation and eye movements are maintained even in the presence of signals related to ongoing changes in cognition and brain state, both of which have the potential to interfere with eye position encoding and movement generation. In this study, we recorded spiking responses of neuronal populations in the SC while monkeys performed a memory-guided saccade task and found that the activity of some of the neurons fluctuated over tens of minutes. By leveraging the statistical power afforded by high-dimensional neuronal recordings, we were able to identify a low-dimensional pattern of activity that was correlated with the subjects' arousal levels. Importantly, we found that the spiking responses of deep-layer SC neurons were less correlated with this brain-wide arousal signal, and that neural activity associated with changes in pupil size and saccade tuning did not overlap in population activity space with movement initiation signals. Taken together, these findings provide a framework for understanding how signals related to cognition and arousal can be embedded in the population activity of oculomotor structures without compromising the fidelity of the motor output.
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Affiliation(s)
- Richard Johnston
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, USA
| | - Matthew A. Smith
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, USA
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5
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Jahn CI, Markov NT, Morea B, Daw ND, Ebitz RB, Buschman TJ. Learning attentional templates for value-based decision-making. Cell 2024; 187:1476-1489.e21. [PMID: 38401541 DOI: 10.1016/j.cell.2024.01.041] [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: 07/10/2023] [Revised: 12/18/2023] [Accepted: 01/25/2024] [Indexed: 02/26/2024]
Abstract
Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.
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Affiliation(s)
- Caroline I Jahn
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA.
| | - Nikola T Markov
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Britney Morea
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Nathaniel D Daw
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Princeton, NJ 08540, USA
| | - R Becket Ebitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Department of Neurosciences, Université de Montréal, Montréal, QC H3C 3J7, Canada
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Princeton, NJ 08540, USA.
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6
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Chang YT, Finkel EA, Xu D, O'Connor DH. Rule-based modulation of a sensorimotor transformation across cortical areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.21.554194. [PMID: 37662301 PMCID: PMC10473613 DOI: 10.1101/2023.08.21.554194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes rule information and uses this information to guide behavioral responses to sensory stimuli. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task in which they switched between two rules in different blocks of trials: licking in response to tactile stimuli applied to a whisker while rejecting visual stimuli, or licking to visual stimuli while rejecting the tactile stimuli. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, the single-trial activity of individual neurons distinguished between the two rules both prior to and in response to the tactile stimulus. Variable rule-dependent responses to identical stimuli could in principle occur via appropriate configuration of pre-stimulus preparatory states of a neural population, which would shape the subsequent response. We hypothesized that neural populations in S1, S2, MM and ALM would show preparatory activity states that were set in a rule-dependent manner to cause processing of sensory information according to the current rule. This hypothesis was supported for the motor cortical areas by findings that (1) the current task rule could be decoded from pre-stimulus population activity in ALM and MM; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states within ALM and MM impaired task performance. Our findings indicate that flexible selection of an appropriate action in response to a sensory input can occur via configuration of preparatory states in the motor cortex.
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7
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Coop SH, Yates JL, Mitchell JF. Pre-saccadic Neural Enhancements in Marmoset Area MT. J Neurosci 2024; 44:e2034222023. [PMID: 38050176 PMCID: PMC10860570 DOI: 10.1523/jneurosci.2034-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/15/2023] [Accepted: 11/20/2023] [Indexed: 12/06/2023] Open
Abstract
Each time we make an eye movement, attention moves before the eyes, resulting in a perceptual enhancement at the target. Recent psychophysical studies suggest that this pre-saccadic attention enhances the visual features at the saccade target, whereas covert attention causes only spatially selective enhancements. While previous nonhuman primate studies have found that pre-saccadic attention does enhance neural responses spatially, no studies have tested whether changes in neural tuning reflect an automatic feature enhancement. Here we examined pre-saccadic attention using a saccade foraging task developed for marmoset monkeys (one male and one female). We recorded from neurons in the middle temporal area with peripheral receptive fields that contained a motion stimulus, which would either be the target of a saccade or a distracter as a saccade was made to another location. We established that marmosets, like macaques, show enhanced pre-saccadic neural responses for saccades toward the receptive field, including increases in firing rate and motion information. We then examined if the specific changes in neural tuning might support feature enhancements for the target. Neurons exhibited diverse changes in tuning but predominantly showed additive and multiplicative increases that were uniformly applied across motion directions. These findings confirm that marmoset monkeys, like macaques, exhibit pre-saccadic neural enhancements during saccade foraging tasks with minimal training requirements. However, at the level of individual neurons, the lack of feature-tuned enhancements is similar to neural effects reported during covert spatial attention.
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Affiliation(s)
- Shanna H Coop
- Brain and Cognitive Sciences, University of Rochester, Rochester 14627-0268, New York
- Center for Visual Science, University of Rochester, Rochester 14627-0268, New York
| | - Jacob L Yates
- Brain and Cognitive Sciences, University of Rochester, Rochester 14627-0268, New York
- Center for Visual Science, University of Rochester, Rochester 14627-0268, New York
- Department of Biology, University of Maryland College Park, College Park, Maryland, 20742-5025
| | - Jude F Mitchell
- Brain and Cognitive Sciences, University of Rochester, Rochester 14627-0268, New York
- Center for Visual Science, University of Rochester, Rochester 14627-0268, New York
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8
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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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9
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Hajnal MA, Tran D, Einstein M, Martelo MV, Safaryan K, Polack PO, Golshani P, Orbán G. Continuous multiplexed population representations of task context in the mouse primary visual cortex. Nat Commun 2023; 14:6687. [PMID: 37865648 PMCID: PMC10590415 DOI: 10.1038/s41467-023-42441-w] [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: 03/10/2023] [Accepted: 10/10/2023] [Indexed: 10/23/2023] Open
Abstract
Effective task execution requires the representation of multiple task-related variables that determine how stimuli lead to correct responses. Even the primary visual cortex (V1) represents other task-related variables such as expectations, choice, and context. However, it is unclear how V1 can flexibly accommodate these variables without interfering with visual representations. We trained mice on a context-switching cross-modal decision task, where performance depends on inferring task context. We found that the context signal that emerged in V1 was behaviorally relevant as it strongly covaried with performance, independent from movement. Importantly, this signal was integrated into V1 representation by multiplexing visual and context signals into orthogonal subspaces. In addition, auditory and choice signals were also multiplexed as these signals were orthogonal to the context representation. Thus, multiplexing allows V1 to integrate visual inputs with other sensory modalities and cognitive variables to avoid interference with the visual representation while ensuring the maintenance of task-relevant variables.
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Affiliation(s)
- Márton Albert Hajnal
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary
| | - Duy Tran
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Albert Einstein College of Medicine, New York, NY, 10461, USA
| | - Michael Einstein
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Mauricio Vallejo Martelo
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Karen Safaryan
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Pierre-Olivier Polack
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- West Los Angeles VA Medical Center, CA, 90073, Los Angeles, USA.
| | - Gergő Orbán
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary.
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10
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Jiang Y, He S, Zhang J. Different roles of response covariability and its attentional modulation in the sensory cortex and posterior parietal cortex. Proc Natl Acad Sci U S A 2023; 120:e2216942120. [PMID: 37812698 PMCID: PMC10589615 DOI: 10.1073/pnas.2216942120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 08/16/2023] [Indexed: 10/11/2023] Open
Abstract
The covariability of neural responses in the neuron population is highly relevant to the information encoding. Cognitive processes, such as attention, are found to modulate the covariability in the visual cortex to improve information encoding, suggesting the computational advantage of covariability modulation in the neural system. However, is the covariability modulation a general mechanism for enhanced information encoding throughout the information processing pathway, or only adopted in certain processing stages, depending on the property of neural representation? Here, with ultrahigh-field MRI, we examined the covariability, which was estimated by noise correlation, in different attention states in the early visual cortex and posterior parietal cortex (PPC) of the human brain, and its relationship to the quality of information encoding. Our results showed that while attention decreased the covariability to improve the stimulus encoding in the early visual cortex, covariability modulation was not observed in the PPC, where covariability had little impact on information encoding. Further, attention promoted the information flow between the early visual cortex and PPC, with an apparent emphasis on a flow from high- to low-dimensional representations, suggesting the existence of a reduction in the dimensionality of neural representation from the early visual cortex to PPC. Finally, the neural response patterns in the PPC could predict the amplitudes of covariability change in the early visual cortex, indicating a top-down control from the PPC to early visual cortex. Our findings reveal the specific roles of the sensory cortex and PPC during attentional modulation of covariability, determined by the complexity and fidelity of the neural representation in each cortical region.
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Affiliation(s)
- Yong Jiang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing100101, China
| | - Sheng He
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing100101, China
- Institute of AI, Hefei Comprehensive National Science Center, Hefei230088, China
- University of Chinese Academy of Sciences, Beijing100049, China
| | - Jiedong Zhang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing100101, China
- University of Chinese Academy of Sciences, Beijing100049, China
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11
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Stan PL, Smith MA. Expectation reshapes V4 neuronal activity and improves perceptual performance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.27.555026. [PMID: 37693510 PMCID: PMC10491105 DOI: 10.1101/2023.08.27.555026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Recent visual experience creates expectations that heavily influence our visual perception. How does expectation shape the activity of cortical neurons to allow for improved perceptual discrimination of visual inputs? We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different expectation conditions. We found that higher expectation led to an improvement in the ability to detect a change in an image. This improvement was associated with decreased neural responses to the image, providing evidence that a reduction in activity can improve stimulus encoding. The decrease in response could not be fully explained by short-timescale adaptation, suggesting partially separate mechanisms of adaptation and expectation. Additionally, higher expectation was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which expectation influences perception. Taken together, the results of our study contribute to an understanding of how visual experience and expectation can shape our perception and behavior through modulating activity patterns across the visual cortex.
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12
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Haggard M, Chacron MJ. Coding of object location by heterogeneous neural populations with spatially dependent correlations in weakly electric fish. PLoS Comput Biol 2023; 19:e1010938. [PMID: 36867650 PMCID: PMC10016687 DOI: 10.1371/journal.pcbi.1010938] [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] [Received: 10/12/2022] [Revised: 03/15/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Understanding how neural populations encode sensory stimuli remains a central problem in neuroscience. Here we performed multi-unit recordings from sensory neural populations in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus in response to stimuli located at different positions along the rostro-caudal axis. Our results reveal that the spatial dependence of correlated activity along receptive fields can help mitigate the deleterious effects that these correlations would otherwise have if they were spatially independent. Moreover, using mathematical modeling, we show that experimentally observed heterogeneities in the receptive fields of neurons help optimize information transmission as to object location. Taken together, our results have important implications for understanding how sensory neurons whose receptive fields display antagonistic center-surround organization encode location. Important similarities between the electrosensory system and other sensory systems suggest that our results will be applicable elsewhere.
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Affiliation(s)
- Myriah Haggard
- Quantitative Life Sciences, McGill University, Montreal, Canada
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13
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Scott DN, Frank MJ. Adaptive control of synaptic plasticity integrates micro- and macroscopic network function. Neuropsychopharmacology 2023; 48:121-144. [PMID: 36038780 PMCID: PMC9700774 DOI: 10.1038/s41386-022-01374-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/09/2022]
Abstract
Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.
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Affiliation(s)
- Daniel N Scott
- Cognitive Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| | - Michael J Frank
- Cognitive Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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14
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McBurney-Lin J, Vargova G, Garad M, Zagha E, Yang H. The locus coeruleus mediates behavioral flexibility. Cell Rep 2022; 41:111534. [PMID: 36288712 PMCID: PMC9662304 DOI: 10.1016/j.celrep.2022.111534] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/05/2022] [Accepted: 09/29/2022] [Indexed: 11/25/2022] Open
Abstract
Behavioral flexibility is the ability to adjust behavioral strategies in response to changing environmental contingencies. A major hypothesis in the field posits that the activity of neurons in the locus coeruleus (LC) plays an important role in mediating behavioral flexibility. To test this hypothesis, we developed a tactile-based rule-shift detection task in which mice responded to left and right whisker deflections in a context-dependent manner and exhibited varying degrees of switching behavior. Recording spiking activity from optogenetically tagged neurons in the LC at millisecond precision during task performance revealed a prominent graded correlation between baseline LC activity and behavioral flexibility, where higher baseline activity following a rule change was associated with faster behavioral switching to the new rule. Increasing baseline LC activity with optogenetic activation accelerated task switching and improved task performance. Overall, our study provides important evidence to reveal the link between LC activity and behavioral flexibility.
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Affiliation(s)
- Jim McBurney-Lin
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, CA 92521, USA,Neuroscience Graduate Program, University of California, Riverside, Riverside, CA 92521, USA,These authors contributed equally
| | - Greta Vargova
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, CA 92521, USA,These authors contributed equally
| | - Machhindra Garad
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, CA 92521, USA
| | - Edward Zagha
- Neuroscience Graduate Program, University of California, Riverside, Riverside, CA 92521, USA,Department of Psychology, University of California, Riverside, Riverside, CA 92521, USA
| | - Hongdian Yang
- Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, CA 92521, USA; Neuroscience Graduate Program, University of California, Riverside, Riverside, CA 92521, USA.
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15
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Arlt C, Barroso-Luque R, Kira S, Bruno CA, Xia N, Chettih SN, Soares S, Pettit NL, Harvey CD. Cognitive experience alters cortical involvement in goal-directed navigation. eLife 2022; 11:76051. [PMID: 35735909 PMCID: PMC9259027 DOI: 10.7554/elife.76051] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Neural activity in the mammalian cortex has been studied extensively during decision tasks, and recent work aims to identify under what conditions cortex is actually necessary for these tasks. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same navigation decision task, revealing past learning as a critical determinant of whether cortex is necessary for goal-directed navigation. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multiarea calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron–neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in goal-directed navigation.
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Affiliation(s)
- Charlotte Arlt
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | | | - Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Carissa A Bruno
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Ningjing Xia
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Selmaan N Chettih
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Sofia Soares
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Noah L Pettit
- Department of Neurobiology, Harvard Medical School, Boston, United States
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16
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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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17
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18
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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: 27] [Impact Index Per Article: 13.5] [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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19
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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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20
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Speed A, Haider B. Probing mechanisms of visual spatial attention in mice. Trends Neurosci 2021; 44:822-836. [PMID: 34446296 PMCID: PMC8484049 DOI: 10.1016/j.tins.2021.07.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/05/2021] [Accepted: 07/30/2021] [Indexed: 11/25/2022]
Abstract
The role of spatial attention for visual perception has been thoroughly studied in primates, but less so in mice. Several behavioral tasks in mice reveal spatial attentional effects, with similarities to observations in primates. Pairing these tasks with large-scale, cell-type-specific techniques could enable deeper access to underlying mechanisms, and help define the utility and limitations of resolving attentional effects on visual perception and neural activity in mice. In this Review, we evaluate behavioral and neural evidence for visual spatial attention in mice; assess how specializations of the mouse visual system and behavioral repertoire impact interpretation of spatial attentional effects; and outline how several measurement and manipulation techniques in mice could precisely test and refine models of attentional modulation across scales.
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Affiliation(s)
- Anderson Speed
- Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Bilal Haider
- Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA.
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21
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Umakantha A, Morina R, Cowley BR, Snyder AC, Smith MA, Yu BM. Bridging neuronal correlations and dimensionality reduction. Neuron 2021; 109:2740-2754.e12. [PMID: 34293295 PMCID: PMC8505167 DOI: 10.1016/j.neuron.2021.06.028] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 05/05/2021] [Accepted: 06/25/2021] [Indexed: 01/01/2023]
Abstract
Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.
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Affiliation(s)
- Akash Umakantha
- Carnegie Mellon Neuroscience Institute, Pittsburgh, PA 15213, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Rudina Morina
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Benjamin R Cowley
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Adam C Snyder
- 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
| | - Matthew A Smith
- Carnegie Mellon Neuroscience Institute, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Byron M Yu
- Carnegie Mellon Neuroscience Institute, 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.
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22
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Friedl-Werner A, Machado ML, Balestra C, Liegard Y, Philoxene B, Brauns K, Stahn AC, Hitier M, Besnard S. Impaired Attentional Processing During Parabolic Flight. Front Physiol 2021; 12:675426. [PMID: 34054584 PMCID: PMC8155259 DOI: 10.3389/fphys.2021.675426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/09/2021] [Indexed: 11/19/2022] Open
Abstract
Previous studies suggest that altered gravity levels during parabolic flight maneuvers affect spatial updating. Little is known about the impact of the experimental setting and psychological stressors associated with parabolic flight experiments on attentional processes. To address this gap, we investigated the level of alertness, selective and sustained attention in 1 and 0 g using a Go/No-Go Continuous Performance Task. We also identified several parameters associated with the experimental set-up of a parabolic flight that could be expected to affect attentional processing. These included the use of scopolamine, sleep quality prior to the flight day, participant’s stress level as well as mood and anxiety state before and after the parabolic flight. We observed a deterioration in attentional processing prior to the first parabola that was further aggravated in weightlessness and returned to baseline after the last parabola. Reaction Time, Hit and False Alarm Rate were moderately correlated with self-reported anxiety state, but not cortisol levels or emotional states. The use of scopolamine had minor effects on Reaction Time. Our results confirm previous studies reporting impairments of cognitive performance in 0 g, and highlight important aspects that should be considered for the design of behavioral research experiments in future parabolic flight campaigns.
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Affiliation(s)
- Anika Friedl-Werner
- Charité - Universitätsmedizin Berlin, a Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany.,Université de Normandie, INSERM U1075 COMETE, Caen, France
| | | | - Costantino Balestra
- Environmental, Occupational & Ageing "Integrative Physiology" Laboratory, Haute Ecole Bruxelles-Brabant, Brussels, Belgium.,DAN Europe Research Division (Roseto (It)-Brussels (B)), Brussels, Belgium
| | | | | | - Katharina Brauns
- Charité - Universitätsmedizin Berlin, a Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany
| | - Alexander C Stahn
- Charité - Universitätsmedizin Berlin, a Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany.,Unit of Experimental Psychiatry, Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Martin Hitier
- Université de Normandie, INSERM U1075 COMETE, Caen, France.,Department of Otolaryngology Head and Neck Surgery, Centre Hospitalier Universitaire de Caen Normandie, Caen, France.,Department of Anatomy, Université de Normandie, Caen, France
| | - Stephane Besnard
- Université de Normandie, INSERM U1075 COMETE, Caen, France.,Aix Marseille Université, CNRS, UMR 7260, Laboratoire de Neurosciences Sensorielles et Cognitives - Equipe Physiopathologie et Thérapie des Désordres Vestibulaires, Marseille, France
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23
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Hennig JA, Oby ER, Golub MD, Bahureksa LA, Sadtler PT, Quick KM, Ryu SI, Tyler-Kabara EC, Batista AP, Chase SM, Yu BM. Learning is shaped by abrupt changes in neural engagement. Nat Neurosci 2021; 24:727-736. [PMID: 33782622 DOI: 10.1038/s41593-021-00822-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 02/22/2021] [Indexed: 01/30/2023]
Abstract
Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.
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Affiliation(s)
- Jay A Hennig
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA. .,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA. .,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Emily R Oby
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew D Golub
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Lindsay A Bahureksa
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Patrick T Sadtler
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kristin M Quick
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Aaron P Batista
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven M Chase
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Byron M Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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24
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Clancy KB, Mrsic-Flogel TD. The sensory representation of causally controlled objects. Neuron 2021; 109:677-689.e4. [PMID: 33357383 PMCID: PMC7889580 DOI: 10.1016/j.neuron.2020.12.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 08/17/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022]
Abstract
Intentional control over external objects is informed by our sensory experience of them. To study how causal relationships are learned and effected, we devised a brain machine interface (BMI) task using wide-field calcium signals. Mice learned to entrain activity patterns in arbitrary pairs of cortical regions to guide a visual cursor to a target location for reward. Brain areas that were normally correlated could be rapidly reconfigured to exert control over the cursor in a sensory-feedback-dependent manner. Higher visual cortex was more engaged when expert but not naive animals controlled the cursor. Individual neurons in higher visual cortex responded more strongly to the cursor when mice controlled it than when they passively viewed it, with the greatest response boosting as the cursor approached the target location. Thus, representations of causally controlled objects are sensitive to intention and proximity to the subject's goal, potentially strengthening sensory feedback to allow more fluent control.
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Affiliation(s)
- Kelly B Clancy
- Biozentrum, University of Basel, 70 Klingelbergstrasse, 4056 Basel, Switzerland.
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25
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Bae H, Kim SJ, Kim CE. Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks. Front Syst Neurosci 2021; 14:615129. [PMID: 33519390 PMCID: PMC7843526 DOI: 10.3389/fnsys.2020.615129] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/14/2020] [Indexed: 12/26/2022] Open
Abstract
One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.
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Affiliation(s)
- Hyojin Bae
- Department of Physiology, Gachon University College of Korean Medicine, Seongnam, South Korea
| | - Sang Jeong Kim
- Laboratory of Neurophysiology, Department of Physiology, Seoul National University College of Medicine, Seoul, South Korea
| | - Chang-Eop Kim
- Department of Physiology, Gachon University College of Korean Medicine, Seongnam, South Korea
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26
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Jacobs EAK, Steinmetz NA, Peters AJ, Carandini M, Harris KD. Cortical State Fluctuations during Sensory Decision Making. Curr Biol 2020; 30:4944-4955.e7. [PMID: 33096037 PMCID: PMC7758730 DOI: 10.1016/j.cub.2020.09.067] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 07/28/2020] [Accepted: 09/21/2020] [Indexed: 12/16/2022]
Abstract
In many behavioral tasks, cortex enters a desynchronized state where low-frequency fluctuations in population activity are suppressed. The precise behavioral correlates of desynchronization and its global organization are unclear. One hypothesis holds that desynchronization enhances stimulus coding in the relevant sensory cortex. Another hypothesis holds that desynchronization reflects global arousal, such as task engagement. Here, we trained mice on tasks where task engagement could be distinguished from sensory accuracy. Using widefield calcium imaging, we found that performance-related desynchronization was global and correlated better with engagement than with accuracy. Consistent with this link between desynchronization and engagement, rewards had a long-lasting desynchronizing effect. To determine whether engagement-related state changes depended on the relevant sensory modality, we trained mice on visual and auditory tasks and found that in both cases desynchronization was global, including regions such as somatomotor cortex. We conclude that variations in low-frequency fluctuations are predominately global and related to task engagement.
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Affiliation(s)
- Elina A K Jacobs
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
| | - Nicholas A Steinmetz
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
| | - Andrew J Peters
- UCL Institute of Ophthalmology, University College London, Bath Street, London EC1V 9EL, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, Bath Street, London EC1V 9EL, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
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27
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Semedo JD, Gokcen E, Machens CK, Kohn A, Yu BM. Statistical methods for dissecting interactions between brain areas. Curr Opin Neurobiol 2020; 65:59-69. [PMID: 33142111 PMCID: PMC7935404 DOI: 10.1016/j.conb.2020.09.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 12/12/2022]
Abstract
The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of inter-areal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions.
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Affiliation(s)
- João D Semedo
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Evren Gokcen
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Christian K Machens
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Adam Kohn
- Dominick 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; Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Byron M Yu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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28
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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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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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Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields. J Neurosci 2020; 40:9634-9649. [PMID: 33168622 PMCID: PMC7726533 DOI: 10.1523/jneurosci.0276-20.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 07/06/2020] [Accepted: 07/12/2020] [Indexed: 12/18/2022] Open
Abstract
Cortical areas comprise multiple types of inhibitory interneurons, with stereotypical connectivity motifs that may follow specific plasticity rules. Yet, their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyze the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Synapses from each inhibitory population change according to distinct plasticity rules. We tested different combinations of three rules: Hebbian, anti-Hebbian, and homeostatic scaling. Depending on the inhibitory plasticity rule, synapses become unspecific (flat), anticorrelated to, or correlated with excitatory synapses. Crucially, the neuron's receptive field (i.e., its response to presynaptic stimuli) depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or nonpreferred inputs, in line with recent experimental findings that show dramatic context-dependent changes of neurons' receptive fields. We thus confirm that a neuron's receptive field does not follow directly from the weight profiles of its presynaptic afferents. Our results show how plasticity rules in various cell types can interact to shape cortical circuit motifs and their dynamics.SIGNIFICANCE STATEMENT Neurons in sensory areas of the cortex are known to respond to specific features of a given input (e.g., specific sound frequencies), but recent experimental studies show that such responses (i.e., their receptive fields) depend on context. Inspired by the cortical connectivity, we built models of excitatory and inhibitory inputs onto a single neuron, to study how receptive fields may change on short and long time scales. We show how various synaptic plasticity rules allow for the emergence of diverse connectivity profiles and, moreover, how their dynamic interaction creates a mechanism by which postsynaptic responses can quickly change. Our work emphasizes multiple roles of inhibition in cortical processing and provides a first mechanistic model for flexible receptive fields.
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Kohn A, Jasper AI, Semedo JD, Gokcen E, Machens CK, Yu BM. Principles of Corticocortical Communication: Proposed Schemes and Design Considerations. Trends Neurosci 2020; 43:725-737. [PMID: 32771224 PMCID: PMC7484382 DOI: 10.1016/j.tins.2020.07.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 07/01/2020] [Accepted: 07/05/2020] [Indexed: 12/22/2022]
Abstract
Nearly all brain functions involve routing neural activity among a distributed network of areas. Understanding this routing requires more than a description of interareal anatomical connectivity: it requires understanding what controls the flow of signals through interareal circuitry and how this communication might be modulated to allow flexible behavior. Here we review proposals of how communication, particularly between visual cortical areas, is instantiated and modulated, highlighting recent work that offers new perspectives. We suggest transitioning from a focus on assessing changes in the strength of interareal interactions, as often seen in studies of interareal communication, to a broader consideration of how different signaling schemes might contribute to computation. To this end, we discuss a set of features that might be desirable for a communication scheme.
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Affiliation(s)
- Adam Kohn
- Dominik Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, NY, USA; Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, NY, USA; Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, NY, USA.
| | - Anna I Jasper
- Dominik Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, NY, USA
| | - João D Semedo
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Evren Gokcen
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Christian K Machens
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Byron M Yu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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32
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Kreiter AK. Synchrony, flexible network configuration, and linking neural events to behavior. CURRENT OPINION IN PHYSIOLOGY 2020. [DOI: 10.1016/j.cophys.2020.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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33
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Balaguer-Ballester E, Nogueira R, Abofalia JM, Moreno-Bote R, Sanchez-Vives MV. Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions. PLoS Comput Biol 2020; 16:e1007862. [PMID: 32579563 PMCID: PMC7313741 DOI: 10.1371/journal.pcbi.1007862] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 04/09/2020] [Indexed: 12/03/2022] Open
Abstract
Shared neuronal variability has been shown to modulate cognitive processing. However, the relationship between shared variability and behavioral performance is heterogeneous and complex in frontal areas such as the orbitofrontal cortex (OFC). Mounting evidence shows that single-units in OFC encode a detailed cognitive map of task-space events, but the existence of a robust neuronal ensemble coding for the predictability of choice outcome is less established. Here, we hypothesize that the coding of foreseeable outcomes is potentially unclear from the analysis of units activity and their pairwise correlations. However, this code might be established more conclusively when higher-order neuronal interactions are mapped to the choice outcome. As a case study, we investigated the trial-to-trial shared variability of neuronal ensemble activity during a two-choice interval-discrimination task in rodent OFC, specifically designed such that a lose-switch strategy is optimal by repeating the rewarded stimulus in the upcoming trial. Results show that correlations among triplets are higher during correct choices with respect to incorrect ones, and that this is sustained during the entire trial. This effect is not observed for pairwise nor for higher than third-order correlations. This scenario is compatible with constellations of up to three interacting units assembled during trials in which the task is performed correctly. More interestingly, a state-space spanned by such constellations shows that only correct outcome states that can be successfully predicted are robust over 100 trials of the task, and thus they can be accurately decoded. However, both incorrect and unpredictable outcome representations were unstable and thus non-decodeable, due to spurious negative correlations. Our results suggest that predictability of successful outcomes, and hence the optimal behavioral strategy, can be mapped out in OFC ensemble states reliable over trials of the task, and revealed by sufficiency complex neuronal interactions. Neuronal responses can differ substantially during repetitions of the same tasks; however, they are often coordinated (shared) across multiple neighboring neurons. Such correlation between neurons has been related to the capacity of the brain to take decisions, but specifically how this relation is established is still under study. In this work, we address this question by focusing on an intriguing case study, the orbitofrontal cortex, since this brain area has been found in various studies to be useful for decision-making. Here, we question whether orchestrated groups of neurons encode sufficient information for optimizing their decision strategy; that is, whether the outcome of a choice can be predicted or not on the basis of previous experience. We thus designed a decision-making task for a rat in which some of the correct choices can be predicted. We found that only successful outcomes that can actually be predicted were robustly encoded over time. This finding was shown by analyzing sufficiently complex interactions between three neurons, whilst more complex orchestrations did not add further insights. Thus, we propose that coordinated responses of up to three neurons in the OFC could contribute to the capacity of the animal to take the optimal decision.
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Affiliation(s)
- Emili Balaguer-Ballester
- Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom
- Bernstein Center for Computational Neuroscience, Medical Faculty Mannheim and Heidelberg University, Mannheim, Germany
- * E-mail:
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
| | - Juan M. Abofalia
- IDIBAPS (Institut d’Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain
| | - Ruben Moreno-Bote
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Center for Brain and Cognition, Mercé Rodoreda building (Ciutadella campus), Barcelona, Spain
- Serra Húnter Fellow Programme, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria V. Sanchez-Vives
- IDIBAPS (Institut d’Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain
- ICREA (Institució Catalana de Recerca i Estudis Avançats), Barcelona, Spain
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