1
|
Marino PJ, Bahureksa L, Fernández Fisac C, Oby ER, Smoulder AL, Motiwala A, Degenhart AD, Grigsby EM, Joiner WM, Chase SM, Yu BM, Batista AP. A posture subspace in primary motor cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.12.607361. [PMID: 39185208 PMCID: PMC11343157 DOI: 10.1101/2024.08.12.607361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
To generate movements, the brain must combine information about movement goal and body posture. Motor cortex (M1) is a key node for the convergence of these information streams. How are posture and goal information organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture information in M1 than previously recognized. The compartmentalization of posture and goal information might allow the two to be flexibly combined in the service of our broad repertoire of actions.
Collapse
|
2
|
Ferro D, Cash-Padgett T, Wang MZ, Hayden BY, Moreno-Bote R. Gaze-centered gating, reactivation, and reevaluation of economic value in orbitofrontal cortex. Nat Commun 2024; 15:6163. [PMID: 39039055 PMCID: PMC11263430 DOI: 10.1038/s41467-024-50214-2] [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: 05/29/2023] [Accepted: 07/03/2024] [Indexed: 07/24/2024] Open
Abstract
During economic choice, options are often considered in alternation, until commitment. Nonetheless, neuroeconomics typically ignores the dynamic aspects of deliberation. We trained two male macaques to perform a value-based decision-making task in which two risky offers were presented in sequence at the opposite sides of the visual field, each followed by a delay epoch where offers were invisible. Surprisingly, during the two delays, subjects tend to look at empty locations where the offers had previously appeared, with longer fixations increasing the probability of choosing the associated offer. Spiking activity in orbitofrontal cortex reflects the value of the gazed offer, or of the offer associated with the gazed empty spatial location, even if it is not the most recent. This reactivation reflects a reevaluation process, as fluctuations in neural spiking correlate with upcoming choice. Our results suggest that look-at-nothing gazing triggers the reactivation of a previously seen offer for further evaluation.
Collapse
Affiliation(s)
- Demetrio Ferro
- Center for Brain and Cognition, Universitat Pompeu Fabra, 08002, Barcelona, Spain.
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08002, Barcelona, Spain.
| | - Tyler Cash-Padgett
- Department of Neuroscience, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN55455, USA
| | - Maya Zhe Wang
- Department of Neuroscience, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN55455, USA
| | - Benjamin Y Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Rubén Moreno-Bote
- Center for Brain and Cognition, Universitat Pompeu Fabra, 08002, Barcelona, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08002, Barcelona, Spain
- Serra Húnter Fellow Programme, Universitat Pompeu Fabra, Barcelona, Spain
| |
Collapse
|
3
|
Tye KM, Miller EK, Taschbach FH, Benna MK, Rigotti M, Fusi S. Mixed selectivity: Cellular computations for complexity. Neuron 2024; 112:2289-2303. [PMID: 38729151 PMCID: PMC11257803 DOI: 10.1016/j.neuron.2024.04.017] [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: 12/11/2023] [Revised: 03/08/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024]
Abstract
The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.
Collapse
Affiliation(s)
- Kay M Tye
- Salk Institute for Biological Studies, La Jolla, CA, USA; Howard Hughes Medical Institute, La Jolla, CA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Kavli Institute for Brain and Mind, San Diego, CA, USA.
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Felix H Taschbach
- Salk Institute for Biological Studies, La Jolla, CA, USA; Biological Science Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Marcus K Benna
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | | | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
| |
Collapse
|
4
|
Xiao J, Adkinson JA, Allawala AB, Banks G, Bartoli E, Fan X, Mocchi M, Pascuzzi B, Pulapaka S, Franch MC, Mathew SJ, Mathura RK, Myers J, Pirtle V, Provenza NR, Shofty B, Watrous AJ, Pitkow X, Goodman WK, Pouratian N, Sheth S, Bijanki KR, Hayden BY. Insula uses overlapping codes for emotion in self and others. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.596966. [PMID: 38895233 PMCID: PMC11185604 DOI: 10.1101/2024.06.04.596966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
In daily life, we must recognize others' emotions so we can respond appropriately. This ability may rely, at least in part, on neural responses similar to those associated with our own emotions. We hypothesized that the insula, a cortical region near the junction of the temporal, parietal, and frontal lobes, may play a key role in this process. We recorded local field potential (LFP) activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one on identifying facial emotional responses in others. We found matching patterns of gamma- and high-gamma band activity for the two tasks in the insula. Three other regions (MTL, ACC, and OFC) clearly encoded both self- and other-emotions, but used orthogonal activity patterns to do so. These results support the hypothesis that the insula plays a particularly important role in mediating between experienced vs. observed emotions.
Collapse
Affiliation(s)
- Jiayang Xiao
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Joshua A. Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | | | - Garrett Banks
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Xiaoxu Fan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Madaline Mocchi
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Bailey Pascuzzi
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Suhruthaa Pulapaka
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Melissa C. Franch
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Sanjay J. Mathew
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, 77030
| | - Raissa K. Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - John Myers
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Victoria Pirtle
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Ben Shofty
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Xaq Pitkow
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Wayne K. Goodman
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, 77030
| | - Nader Pouratian
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, 75390
| | - Sameer Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Benjamin Y. Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| |
Collapse
|
5
|
Fine JM, Yoo SBM, Hayden BY. Control over a mixture of policies determines change of mind topology during continuous choice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590154. [PMID: 38712284 PMCID: PMC11071291 DOI: 10.1101/2024.04.18.590154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Behavior is naturally organized into categorically distinct states with corresponding patterns of neural activity; how does the brain control those states? We propose that states are regulated by specific neural processes that implement meta-control that can blend simpler control processes. To test this hypothesis, we recorded from neurons in the dorsal anterior cingulate cortex (dACC) and dorsal premotor cortex (PMd) while macaques performed a continuous pursuit task with two moving prey that followed evasive strategies. We used a novel control theoretic approach to infer subjects' moment-to-moment latent control variables, which in turn dictated their blend of distinct identifiable control processes. We identified low-dimensional subspaces in neuronal responses that reflected the current strategy, the value of the pursued target, and the relative value of the two targets. The top two principal components of activity tracked changes of mind in abstract and change-type-specific formats, respectively. These results indicate that control of behavioral state reflects the interaction of brain processes found in dorsal prefrontal regions that implement a mixture over low-level control policies.
Collapse
|
6
|
Gabriel DB, Havugimana F, Liley AE, Aguilar I, Yeasin M, Simon NW. Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588332. [PMID: 38645204 PMCID: PMC11030364 DOI: 10.1101/2024.04.08.588332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives. Inaccurate risk/reward estimations can engender excessive risk-taking, a central trait in many psychiatric disorders. The lateral orbitofrontal cortex (lOFC) has been linked to many disorders associated with excessively risky behavior and is ideally situated to mediate risky decision-making. Here, we used single-unit electrophysiology to measure neuronal activity from lOFC of freely moving rats performing in a punishment-based risky decision-making task. Subjects chose between a small, safe reward and a large reward associated with either 0% or 50% risk of concurrent punishment. lOFC activity repeatedly encoded current risk in the environment throughout the decision-making sequence, signaling risk before, during, and after a choice. In addition, lOFC encoded reward magnitude, although this information was only evident during action selection. A Random Forest classifier successfully used neural data accurately to predict the risk of punishment in any given trial, and the ability to predict choice via lOFC activity differentiated between and risk-preferring and risk-averse rats. Finally, risk preferring subjects demonstrated reduced lOFC encoding of risk and increased encoding of reward magnitude. These findings suggest lOFC may serve as a central decision-making hub in which external, environmental information converges with internal, subjective information to guide decision-making in the face of punishment risk.
Collapse
Affiliation(s)
- Daniel B.K. Gabriel
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202
| | - Felix Havugimana
- Department of Computer Engineering, University of Memphis, Memphis, TN, 38152
| | - Anna E. Liley
- Institut du Cerveau/Paris Brain Institute, Paris, France, 75013
| | - Ivan Aguilar
- Department of Psychology, University of Memphis, Memphis, TN, 38152
| | - Mohammed Yeasin
- Department of Computer Engineering, University of Memphis, Memphis, TN, 38152
| | | |
Collapse
|
7
|
Tlaie A, Shapcott K, van der Plas TL, Rowland J, Lees R, Keeling J, Packer A, Tiesinga P, Schölvinck ML, Havenith MN. What does the mean mean? A simple test for neuroscience. PLoS Comput Biol 2024; 20:e1012000. [PMID: 38640119 PMCID: PMC11062559 DOI: 10.1371/journal.pcbi.1012000] [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: 10/03/2023] [Revised: 05/01/2024] [Accepted: 03/12/2024] [Indexed: 04/21/2024] Open
Abstract
Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.
Collapse
Affiliation(s)
- Alejandro Tlaie
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
- Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain
| | | | - Thijs L. van der Plas
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - James Rowland
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Robert Lees
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Joshua Keeling
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Adam Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Paul Tiesinga
- Department of Neuroinformatics, Donders Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Martha N. Havenith
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
8
|
Churchland MM, Shenoy KV. Preparatory activity and the expansive null-space. Nat Rev Neurosci 2024; 25:213-236. [PMID: 38443626 DOI: 10.1038/s41583-024-00796-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.
Collapse
Affiliation(s)
- Mark M Churchland
- Department of Neuroscience, 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, New York, NY, USA.
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| |
Collapse
|
9
|
Grabenhorst F, Ponce-Alvarez A, Battaglia-Mayer A, Deco G, Schultz W. A view-based decision mechanism for rewards in the primate amygdala. Neuron 2023; 111:3871-3884.e14. [PMID: 37725980 PMCID: PMC10914681 DOI: 10.1016/j.neuron.2023.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 07/12/2023] [Accepted: 08/23/2023] [Indexed: 09/21/2023]
Abstract
Primates make decisions visually by shifting their view from one object to the next, comparing values between objects, and choosing the best reward, even before acting. Here, we show that when monkeys make value-guided choices, amygdala neurons encode their decisions in an abstract, purely internal representation defined by the monkey's current view but not by specific object or reward properties. Across amygdala subdivisions, recorded activity patterns evolved gradually from an object-specific value code to a transient, object-independent code in which currently viewed and last-viewed objects competed to reflect the emerging view-based choice. Using neural-network modeling, we identified a sequence of computations by which amygdala neurons implemented view-based decision making and eventually recovered the chosen object's identity when the monkeys acted on their choice. These findings reveal a neural mechanism in the amygdala that derives object choices from abstract, view-based computations, suggesting an efficient solution for decision problems with many objects.
Collapse
Affiliation(s)
- Fabian Grabenhorst
- Department of Experimental Psychology, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK.
| | - Adrián Ponce-Alvarez
- Center for Brain and Cognition, Department of Technology and Information, Universitat Pompeu Fabra, Carrer Ramón Trias Fargas, 25-27, 08005 Barcelona, Spain; Departament de Matemàtiques, EPSEB, Universitat Politècnica de Catalunya, Barcelona, 08028 Barcelona, Spain
| | | | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, Universitat Pompeu Fabra, Carrer Ramón Trias Fargas, 25-27, 08005 Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats, Universitat Barcelona, Passeig Lluís Companys 23, 08010 Barcelona, Spain
| | - Wolfram Schultz
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| |
Collapse
|
10
|
Gurnani H, Cayco Gajic NA. Signatures of task learning in neural representations. Curr Opin Neurobiol 2023; 83:102759. [PMID: 37708653 DOI: 10.1016/j.conb.2023.102759] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/28/2023] [Accepted: 07/20/2023] [Indexed: 09/16/2023]
Abstract
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level.
Collapse
Affiliation(s)
- Harsha Gurnani
- Department of Biology, University of Washington, Seattle, WA, USA. https://twitter.com/HarshaGurnani
| | - N Alex Cayco Gajic
- Laboratoire de Neuroscience Cognitives, Ecole Normale Supérieure, Université PSL, Paris, France.
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Herman AB, Smith EH, Schevon CA, Yates MJ, McKhann GM, Botvinick M, Hayden BY, Sheth SA. Pretrial predictors of conflict response efficacy in the human prefrontal cortex. iScience 2023; 26:108047. [PMID: 37867949 PMCID: PMC10589857 DOI: 10.1016/j.isci.2023.108047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 07/14/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023] Open
Abstract
The ability to perform motor actions depends, in part, on the brain's initial state. We hypothesized that initial state dependence is a more general principle and applies to cognitive control. To test this idea, we examined human single units recorded from the dorsolateral prefrontal (dlPFC) cortex and dorsal anterior cingulate cortex (dACC) during a task that interleaves motor and perceptual conflict trials, the multisource interference task (MSIT). In both brain regions, variability in pre-trial firing rates predicted subsequent reaction time (RT) on conflict trials. In dlPFC, ensemble firing rate patterns suggested the existence of domain-specific initial states, while in dACC, firing patterns were more consistent with a domain-general initial state. The deployment of shared and independent factors that we observe for conflict resolution may allow for flexible and fast responses mediated by cognitive initial states. These results also support hypotheses that place dACC hierarchically earlier than dlPFC in proactive control.
Collapse
Affiliation(s)
- Alexander B. Herman
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elliot H. Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA
- Department of Neurology, Columbia University, NYC, NY 10027, USA
| | | | - Mark J. Yates
- Department of Neurological surgery, Columbia University, NYC, NY 10027, USA
| | - Guy M. McKhann
- Department of Neurological surgery, Columbia University, NYC, NY 10027, USA
| | | | - Benjamin Y. Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, and Center for Neural Engineering, University of Minnesota, Minneapolis, MN 55455, USA
- McNair Medical Institute, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
- McNair Medical Institute, Baylor College of Medicine, Houston, TX 77030, USA
| |
Collapse
|
13
|
Barbosa J, Proville R, Rodgers CC, DeWeese MR, Ostojic S, Boubenec Y. Early selection of task-relevant features through population gating. Nat Commun 2023; 14:6837. [PMID: 37884507 PMCID: PMC10603060 DOI: 10.1038/s41467-023-42519-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.
Collapse
Affiliation(s)
- Joao Barbosa
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France.
| | - Rémi Proville
- Tailored Data Solutions, 192 Cours Gambetta, 84300, Cavaillon, France
| | - Chris C Rodgers
- Department of Neurosurgery, Emory University, Atlanta, GA, 30033, USA
| | - Michael R DeWeese
- Department of Physics, Helen Wills Neuroscience Institute, and Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France
| | - Yves Boubenec
- Laboratoire des Systèmes Perceptifs, Département d'Études Cognitives, École Normale Supérieure PSL Research University, CNRS, Paris, France
| |
Collapse
|
14
|
Johnston WJ, Fine JM, Yoo SBM, Ebitz RB, Hayden BY. Semi-orthogonal subspaces for value mediate a tradeoff between binding and generalization. ARXIV 2023:arXiv:2309.07766v1. [PMID: 37744462 PMCID: PMC10516109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
When choosing between options, we must associate their values with the action needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. To test this hypothesis, we examined neuronal responses in five reward-sensitive regions in macaques performing a risky choice task with sequential offers. Surprisingly, in all areas, the neural population encoded the values of offers presented on the left and right in distinct subspaces. We show that the encoding we observe is sufficient to bind the values of the offers to their respective positions in space while preserving abstract value information, which may be important for rapid learning and generalization to novel contexts. Moreover, after both offers have been presented, all areas encode the value of the first and second offers in orthogonal subspaces. In this case as well, the orthogonalization provides binding. Our binding-by-subspace hypothesis makes two novel predictions borne out by the data. First, behavioral errors should correlate with putative spatial (but not temporal) misbinding in the neural representation. Second, the specific representational geometry that we observe across animals also indicates that behavioral errors should increase when offers have low or high values, compared to when they have medium values, even when controlling for value difference. Together, these results support the idea that the brain makes use of semi-orthogonal subspaces to bind features together.
Collapse
Affiliation(s)
- W. Jeffrey Johnston
- Center for Theoretical Neuroscience and Mortimer B. Zuckerman Mind, Brain, and Behavior Institute, Columbia University, New York, New York, United States of America
| | - Justin M. Fine
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, United States of America
| | - Seng Bum Michael Yoo
- Department of Biomedical Engineering, Sunkyunkwan University, and Center for Neuroscience Imaging Research, Institute of Basic Sciences, Suwon, South Korea, Republic of Korea, 16419
| | - R. Becket Ebitz
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
| | - Benjamin Y. Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, United States of America
| |
Collapse
|
15
|
Maisson DJN, Cervera RL, Voloh B, Conover I, Zambre M, Zimmermann J, Hayden BY. Widespread coding of navigational variables in prefrontal cortex. Curr Biol 2023; 33:3478-3488.e3. [PMID: 37541250 PMCID: PMC10984098 DOI: 10.1016/j.cub.2023.07.024] [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] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/01/2023] [Accepted: 07/13/2023] [Indexed: 08/06/2023]
Abstract
To navigate effectively, we must represent information about our location in the environment. Traditional research highlights the role of the hippocampal complex in this process. Spurred by recent research highlighting the widespread cortical encoding of cognitive and motor variables previously thought to have localized function, we hypothesized that navigational variables would be likewise encoded widely, especially in the prefrontal cortex, which is associated with volitional behavior. We recorded neural activity from six prefrontal regions while macaques performed a foraging task in an open enclosure. In all regions, we found strong encoding of allocentric position, allocentric head direction, boundary distance, and linear and angular velocity. These encodings were not accounted for by distance, time to reward, or motor factors. The strength of coding of all variables increased along a ventral-to-dorsal gradient. Together, these results argue that encoding of navigational variables is not localized to the hippocampus and support the hypothesis that navigation is continuous with other forms of flexible cognition in the service of action.
Collapse
Affiliation(s)
- David J-N Maisson
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Roberto Lopez Cervera
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Benjamin Voloh
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Indirah Conover
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mrunal Zambre
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Benjamin Y Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
| |
Collapse
|
16
|
Weber J, Iwama G, Solbakk AK, Blenkmann AO, Larsson PG, Ivanovic J, Knight RT, Endestad T, Helfrich R. Subspace partitioning in the human prefrontal cortex resolves cognitive interference. Proc Natl Acad Sci U S A 2023; 120:e2220523120. [PMID: 37399398 PMCID: PMC10334727 DOI: 10.1073/pnas.2220523120] [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: 12/02/2022] [Accepted: 05/31/2023] [Indexed: 07/05/2023] Open
Abstract
The human prefrontal cortex (PFC) constitutes the structural basis underlying flexible cognitive control, where mixed-selective neural populations encode multiple task features to guide subsequent behavior. The mechanisms by which the brain simultaneously encodes multiple task-relevant variables while minimizing interference from task-irrelevant features remain unknown. Leveraging intracranial recordings from the human PFC, we first demonstrate that competition between coexisting representations of past and present task variables incurs a behavioral switch cost. Our results reveal that this interference between past and present states in the PFC is resolved through coding partitioning into distinct low-dimensional neural states; thereby strongly attenuating behavioral switch costs. In sum, these findings uncover a fundamental coding mechanism that constitutes a central building block of flexible cognitive control.
Collapse
Affiliation(s)
- Jan Weber
- Hertie Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, 72076Tübingen, Germany
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, 72076Tübingen, Germany
| | - Gabriela Iwama
- Hertie Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, 72076Tübingen, Germany
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, 72076Tübingen, Germany
| | - Anne-Kristin Solbakk
- Department of Psychology, University of Oslo, 0373Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, 0373Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, 0372Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, 8657Mosjøen, Norway
| | - Alejandro O. Blenkmann
- Department of Psychology, University of Oslo, 0373Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, 0373Oslo, Norway
| | - Pal G. Larsson
- Department of Neurosurgery, Oslo University Hospital, 0372Oslo, Norway
| | - Jugoslav Ivanovic
- Department of Neurosurgery, Oslo University Hospital, 0372Oslo, Norway
| | - Robert T. Knight
- Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA94720
- Department of Psychology, UC Berkeley, Berkeley, CA94720
| | - Tor Endestad
- Department of Psychology, University of Oslo, 0373Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, 0373Oslo, Norway
| | - Randolph Helfrich
- Hertie Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, 72076Tübingen, Germany
| |
Collapse
|
17
|
Fine JM, Maisson DJN, Yoo SBM, Cash-Padgett TV, Wang MZ, Zimmermann J, Hayden BY. Abstract Value Encoding in Neural Populations But Not Single Neurons. J Neurosci 2023; 43:4650-4663. [PMID: 37208178 PMCID: PMC10286943 DOI: 10.1523/jneurosci.1954-22.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 05/07/2023] [Accepted: 05/11/2023] [Indexed: 05/21/2023] Open
Abstract
An important open question in neuroeconomics is how the brain represents the value of offers in a way that is both abstract (allowing for comparison) and concrete (preserving the details of the factors that influence value). Here, we examine neuronal responses to risky and safe options in five brain regions that putatively encode value in male macaques. Surprisingly, we find no detectable overlap in the neural codes used for risky and safe options, even when the options have identical subjective values (as revealed by preference) in any of the regions. Indeed, responses are weakly correlated and occupy distinct (semi-orthogonal) encoding subspaces. Notably, however, these subspaces are linked through a linear transform of their constituent encodings, a property that allows for comparison of dissimilar option types. This encoding scheme allows these regions to multiplex decision related processes: they can encode the detailed factors that influence offer value (here, risky and safety) but also directly compare dissimilar offer types. Together these results suggest a neuronal basis for the qualitatively different psychological properties of risky and safe options and highlight the power of population geometry to resolve outstanding problems in neural coding.SIGNIFICANCE STATEMENT To make economic choices, we must have some mechanism for comparing dissimilar offers. We propose that the brain uses distinct neural codes for risky and safe offers, but that these codes are linearly transformable. This encoding scheme has the dual advantage of allowing for comparison across offer types while preserving information about offer type, which in turn allows for flexibility in changing circumstances. We show that responses to risky and safe offers exhibit these predicted properties in five different reward-sensitive regions. Together, these results highlight the power of population coding principles for solving representation problems in economic choice.
Collapse
Affiliation(s)
- Justin M Fine
- Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - David J-N Maisson
- Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - Seng Bum Michael Yoo
- Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - Tyler V Cash-Padgett
- Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - Maya Zhe Wang
- Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - Jan Zimmermann
- Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - Benjamin Y Hayden
- Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| |
Collapse
|
18
|
Libedinsky C. Comparing representations and computations in single neurons versus neural networks. Trends Cogn Sci 2023; 27:517-527. [PMID: 37005114 DOI: 10.1016/j.tics.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 04/03/2023]
Abstract
Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.
Collapse
|
19
|
Johnston WJ, Fine JM, Yoo SBM, Ebitz RB, Hayden BY. Subspace orthogonalization as a mechanism for binding values to space. ARXIV 2023:arXiv:2205.06769v2. [PMID: 36776821 PMCID: PMC9915762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
When choosing between options, we must solve an important binding problem. The values of the options must be associated with information about the action needed to select them. We hypothesize that the brain solves this binding problem through use of distinct population subspaces. To test this hypothesis, we examined the responses of single neurons in five reward-sensitive regions in rhesus macaques performing a risky choice task. In all areas, neurons encoded the value of the offers presented on both the left and the right side of the display in semi-orthogonal subspaces, which served to bind the values of the two offers to their positions in space. Supporting the idea that this orthogonalization is functionally meaningful, we observed a session-to-session covariation between choice behavior and the orthogonalization of the two value subspaces: trials with less orthogonalized subspaces were associated with greater likelihood of choosing the less valued option. Further inspection revealed that these semi-orthogonal subspaces arose from a combination of linear and nonlinear mixed selectivity in the neural population. We show this combination of selectivity balances reliable binding with an ability to generalize value across different spatial locations. These results support the hypothesis that semi-orthogonal subspaces support reliable binding, which is essential to flexible behavior in the face of multiple options.
Collapse
Affiliation(s)
- W. Jeffrey Johnston
- Center for Theoretical Neuroscience and Mortimer B. Zuckerman Mind, Brain, and Behavior Institute, Columbia University, New York, New York
| | - Justin M. Fine
- Department of Neuroscience, Center for Magnetic Resonance Research, and Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455
| | - Seng Bum Michael Yoo
- Department of Biomedical Engineering, Sunkyunkwan University, and Center for Neuroscience Imaging Research, Institute of Basic Sciences, Suwon, South Korea, Republic of Korea, 16419
- Current address: Department of Brain and Cognitive Sciences, Massachusetts Institution of Technology, Cambridge, Massachusetts, MA, 02139
| | - R. Becket Ebitz
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
| | - Benjamin Y. Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, and Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455
| |
Collapse
|
20
|
Poh JH, Vu MAT, Stanek JK, Hsiung A, Egner T, Adcock RA. Hippocampal convergence during anticipatory midbrain activation promotes subsequent memory formation. Nat Commun 2022; 13:6729. [PMID: 36344524 PMCID: PMC9640528 DOI: 10.1038/s41467-022-34459-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/25/2022] [Indexed: 11/09/2022] Open
Abstract
The hippocampus has been a focus of memory research since H.M's surgery abolished his ability to form new memories, yet its mechanistic role in memory remains debated. Here, we identify a candidate memory mechanism: an anticipatory hippocampal "convergence state", observed while awaiting valuable information, and which predicts subsequent learning. During fMRI, participants viewed trivia questions eliciting high or low curiosity, followed seconds later by its answer. We reasoned that encoding success requires a confluence of conditions, so that hippocampal states more conducive to memory formation should converge in state space. To operationalize convergence of neural states, we quantified the typicality of multivoxel patterns in the medial temporal lobes during anticipation and encoding of trivia answers. We found that the typicality of anticipatory hippocampal patterns increased during high curiosity. Crucially, anticipatory hippocampal pattern typicality increased with dopaminergic midbrain activation and uniquely accounted for the association between midbrain activation and subsequent recall. We propose that hippocampal convergence states may complete a cascade from motivation and midbrain activation to memory enhancement, and may be a general predictor of memory formation.
Collapse
Affiliation(s)
- Jia-Hou Poh
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
| | - Mai-Anh T Vu
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - Jessica K Stanek
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Abigail Hsiung
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Tobias Egner
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - R Alison Adcock
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
- Department of Neurobiology, Duke University, Durham, NC, USA.
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA.
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA.
| |
Collapse
|
21
|
A neuronal prospect theory model in the brain reward circuitry. Nat Commun 2022; 13:5855. [PMID: 36195765 PMCID: PMC9532451 DOI: 10.1038/s41467-022-33579-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 09/22/2022] [Indexed: 11/23/2022] Open
Abstract
Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, obeys prospect theory remains unknown. Here, we show, with theoretical accuracy equivalent to that of human neuroimaging studies, that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards parameterized as a multiplicative combination of utility and probability weighting functions, as in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reconstructed the risk preferences and subjective probability weighting revealed by the animals’ choices. Thus, distributed neural coding explains the computation of subjective valuations under risk. It is unclear how the activity of individual neurons conform to prospect theory. Here, the authors demonstrate that the activity of single neurons in various reward-related regions in the monkey brain can be described as encoding a multiplicative combination of utility and probability weighting, and that this subjective valuation process is achieved via a distributed coding scheme.
Collapse
|
22
|
Inagaki HK, Chen S, Daie K, Finkelstein A, Fontolan L, Romani S, Svoboda K. Neural Algorithms and Circuits for Motor Planning. Annu Rev Neurosci 2022; 45:249-271. [PMID: 35316610 DOI: 10.1146/annurev-neuro-092021-121730] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.
Collapse
Affiliation(s)
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Kayvon Daie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
| | - Arseny Finkelstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Lorenzo Fontolan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
| |
Collapse
|
23
|
Wang XJ. Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition. Annu Rev Neurosci 2022; 45:533-560. [PMID: 35803587 DOI: 10.1146/annurev-neuro-110920-035434] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.
Collapse
Affiliation(s)
- Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA;
| |
Collapse
|
24
|
Wang MZ, Hayden BY, Heilbronner SR. A structural and functional subdivision in central orbitofrontal cortex. Nat Commun 2022; 13:3623. [PMID: 35750659 PMCID: PMC9232485 DOI: 10.1038/s41467-022-31273-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 06/07/2022] [Indexed: 11/09/2022] Open
Abstract
Economic choice requires many cognitive subprocesses, including stimulus detection, valuation, motor output, and outcome monitoring; many of these subprocesses are associated with the central orbitofrontal cortex (cOFC). Prior work has largely assumed that the cOFC is a single region with a single function. Here, we challenge that unified view with convergent anatomical and physiological results from rhesus macaques. Anatomically, we show that the cOFC can be subdivided according to its much stronger (medial) or weaker (lateral) bidirectional anatomical connectivity with the posterior cingulate cortex (PCC). We call these subregions cOFCm and cOFCl, respectively. These two subregions have notable functional differences. Specifically, cOFCm shows enhanced functional connectivity with PCC, as indicated by both spike-field coherence and mutual information. The cOFCm-PCC circuit, but not the cOFCl-PCC circuit, shows signatures of relaying choice signals from a non-spatial comparison framework to a spatially framed organization and shows a putative bidirectional mutually excitatory pattern.
Collapse
Affiliation(s)
- Maya Zhe Wang
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA.
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| |
Collapse
|
25
|
Thivierge JP, Pilzak A. Estimating null and potent modes of feedforward communication in a computational model of cortical activity. Sci Rep 2022; 12:742. [PMID: 35031628 PMCID: PMC8760251 DOI: 10.1038/s41598-021-04684-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 12/15/2021] [Indexed: 11/08/2022] Open
Abstract
Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, pairwise correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.
Collapse
Affiliation(s)
- Jean-Philippe Thivierge
- School of Psychology, University of Ottawa, Ottawa, ON, Canada.
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada.
| | - Artem Pilzak
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| |
Collapse
|
26
|
Weissbourd B, Momose T, Nair A, Kennedy A, Hunt B, Anderson DJ. A genetically tractable jellyfish model for systems and evolutionary neuroscience. Cell 2021; 184:5854-5868.e20. [PMID: 34822783 PMCID: PMC8629132 DOI: 10.1016/j.cell.2021.10.021] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 08/30/2021] [Accepted: 10/19/2021] [Indexed: 11/22/2022]
Abstract
Jellyfish are radially symmetric organisms without a brain that arose more than 500 million years ago. They achieve organismal behaviors through coordinated interactions between autonomously functioning body parts. Jellyfish neurons have been studied electrophysiologically, but not at the systems level. We introduce Clytia hemisphaerica as a transparent, genetically tractable jellyfish model for systems and evolutionary neuroscience. We generate stable F1 transgenic lines for cell-type-specific conditional ablation and whole-organism GCaMP imaging. Using these tools and computational analyses, we find that an apparently diffuse network of RFamide-expressing umbrellar neurons is functionally subdivided into a series of spatially localized subassemblies whose synchronous activation controls directional food transfer from the tentacles to the mouth. These data reveal an unanticipated degree of structured neural organization in this species. Clytia affords a platform for systems-level studies of neural function, behavior, and evolution within a clade of marine organisms with growing ecological and economic importance.
Collapse
Affiliation(s)
- Brandon Weissbourd
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Tsuyoshi Momose
- Sorbonne Université, CNRS, Laboratoire de Biologie du Développement de Villefranche-sur-Mer (LBDV), 06230 Villefranche-sur-Mer, France
| | - Aditya Nair
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ann Kennedy
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA
| | - Bridgett Hunt
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA
| | - David J Anderson
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA.
| |
Collapse
|
27
|
Ebitz RB, Hayden BY. The population doctrine in cognitive neuroscience. Neuron 2021; 109:3055-3068. [PMID: 34416170 PMCID: PMC8725976 DOI: 10.1016/j.neuron.2021.07.011] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/02/2021] [Accepted: 07/13/2021] [Indexed: 01/08/2023]
Abstract
A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population-level thinking holds for cognitive neuroscience-for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing.
Collapse
Affiliation(s)
- R Becket Ebitz
- Department of Neurosciences, Faculté de médecine, Université de Montréal, Montréal, QC, Canada.
| | - Benjamin Y Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, and Center for Neuroengineering, University of Minnesota, Minneapolis, MN, USA
| |
Collapse
|
28
|
Hunt LT. Frontal circuit specialisations for decision making. Eur J Neurosci 2021; 53:3654-3671. [PMID: 33864305 DOI: 10.1111/ejn.15236] [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: 01/16/2021] [Revised: 03/15/2021] [Accepted: 04/04/2021] [Indexed: 11/29/2022]
Abstract
There is widespread consensus that distributed circuits across prefrontal and anterior cingulate cortex (PFC/ACC) are critical for reward-based decision making. The circuit specialisations of these areas in primates were likely shaped by their foraging niche, in which decision making is typically sequential, attention-guided and temporally extended. Here, I argue that in humans and other primates, PFC/ACC circuits are functionally specialised in two ways. First, microcircuits found across PFC/ACC are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. These properties provide the basis of a computational account of time-varying neural activity within PFC/ACC as a decision is being made. Second, the macrocircuit connections (to other brain areas) differ between distinct PFC/ACC cytoarchitectonic subregions. This variation in macrocircuit connections explains why PFC/ACC subregions make unique contributions to reward-based decision tasks and how these contributions are shaped by attention. They predict dissociable neural representations to emerge in orbitofrontal, anterior cingulate and dorsolateral prefrontal cortex during sequential attention-guided choice, as recently confirmed in neurophysiological recordings.
Collapse
Affiliation(s)
- Laurence T Hunt
- Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| |
Collapse
|
29
|
Azab H, Hayden BY. Partial integration of the components of value in anterior cingulate cortex. Behav Neurosci 2021; 134:296-308. [PMID: 32658523 DOI: 10.1037/bne0000382] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Evaluation often involves integrating multiple determinants of value, such as the different possible outcomes in risky choice. A brain region can be placed either before or after a presumed evaluation stage by measuring how responses of its neurons depend on multiple determinants of value. A brain region could also, in principle, show partial integration, which would indicate that it occupies a middle position between (preevaluative) nonintegration and (postevaluative) full integration. Existing mathematical techniques cannot distinguish full from partial integration and therefore risk misidentifying regional function. Here we use a new Bayesian regression-based approach to analyze responses of neurons in dorsal anterior cingulate cortex (dACC) to risky offers. We find that dACC neurons only partially integrate across outcome dimensions, indicating that dACC cannot be assigned to either a pre- or postevaluative position. Neurons in dACC also show putative signatures of value comparison, thereby demonstrating that comparison does not require complete evaluation before proceeding. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Collapse
Affiliation(s)
- Habiba Azab
- Department of Neuroscience, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Twin Cities
| | - Benjamin Y Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Twin Cities
| |
Collapse
|
30
|
Thornquist SC, Pitsch MJ, Auth CS, Crickmore MA. Biochemical evidence accumulates across neurons to drive a network-level eruption. Mol Cell 2021; 81:675-690.e8. [PMID: 33453167 PMCID: PMC7924971 DOI: 10.1016/j.molcel.2020.12.029] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 11/05/2020] [Accepted: 12/15/2020] [Indexed: 11/19/2022]
Abstract
Neural network computations are usually assumed to emerge from patterns of fast electrical activity. Challenging this view, we show that a male fly's decision to persist in mating hinges on a biochemical computation that enables processing over minutes to hours. Each neuron in a recurrent network contains slightly different internal molecular estimates of mating progress. Protein kinase A (PKA) activity contrasts this internal measurement with input from the other neurons to represent accumulated evidence that the goal of the network has been achieved. When consensus is reached, PKA pushes the network toward a large-scale and synchronized burst of calcium influx that we call an eruption. Eruptions transform continuous deliberation within the network into an all-or-nothing output, after which the male will no longer sacrifice his life to continue mating. Here, biochemical activity, invisible to most large-scale recording techniques, is the key computational currency directing behavior and motivational state.
Collapse
Affiliation(s)
- Stephen C Thornquist
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Maximilian J Pitsch
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Charlotte S Auth
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Michael A Crickmore
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
| |
Collapse
|
31
|
Neural Population Dynamics Underlying Expected Value Computation. J Neurosci 2021; 41:1684-1698. [PMID: 33441432 PMCID: PMC8115883 DOI: 10.1523/jneurosci.1987-20.2020] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/12/2020] [Accepted: 12/20/2020] [Indexed: 11/22/2022] Open
Abstract
Computation of expected values (i.e., probability × magnitude) seems to be a dynamic integrative process performed by the brain for efficient economic behavior. However, neural dynamics underlying this computation is largely unknown. Using lottery tasks in monkeys (Macaca mulatta, male; Macaca fuscata, female), we examined (1) whether four core reward-related brain regions detect and integrate probability and magnitude cued by numerical symbols and (2) whether these brain regions have distinct dynamics in the integrative process. Extraction of the mechanistic structure of neural population signals demonstrated that expected value signals simultaneously arose in the central orbitofrontal cortex (cOFC; medial part of area 13) and ventral striatum (VS). Moreover, these signals were incredibly stable compared with weak and/or fluctuating signals in the dorsal striatum and medial OFC. Temporal dynamics of these stable expected value signals were unambiguously distinct: sharp and gradual signal evolutions in the cOFC and VS, respectively. These intimate dynamics suggest that the cOFC and VS compute the expected values with unique time constants, as distinct, partially overlapping processes. SIGNIFICANCE STATEMENT Our results differ from those of earlier studies suggesting that many reward-related regions in the brain signal probability and/or magnitude and provide a mechanistic structure for expected value computation employed in multiple neural populations. A central part of the orbitofrontal cortex (cOFC) and ventral striatum (VS) can simultaneously detect and integrate probability and magnitude into an expected value. Our empirical study on these neural population dynamics raises a possibility that the cOFC and VS cooperate on this computation with unique time constants as distinct, partially overlapping processes.
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Stokes MG, Muhle-Karbe PS, Myers NE. Theoretical distinction between functional states in working memory and their corresponding neural states. VISUAL COGNITION 2020; 28:420-432. [PMID: 33223922 PMCID: PMC7655036 DOI: 10.1080/13506285.2020.1825141] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 09/10/2020] [Indexed: 12/15/2022]
Abstract
Working memory (WM) is important for guiding behaviour, but not always for the next possible action. Here we define a WM item that is currently relevant for guiding behaviour as the functionally "active" item; whereas items maintained in WM, but not immediately relevant to behaviour, are defined as functionally "latent". Traditional neurophysiological theories of WM proposed that content is maintained via persistent neural activity (e.g., stable attractors); however, more recent theories have highlighted the potential role for "activity-silent" mechanisms (e.g., short-term synaptic plasticity). Given these somewhat parallel dichotomies, functionally active and latent cognitive states of WM have been associated with storage based on persistent-activity and activity-silent neural mechanisms, respectively. However, in this article we caution against a one-to-one correspondence between functional and activity states. We argue that the principal theoretical requirement for active and latent WM is that the corresponding neural states play qualitatively different functional roles. We consider a number of candidate solutions, and conclude that the neurophysiological mechanisms for functionally active and latent WM items are theoretically independent of the distinction between persistent activity-based and activity-silent forms of WM storage.
Collapse
Affiliation(s)
- Mark G. Stokes
- Wellcome Centre for Integrative Neuroimaging and Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Paul S. Muhle-Karbe
- Wellcome Centre for Integrative Neuroimaging and Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Nicholas E. Myers
- Wellcome Centre for Integrative Neuroimaging and Department of Experimental Psychology, University of Oxford, Oxford, UK
| |
Collapse
|
34
|
Tang C, Herikstad R, Parthasarathy A, Libedinsky C, Yen SC. Minimally dependent activity subspaces for working memory and motor preparation in the lateral prefrontal cortex. eLife 2020; 9:e58154. [PMID: 32902383 PMCID: PMC7481007 DOI: 10.7554/elife.58154] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 08/21/2020] [Indexed: 12/15/2022] Open
Abstract
The lateral prefrontal cortex is involved in the integration of multiple types of information, including working memory and motor preparation. However, it is not known how downstream regions can extract one type of information without interference from the others present in the network. Here, we show that the lateral prefrontal cortex of non-human primates contains two minimally dependent low-dimensional subspaces: one that encodes working memory information, and another that encodes motor preparation information. These subspaces capture all the information about the target in the delay periods, and the information in both subspaces is reduced in error trials. A single population of neurons with mixed selectivity forms both subspaces, but the information is kept largely independent from each other. A bump attractor model with divisive normalization replicates the properties of the neural data. These results provide new insights into neural processing in prefrontal regions.
Collapse
Affiliation(s)
- Cheng Tang
- Institute of Molecular and Cell Biology, A*STARSingaporeSingapore
| | - Roger Herikstad
- The N1 Institute for Health, National University of Singapore (NUS)SingaporeSingapore
| | | | - Camilo Libedinsky
- Institute of Molecular and Cell Biology, A*STARSingaporeSingapore
- The N1 Institute for Health, National University of Singapore (NUS)SingaporeSingapore
- Department of Psychology, NUSSingaporeSingapore
| | - Shih-Cheng Yen
- The N1 Institute for Health, National University of Singapore (NUS)SingaporeSingapore
- Innovation and Design Programme, Faculty of Engineering, NUSSingaporeSingapore
| |
Collapse
|
35
|
Gardner MPH, Schoenbaum G. Neuroscience: From Sensory Discrimination to Choice in Gustatory Cortex. Curr Biol 2020; 30:R444-R446. [PMID: 32428476 DOI: 10.1016/j.cub.2020.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Sensory areas have been shown to be influenced by higher-order cognitive processes. Yet how do these top-down processes affect decisions? A recent study has revealed a dynamic evolution of neural activity from sensory discrimination to choice in rodent taste cortex.
Collapse
|