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Linn S, Lawley SD, Karamched BR, Kilpatrick ZP, Josić K. Fast decisions reflect biases; slow decisions do not. Phys Rev E 2024; 110:024305. [PMID: 39295031 DOI: 10.1103/physreve.110.024305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 06/26/2024] [Indexed: 09/21/2024]
Abstract
Decisions are often made by heterogeneous groups of individuals, each with distinct initial biases and access to information of different quality. We show that in groups of independent agents who accumulate evidence the first to decide are those with the strongest initial biases. Their decisions align with their initial bias, regardless of the underlying truth. In contrast, agents who decide last make decisions as if they were initially unbiased and hence make better choices. We obtain asymptotic expressions in the large population limit quantifying how agents' initial inclinations shape early decisions. Our analysis shows how bias, information quality, and decision order interact in nontrivial ways to determine the reliability of decisions in a group.
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Affiliation(s)
| | | | - Bhargav R Karamched
- Department of Mathematics, Florida State University, Tallahassee, Florida 32306, USA
- Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA
- Program in Neuroscience, Florida State University, Tallahassee, Florida 32306, USA
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2
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Grijseels DM, Fairbank DA, Miller CT. A model of marmoset monkey vocal turn-taking. Proc Biol Sci 2024; 291:20240150. [PMID: 38955229 PMCID: PMC11334984 DOI: 10.1098/rspb.2024.0150] [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: 01/29/2024] [Revised: 04/22/2024] [Accepted: 06/06/2024] [Indexed: 07/04/2024] Open
Abstract
Vocal turn-taking has been described in a diversity of species. Yet, a model that is able to capture the various processes underlying this social behaviour across species has not been developed. To this end, here we recorded a large and diverse dataset of marmoset monkey vocal behaviour in social contexts comprising one, two and three callers and developed a model to determine the keystone factors that affect the dynamics of these natural communicative interactions. Notably, marmoset turn-taking did not abide by coupled-oscillator dynamics, but rather call timing was overwhelmingly stochastic in these exchanges. Our features-based model revealed four key factors that encapsulate the majority of patterns evident in the behaviour, ranging from internal processes, such as particular states of the individual driving increased calling, to social context-driven suppression of calling. These findings indicate that marmoset vocal turn-taking is affected by a broader suite of mechanisms than previously considered and that our model provides a predictive framework with which to further explicate this natural behaviour at both the behavioural and neurobiological levels, and for direct comparisons with the analogous behaviour in other species.
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Affiliation(s)
- Dori M. Grijseels
- Cortical Systems and Behavior Lab, University of California San Diego, La Jolla, CA, USA
| | - Daniella A. Fairbank
- Cortical Systems and Behavior Lab, University of California San Diego, La Jolla, CA, USA
| | - Cory T. Miller
- Cortical Systems and Behavior Lab, University of California San Diego, La Jolla, CA, USA
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA
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3
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Xie T, Adamek M, Cho H, Adamo MA, Ritaccio AL, Willie JT, Brunner P, Kubanek J. Graded decisions in the human brain. Nat Commun 2024; 15:4308. [PMID: 38773117 PMCID: PMC11109249 DOI: 10.1038/s41467-024-48342-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: 05/30/2023] [Accepted: 04/26/2024] [Indexed: 05/23/2024] Open
Abstract
Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.
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Affiliation(s)
- Tao Xie
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, 63110, USA
| | - Markus Adamek
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, 63110, USA
| | - Hohyun Cho
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, 63110, USA
| | - Matthew A Adamo
- Department of Neurosurgery, Albany Medical College, Albany, NY, 12208, USA
| | - Anthony L Ritaccio
- Department of Neurology, Albany Medical College, Albany, NY, 12208, USA
- Department of Neurology, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Jon T Willie
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- National Center for Adaptive Neurotechnologies, St. Louis, MO, 63110, USA
| | - Peter Brunner
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- National Center for Adaptive Neurotechnologies, St. Louis, MO, 63110, USA.
- Department of Neurology, Albany Medical College, Albany, NY, 12208, USA.
| | - Jan Kubanek
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
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4
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Hocker D, Constantinople CM, Savin C. Curriculum learning inspired by behavioral shaping trains neural networks to adopt animal-like decision making strategies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.12.575461. [PMID: 38318205 PMCID: PMC10843159 DOI: 10.1101/2024.01.12.575461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Recurrent neural networks (RNN) are ubiquitously used in neuroscience to capture both neural dynamics and behaviors of living systems. However, when it comes to complex cognitive tasks, traditional methods for training RNNs can fall short in capturing crucial aspects of animal behavior. To address this challenge, we take inspiration from a commonly used (though rarely appreciated) approach from the experimental neuroscientist's toolkit: behavioral shaping. Our solution leverages task compositionality and models the animal's relevant learning experiences prior to the task. Taking as target a temporal wagering task previously studied in rats, we designed a pretraining curriculum of simpler cognitive tasks that are prerequisites for performing it well. These pretraining tasks are not just simplified versions of the temporal wagering task, but reflect relevant sub-computations. We show that this approach is required for RNNs to adopt similar strategies as rats, including long-timescale inference of latent states, which conventional pretraining approaches fail to capture. Mechanistically, our pretraining supports the development of key dynamical systems features needed for implementing both inference and value-based decision making. Overall, our approach addresses a gap in neural network model training by incorporating inductive biases of animals, which is important when modeling complex behaviors that rely on computational abilities acquired from past experiences.
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5
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Linn S, Lawley SD, Karamched BR, Kilpatrick ZP, Josić K. Fast decisions reflect biases, slow decisions do not. ARXIV 2024:arXiv:2401.00306v2. [PMID: 38259347 PMCID: PMC10802676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Decisions are often made by heterogeneous groups of individuals, each with distinct initial biases and access to information of different quality. We show that in large groups of independent agents who accumulate evidence the first to decide are those with the strongest initial biases. Their decisions align with their initial bias, regardless of the underlying truth. In contrast, agents who decide last make decisions as if they were initially unbiased, and hence make better choices. We obtain asymptotic expressions in the large population limit that quantify how agents' initial inclinations shape early decisions. Our analysis shows how bias, information quality, and decision order interact in non-trivial ways to determine the reliability of decisions in a group.
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Affiliation(s)
- Samantha Linn
- Department of Mathematics, University of Utah, Salt Lake City, Utah, USA
| | - Sean D. Lawley
- Department of Mathematics, University of Utah, Salt Lake City, Utah, USA
| | - Bhargav R. Karamched
- Department of Mathematics, Florida State University, Tallahassee, Florida 32306, USA
- Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA
- Program in Neuroscience, Florida State University, Tallahassee, Florida 32306, USA
| | - Zachary P. Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado 80309, USA
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas 77004, USA
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77004, USA
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6
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MaBouDi H, Marshall JAR, Dearden N, Barron AB. How honey bees make fast and accurate decisions. eLife 2023; 12:e86176. [PMID: 37365884 PMCID: PMC10299826 DOI: 10.7554/elife.86176] [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: 01/14/2023] [Accepted: 05/24/2023] [Indexed: 06/28/2023] Open
Abstract
Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.
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Affiliation(s)
- HaDi MaBouDi
- Department of Computer Science, University of SheffieldSheffieldUnited Kingdom
- Sheffield Neuroscience Institute, University of SheffieldSheffieldUnited Kingdom
| | - James AR Marshall
- Department of Computer Science, University of SheffieldSheffieldUnited Kingdom
- Sheffield Neuroscience Institute, University of SheffieldSheffieldUnited Kingdom
| | - Neville Dearden
- Department of Computer Science, University of SheffieldSheffieldUnited Kingdom
| | - Andrew B Barron
- Department of Computer Science, University of SheffieldSheffieldUnited Kingdom
- School of Natural Sciences, Macquarie UniversityNorth RydeAustralia
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Akitake B, Douglas HM, LaFosse PK, Beiran M, Deveau CE, O'Rawe J, Li AJ, Ryan LN, Duffy SP, Zhou Z, Deng Y, Rajan K, Histed MH. Amplified cortical neural responses as animals learn to use novel activity patterns. Curr Biol 2023; 33:2163-2174.e4. [PMID: 37148876 DOI: 10.1016/j.cub.2023.04.032] [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: 06/22/2022] [Revised: 02/09/2023] [Accepted: 04/14/2023] [Indexed: 05/08/2023]
Abstract
Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting that the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found that their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task; therefore, these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.
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Affiliation(s)
- Bradley Akitake
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hannah M Douglas
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Paul K LaFosse
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Manuel Beiran
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ciana E Deveau
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jonathan O'Rawe
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Anna J Li
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lauren N Ryan
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Samuel P Duffy
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhishang Zhou
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yanting Deng
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kanaka Rajan
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mark H Histed
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA.
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8
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Ritz H, Frömer R, Shenhav A. Phantom controllers: Misspecified models create the false appearance of adaptive control during value-based choice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524640. [PMID: 36711762 PMCID: PMC9882254 DOI: 10.1101/2023.01.18.524640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Decision scientists have grown increasingly interested in how people adaptively control their decision making. Researchers have demonstrated that parameters governing the accumulation of evidence towards a choice, such as the decision threshold, are shaped by information available prior to or in parallel with one's evaluation of an option set (e.g., recent outcomes or choice conflict). A recent account has taken a bold leap forward in this approach, suggesting that adjustments in decision parameters can be motivated by the value of the options under consideration. This motivated control account predicts that when faced with difficult choices (similarly valued options) under time pressure, people will adaptively lower their decision threshold to ensure that they make a choice in time. This account was supported by drift diffusion modeling of a deadlined choice task, demonstrating that decision thresholds decrease for difficult relative to easy choices. Here, we reanalyze the data from this experiment, and show that evidence for this novel account does not hold up to further scrutiny. Using a more systematic and comprehensive modeling approach, we show that this previously observed threshold adjustment disappears (or even reverses) under a more complete model of the data. Importantly, we further show how this and other apparent evidence for motivated control arises as an artifact of model (mis)specification, where one model's putatively controlled decision process (e.g., value-driven threshold adjustments) can mimic another model's stimulus-driven decision processes (e.g., accumulator competition or collapsing bounds). Collectively, this work reveals crucial insights and constraints in the pursuit of understanding how control guides decision-making, and when it doesn't.
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Affiliation(s)
- H Ritz
- Cognitive, Linguistic, and Psychological Sciences, Brown University
- Carney Institute for Brain Sciences, Brown University
- Princeton Neuroscience Institute, Princeton University
| | - R Frömer
- Cognitive, Linguistic, and Psychological Sciences, Brown University
- Carney Institute for Brain Sciences, Brown University
- School of Psychology, University of Birmingham
- Centre for Human Brain Health, University of Birmingham
| | - A Shenhav
- Cognitive, Linguistic, and Psychological Sciences, Brown University
- Carney Institute for Brain Sciences, Brown University
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9
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Lee JH, Tsunada J, Vijayan S, Cohen YE. Cortical circuit-based lossless neural integrator for perceptual decision-making: A computational modeling study. Front Comput Neurosci 2022; 16:979830. [DOI: 10.3389/fncom.2022.979830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/17/2022] [Indexed: 11/05/2022] Open
Abstract
The intrinsic uncertainty of sensory information (i.e., evidence) does not necessarily deter an observer from making a reliable decision. Indeed, uncertainty can be reduced by integrating (accumulating) incoming sensory evidence. It is widely thought that this accumulation is instantiated via recurrent rate-code neural networks. Yet, these networks do not fully explain important aspects of perceptual decision-making, such as a subject’s ability to retain accumulated evidence during temporal gaps in the sensory evidence. Here, we utilized computational models to show that cortical circuits can switch flexibly between “retention” and “integration” modes during perceptual decision-making. Further, we found that, depending on how the sensory evidence was readout, we could simulate “stepping” and “ramping” activity patterns, which may be analogous to those seen in different studies of decision-making in the primate parietal cortex. This finding may reconcile these previous empirical studies because it suggests these two activity patterns emerge from the same mechanism.
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Vilà-Balló A, De la Cruz-Puebla M, López-Barroso D, Miró J, Sala-Padró J, Cucurell D, Falip M, Rodríguez-Fornells A. Reward-based decision-making in mesial temporal lobe epilepsy patients with unilateral hippocampal sclerosis pre- and post-surgery. Neuroimage Clin 2022; 36:103251. [PMID: 36510413 PMCID: PMC9668642 DOI: 10.1016/j.nicl.2022.103251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Correct functioning of the reward processing system is critical for optimizing decision-making as well as preventing the development of addictions and/or neuropsychiatric symptoms such as depression, apathy, and anhedonia. Consequently, patients with mesial temporal lobe epilepsy due to unilateral hippocampal sclerosis (mTLE-UHS) represent an excellent opportunity to study the brain networks involved in this system. OBJECTIVE The aim of the current study was to evaluate decision-making and the electrophysiological correlates of feedback processing in a sample of mTLE-UHS patients, compared to healthy controls. In addition, we assessed the impact of mesial temporal lobe surgical resection on these processes, as well as general, neuropsychological functioning. METHOD 17 mTLE-UHS patients and 17 matched healthy controls completed: [1] a computerized version of the Game of Dice Task, [2] a Standard Iowa Gambling Task, and [3] a modified ERP version of a probabilistic gambling task coupled with multichannel electroencephalography. Neuropsychological scores were also obtained both pre- and post-surgery. RESULTS Behavioral analyses showed a pattern of increased risk for the mTLE-UHS group in decision-making under ambiguity compared to the control group. A decrease in the amplitude of the Feedback Related Negativity (FRN), a weaker effect of valence on delta power, and a general reduction of delta and theta power in the mTLE-UHS group, as compared to the control group, were also found. The beta-gamma activity associated with the delivery of positive reward was similar in both groups. Behavioral performance and electrophysiological measures did not worsen post-surgery. CONCLUSIONS Patients with mTLE-UHS showed impairments in decision-making under ambiguity, particularly when they had to make decisions based on the outcomes of their choices, but not in decision-making under risk. No group differences were observed in decision-making when feedbacks were random. These results might be explained by the abnormal feedback processing seen in the EEG activity of patients with mTLE-UHS, and by concomitant impairments in working memory, and memory. These impairments may be linked to the disruption of mesial temporal lobe networks. Finally, feedback processing and decision-making under ambiguity were already affected in mTLE-UHS patients pre-surgery and did not show evidence of clear worsening post-surgery.
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Affiliation(s)
- Adrià Vilà-Balló
- Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain,Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain,Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,Headache and Neurological Pain Research Group, Vall d’Hebron Research Institute, Autonomous University of Barcelona, Barcelona, Spain,Department of Psychology, Faculty of Education and Psychology, University of Girona, Girona, Spain,Corresponding authors.
| | - Myriam De la Cruz-Puebla
- Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain,Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,Department of Cellular Biology, Physiology, and Immunology, Neurosciences Institute, Autonomous University of Barcelona, Barcelona, Spain,Department of Equity in Brain Health, Global Brain Health Institute (GBHI), University of California, San Francisco (UCSF), CA, USA,Department of Internal Medicine, Health Sciences Faculty, Technical University of Ambato, Tungurahua, Ecuador,Dept. of Psychobiology and Methodology of Behavioural Sciences, Faculty of Psychology, University of Málaga, Málaga, Spain
| | - Diana López-Barroso
- Cognitive Neurology and Aphasia Unit, Centro de Investigaciones Médico-Sanitarias, University of Málaga, Málaga, Spain,Instituto de Investigación Biomédica de Málaga-IBIMA, Málaga, Spain,Dept. of Psychobiology and Methodology of Behavioural Sciences, Faculty of Psychology, University of Málaga, Málaga, Spain
| | - Júlia Miró
- Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain,Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,Epilepsy Unit, Neurological Service, Neurology and Genetics Group, Neuroscience Program, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain
| | - Jacint Sala-Padró
- Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain,Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,Dept. of Psychobiology and Methodology of Behavioural Sciences, Faculty of Psychology, University of Málaga, Málaga, Spain
| | - David Cucurell
- Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain,Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain,Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Mercè Falip
- Epilepsy Unit, Neurological Service, Neurology and Genetics Group, Neuroscience Program, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain
| | - Antoni Rodríguez-Fornells
- Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain,Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain,Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,Catalan Institution for Research and Advanced Studies, ICREA, Barcelona, Spain
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11
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Torres JJ, Marro J. Physics Clues on the Mind Substrate and Attributes. Front Comput Neurosci 2022; 16:836532. [PMID: 35465268 PMCID: PMC9026167 DOI: 10.3389/fncom.2022.836532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 11/16/2022] Open
Abstract
The last decade has witnessed a remarkable progress in our understanding of the brain. This has mainly been based on the scrutiny and modeling of the transmission of activity among neurons across lively synapses. A main conclusion, thus far, is that essential features of the mind rely on collective phenomena that emerge from a willful interaction of many neurons that, mediating other cells, form a complex network whose details keep constantly adapting to their activity and surroundings. In parallel, theoretical and computational studies developed to understand many natural and artificial complex systems, which have truthfully explained their amazing emergent features and precise the role of the interaction dynamics and other conditions behind the different collective phenomena they happen to display. Focusing on promising ideas that arise when comparing these neurobiology and physics studies, the present perspective article shortly reviews such fascinating scenarios looking for clues about how high-level cognitive processes such as consciousness, intelligence, and identity can emerge. We, thus, show that basic concepts of physics, such as dynamical phases and non-equilibrium phase transitions, become quite relevant to the brain activity while determined by factors at the subcellular, cellular, and network levels. We also show how these transitions depend on details of the processing mechanism of stimuli in a noisy background and, most important, that one may detect them in familiar electroencephalogram (EEG) recordings. Thus, we associate the existence of such phases, which reveal a brain operating at (non-equilibrium) criticality, with the emergence of most interesting phenomena during memory tasks.
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12
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Zemlianova K, Bose A, Rinzel J. A biophysical counting mechanism for keeping time. BIOLOGICAL CYBERNETICS 2022; 116:205-218. [PMID: 35031845 DOI: 10.1007/s00422-021-00915-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/16/2021] [Indexed: 06/14/2023]
Abstract
The ability to estimate and produce appropriately timed responses is central to many behaviors including speaking, dancing, and playing a musical instrument. A classical framework for estimating or producing a time interval is the pacemaker-accumulator model in which pulses of a pacemaker are counted and compared to a stored representation. However, the neural mechanisms for how these pulses are counted remain an open question. The presence of noise and stochasticity further complicates the picture. We present a biophysical model of how to keep count of a pacemaker in the presence of various forms of stochasticity using a system of bistable Wilson-Cowan units asymmetrically connected in a one-dimensional array; all units receive the same input pulses from a central clock but only one unit is active at any point in time. With each pulse from the clock, the position of the activated unit changes thereby encoding the total number of pulses emitted by the clock. This neural architecture maps the counting problem into the spatial domain, which in turn translates count to a time estimate. We further extend the model to a hierarchical structure to be able to robustly achieve higher counts.
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Affiliation(s)
| | - Amitabha Bose
- Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ, USA
| | - John Rinzel
- Center for Neural Science, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
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13
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Howland JG, Ito R, Lapish CC, Villaruel FR. The rodent medial prefrontal cortex and associated circuits in orchestrating adaptive behavior under variable demands. Neurosci Biobehav Rev 2022; 135:104569. [PMID: 35131398 PMCID: PMC9248379 DOI: 10.1016/j.neubiorev.2022.104569] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 12/17/2021] [Accepted: 02/01/2022] [Indexed: 11/28/2022]
Abstract
Emerging evidence implicates rodent medial prefrontal cortex (mPFC) in tasks requiring adaptation of behavior to changing information from external and internal sources. However, the computations within mPFC and subsequent outputs that determine behavior are incompletely understood. We review the involvement of mPFC subregions, and their projections to the striatum and amygdala in two broad types of tasks in rodents: 1) appetitive and aversive Pavlovian and operant conditioning tasks that engage mPFC-striatum and mPFC-amygdala circuits, and 2) foraging-based tasks that require decision making to optimize reward. We find support for region-specific function of the mPFC, with dorsal mPFC and its projections to the dorsomedial striatum supporting action control with higher cognitive demands, and ventral mPFC engagement in translating affective signals into behavior via discrete projections to the ventral striatum and amygdala. However, we also propose that defined mPFC subdivisions operate as a functional continuum rather than segregated functional units, with crosstalk that allows distinct subregion-specific inputs (e.g., internal, affective) to influence adaptive behavior supported by other subregions.
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Affiliation(s)
- John G Howland
- Department of Anatomy, Physiology, and Pharmacology, University of Saskatchewan, Saskatoon, SK, Canada.
| | - Rutsuko Ito
- Department of Psychology, University of Toronto-Scarborough, Toronto, ON, Canada.
| | - Christopher C Lapish
- Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.
| | - Franz R Villaruel
- Department of Psychology, Concordia University, Montreal, QC, Canada.
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14
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Mahajan NR, Mysore SP. Donut-like organization of inhibition underlies categorical neural responses in the midbrain. Nat Commun 2022; 13:1680. [PMID: 35354821 PMCID: PMC8967821 DOI: 10.1038/s41467-022-29318-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 03/10/2022] [Indexed: 11/08/2022] Open
Abstract
Categorical neural responses underlie various forms of selection and decision-making. Such binary-like responses promote robust signaling of the winner in the presence of input ambiguity and neural noise. Here, we show that a 'donut-like' inhibitory mechanism in which each competing option suppresses all options except itself, is highly effective at generating categorical neural responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization invoked frequently in decision-making models. We demonstrate experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is necessary for categorical signaling by it. The functional pattern of neural inhibition in the midbrain forms an exquisitely structured 'multi-holed' donut consistent with this network's combinatorial inhibitory function for stimulus selection. Additionally, modeling reveals a generalizable neural implementation of the donut-like motif for categorical selection. Self-sparing inhibition may, therefore, be a powerful circuit module central to categorization.
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Affiliation(s)
- Nagaraj R Mahajan
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Shreesh P Mysore
- Departments of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
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15
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From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals. PLoS Comput Biol 2021; 17:e1009654. [PMID: 34898604 PMCID: PMC8699619 DOI: 10.1371/journal.pcbi.1009654] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/23/2021] [Accepted: 11/17/2021] [Indexed: 01/30/2023] Open
Abstract
How does the brain process sensory stimuli, and decide whether to initiate locomotor behaviour? To investigate this question we develop two whole body computer models of a tadpole. The "Central Nervous System" (CNS) model uses evidence from whole-cell recording to define 2300 neurons in 12 classes to study how sensory signals from the skin initiate and stop swimming. In response to skin stimulation, it generates realistic sensory pathway spiking and shows how hindbrain sensory memory populations on each side can compete to initiate reticulospinal neuron firing and start swimming. The 3-D "Virtual Tadpole" (VT) biomechanical model with realistic muscle innervation, body flexion, body-water interaction, and movement is then used to evaluate if motor nerve outputs from the CNS model can produce swimming-like movements in a volume of "water". We find that the whole tadpole VT model generates reliable and realistic swimming. Combining these two models opens new perspectives for experiments.
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16
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Ji N, Madan GK, Fabre GI, Dayan A, Baker CM, Kramer TS, Nwabudike I, Flavell SW. A neural circuit for flexible control of persistent behavioral states. eLife 2021; 10:e62889. [PMID: 34792019 PMCID: PMC8660023 DOI: 10.7554/elife.62889] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/17/2021] [Indexed: 11/29/2022] Open
Abstract
To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely moving animals, we identify stereotyped changes in circuit activity corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the neural activity patterns observed in each state. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.
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Affiliation(s)
- Ni Ji
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Gurrein K Madan
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Guadalupe I Fabre
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Alyssa Dayan
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Casey M Baker
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Talya S Kramer
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
- MIT Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, United States
| | - Ijeoma Nwabudike
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
| | - Steven W Flavell
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
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17
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Hein AM, Altshuler DL, Cade DE, Liao JC, Martin BT, Taylor GK. An Algorithmic Approach to Natural Behavior. Curr Biol 2021; 30:R663-R675. [PMID: 32516620 DOI: 10.1016/j.cub.2020.04.018] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Uncovering the mechanisms and implications of natural behavior is a goal that unites many fields of biology. Yet, the diversity, flexibility, and multi-scale nature of these behaviors often make understanding elusive. Here, we review studies of animal pursuit and evasion - two special classes of behavior where theory-driven experiments and new modeling techniques are beginning to uncover the general control principles underlying natural behavior. A key finding of these studies is that intricate sequences of pursuit and evasion behavior can often be constructed through simple, repeatable rules that link sensory input to motor output: we refer to these rules as behavioral algorithms. Identifying and mathematically characterizing these algorithms has led to important insights, including the discovery of guidance rules that attacking predators use to intercept mobile prey, and coordinated neural and biomechanical mechanisms that animals use to avoid impending collisions. Here, we argue that algorithms provide a good starting point for studies of natural behavior more generally. Rather than beginning at the neural or ecological levels of organization, we advocate starting in the middle, where the algorithms that link sensory input to behavioral output can provide a solid foundation from which to explore both the implementation and the ecological outcomes of behavior. We review insights that have been gained through such an algorithmic approach to pursuit and evasion behaviors. From these, we synthesize theoretical principles and lay out key modeling tools needed to apply an algorithmic approach to the study of other complex natural behaviors.
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Affiliation(s)
- Andrew M Hein
- Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Santa Cruz, CA 95060, USA; Institute of Marine Sciences, University of California, Santa Cruz, CA 95060, USA; Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA.
| | - Douglas L Altshuler
- Department of Zoology, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - David E Cade
- Institute of Marine Sciences, University of California, Santa Cruz, CA 95060, USA; Hopkins Marine Station, Department of Biology, Stanford University, Pacific Grove, CA 93950, USA
| | - James C Liao
- The Whitney Laboratory for Marine Bioscience, Department of Biology, University of Florida, 9505 Ocean Shore Blvd., St. Augustine, FL 32080, USA
| | - Benjamin T Martin
- Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Santa Cruz, CA 95060, USA; Institute of Marine Sciences, University of California, Santa Cruz, CA 95060, USA; Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
| | - Graham K Taylor
- Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK
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18
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Cao R, Pastukhov A, Aleshin S, Mattia M, Braun J. Binocular rivalry reveals an out-of-equilibrium neural dynamics suited for decision-making. eLife 2021; 10:e61581. [PMID: 34369875 PMCID: PMC8352598 DOI: 10.7554/elife.61581] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 05/24/2021] [Indexed: 12/19/2022] Open
Abstract
In ambiguous or conflicting sensory situations, perception is often 'multistable' in that it perpetually changes at irregular intervals, shifting abruptly between distinct alternatives. The interval statistics of these alternations exhibits quasi-universal characteristics, suggesting a general mechanism. Using binocular rivalry, we show that many aspects of this perceptual dynamics are reproduced by a hierarchical model operating out of equilibrium. The constitutive elements of this model idealize the metastability of cortical networks. Independent elements accumulate visual evidence at one level, while groups of coupled elements compete for dominance at another level. As soon as one group dominates perception, feedback inhibition suppresses supporting evidence. Previously unreported features in the serial dependencies of perceptual alternations compellingly corroborate this mechanism. Moreover, the proposed out-of-equilibrium dynamics satisfies normative constraints of continuous decision-making. Thus, multistable perception may reflect decision-making in a volatile world: integrating evidence over space and time, choosing categorically between hypotheses, while concurrently evaluating alternatives.
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Affiliation(s)
- Robin Cao
- Cognitive Biology, Center for Behavioral Brain SciencesMagdeburgGermany
- Gatsby Computational Neuroscience UnitLondonUnited Kingdom
- Istituto Superiore di SanitàRomeItaly
| | | | - Stepan Aleshin
- Cognitive Biology, Center for Behavioral Brain SciencesMagdeburgGermany
| | | | - Jochen Braun
- Cognitive Biology, Center for Behavioral Brain SciencesMagdeburgGermany
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19
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Imprecise neural computations as a source of adaptive behaviour in volatile environments. Nat Hum Behav 2020; 5:99-112. [PMID: 33168951 DOI: 10.1038/s41562-020-00971-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 09/18/2020] [Indexed: 02/01/2023]
Abstract
In everyday life, humans face environments that feature uncertain and volatile or changing situations. Efficient adaptive behaviour must take into account uncertainty and volatility. Previous models of adaptive behaviour involve inferences about volatility that rely on complex and often intractable computations. Because such computations are presumably implausible biologically, it is unclear how humans develop efficient adaptive behaviours in such environments. Here, we demonstrate a counterintuitive result: simple, low-level inferences confined to uncertainty can produce near-optimal adaptive behaviour, regardless of the environmental volatility, assuming imprecisions in computation that conform to the psychophysical Weber law. We further show empirically that this Weber-imprecision model explains human behaviour in volatile environments better than optimal adaptive models that rely on high-level inferences about volatility, even when considering biologically plausible approximations of such models, as well as non-inferential models like adaptive reinforcement learning.
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20
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Biologically plausible mechanisms underlying motor response correction during reward-based decision-making. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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21
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Mysore SP, Kothari NB. Mechanisms of competitive selection: A canonical neural circuit framework. eLife 2020; 9:e51473. [PMID: 32431293 PMCID: PMC7239658 DOI: 10.7554/elife.51473] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/02/2020] [Indexed: 01/25/2023] Open
Abstract
Competitive selection, the transformation of multiple competing sensory inputs and internal states into a unitary choice, is a fundamental component of animal behavior. Selection behaviors have been studied under several intersecting umbrellas including decision-making, action selection, perceptual categorization, and attentional selection. Neural correlates of these behaviors and computational models have been investigated extensively. However, specific, identifiable neural circuit mechanisms underlying the implementation of selection remain elusive. Here, we employ a first principles approach to map competitive selection explicitly onto neural circuit elements. We decompose selection into six computational primitives, identify demands that their execution places on neural circuit design, and propose a canonical neural circuit framework. The resulting framework has several links to neural literature, indicating its biological feasibility, and has several common elements with prominent computational models, suggesting its generality. We propose that this framework can help catalyze experimental discovery of the neural circuit underpinnings of competitive selection.
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Affiliation(s)
- Shreesh P Mysore
- Department of Psychological and Brain Sciences, Johns Hopkins UniversityBaltimoreUnited States
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins UniversityBaltimoreUnited States
| | - Ninad B Kothari
- Department of Psychological and Brain Sciences, Johns Hopkins UniversityBaltimoreUnited States
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22
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Schoemann M, Scherbaum S. From high- to one-dimensional dynamics of decision making: testing simplifications in attractor models. Cogn Process 2020; 21:303-313. [PMID: 32016686 PMCID: PMC7203584 DOI: 10.1007/s10339-020-00953-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/22/2020] [Indexed: 11/14/2022]
Abstract
Computational models introduce simplifications that need to be understood and validated. For attractor models of decision making, the main simplification is the high-level representation of different sub-processes of the complex decision system in one dynamic description of the overall process dynamics. This simplification implies that the overall process dynamics of the decision system are independent from specific values handled in different sub-processes. Here, we test the validity of this simplification empirically by investigating choice perseveration in a nonverbal, value-based decision task. Specifically, we tested whether choice perseveration occurred irrespectively of the attribute dimension as suggested by a simulation of the computational model. We find evidence supporting the validity of the simplification. We conclude that the simplification might capture mechanistic aspects of decision-making processes, and that the summation of the overall process dynamics of decision systems into one single variable is a valid approach in computational modeling. Supplement materials such as empirical data, analysis scripts, and the computational model are publicly available at the Open Science Framework (osf.io/7fb5q).
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Affiliation(s)
- Martin Schoemann
- Department of Psychology, Technische Universität Dresden, Zellescher Weg 17, 01069 Dresden, Germany
- Department of Management/MAPP, Aarhus University, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
| | - Stefan Scherbaum
- Department of Psychology, Technische Universität Dresden, Zellescher Weg 17, 01069 Dresden, Germany
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23
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Atiya NAA, Zgonnikov A, O’Hora D, Schoemann M, Scherbaum S, Wong-Lin K. Changes-of-mind in the absence of new post-decision evidence. PLoS Comput Biol 2020; 16:e1007149. [PMID: 32012147 PMCID: PMC7018100 DOI: 10.1371/journal.pcbi.1007149] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 02/13/2020] [Accepted: 11/08/2019] [Indexed: 11/19/2022] Open
Abstract
Decisions are occasionally accompanied by changes-of-mind. While considered a hallmark of cognitive flexibility, the mechanisms underlying changes-of-mind remain elusive. Previous studies on perceptual decision making have focused on changes-of-mind that are primarily driven by the accumulation of additional noisy sensory evidence after the initial decision. In a motion discrimination task, we demonstrate that changes-of-mind can occur even in the absence of additional evidence after the initial decision. Unlike previous studies of changes-of-mind, the majority of changes-of-mind in our experiment occurred in trials with prolonged initial response times. This suggests a distinct mechanism underlying such changes. Using a neural circuit model of decision uncertainty and change-of-mind behaviour, we demonstrate that this phenomenon is associated with top-down signals mediated by an uncertainty-monitoring neural population. Such a mechanism is consistent with recent neurophysiological evidence showing a link between changes-of-mind and elevated top-down neural activity. Our model explains the long response times associated with changes-of-mind through high decision uncertainty levels in such trials, and accounts for the observed motor response trajectories. Overall, our work provides a computational framework that explains changes-of-mind in the absence of new post-decision evidence.
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Affiliation(s)
- Nadim A. A. Atiya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry∼Londonderry, United Kingdom
| | - Arkady Zgonnikov
- School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - Denis O’Hora
- School of Psychology, National University of Ireland Galway, Galway, Ireland
- * E-mail: (DO); (KFW-L)
| | - Martin Schoemann
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Department of Management/MAPP, Aarhus University, Aarhus, Denmark
| | - Stefan Scherbaum
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry∼Londonderry, United Kingdom
- * E-mail: (DO); (KFW-L)
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24
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Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level. Cell 2020; 180:536-551.e17. [PMID: 31955849 DOI: 10.1016/j.cell.2019.12.018] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 10/28/2019] [Accepted: 12/12/2019] [Indexed: 12/20/2022]
Abstract
Goal-directed behavior requires the interaction of multiple brain regions. How these regions and their interactions with brain-wide activity drive action selection is less understood. We have investigated this question by combining whole-brain volumetric calcium imaging using light-field microscopy and an operant-conditioning task in larval zebrafish. We find global, recurring dynamics of brain states to exhibit pre-motor bifurcations toward mutually exclusive decision outcomes. These dynamics arise from a distributed network displaying trial-by-trial functional connectivity changes, especially between cerebellum and habenula, which correlate with decision outcome. Within this network the cerebellum shows particularly strong and predictive pre-motor activity (>10 s before movement initiation), mainly within the granule cells. Turn directions are determined by the difference neuroactivity between the ipsilateral and contralateral hemispheres, while the rate of bi-hemispheric population ramping quantitatively predicts decision time on the trial-by-trial level. Our results highlight a cognitive role of the cerebellum and its importance in motor planning.
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25
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Switching Operation Modes in the Neocortex via Cholinergic Neuromodulation. Mol Neurobiol 2019; 57:139-149. [DOI: 10.1007/s12035-019-01764-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 08/29/2019] [Indexed: 12/26/2022]
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26
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Krugliakova E, Gorin A, Fedele T, Shtyrov Y, Moiseeva V, Klucharev V, Shestakova A. The Monetary Incentive Delay (MID) Task Induces Changes in Sensory Processing: ERP Evidence. Front Hum Neurosci 2019; 13:382. [PMID: 31736730 PMCID: PMC6839045 DOI: 10.3389/fnhum.2019.00382] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 10/14/2019] [Indexed: 01/08/2023] Open
Abstract
Numerous cognitive studies have demonstrated experience-induced plasticity in the primary sensory cortex, indicating that repeated decisions could modulate sensory processing. In this context, we investigated whether an auditory version of the monetary incentive delay (MID) task could change the neural processing of the incentive cues that code expected monetary outcomes. To study sensory plasticity, we presented the incentive cues as deviants during oddball sessions recorded before and after training in the two MID task sessions. We found that after 2 days of training in the MID task, incentive cues evoked a larger P3a (compared with the baseline condition), indicating there was an enhancement of the involuntary attention to the stimuli that predict rewards. At the individual level, the training-induced change of mismatch-related negativity was correlated with the amplitude of the feedback-related negativity (FRN) recorded during the first MID task session. Our results show that the MID task evokes plasticity changes in the auditory system associated with better passive discrimination of incentive cues and with enhanced involuntary attention switching towards these cues. Thus, the sensory processing of incentive cues is dynamically modulated by previous outcomes.
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Affiliation(s)
- Elena Krugliakova
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Alexey Gorin
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Tommaso Fedele
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland
| | - Yury Shtyrov
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
- Laboratory of Behavioural Neurodynamics, Saint Petersburg State University, Saint Petersburg, Russia
| | - Victoria Moiseeva
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Vasily Klucharev
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Anna Shestakova
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
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27
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28
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Economic Decisions through Circuit Inhibition. Curr Biol 2019; 29:3814-3824.e5. [PMID: 31679936 DOI: 10.1016/j.cub.2019.09.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 09/04/2019] [Accepted: 09/11/2019] [Indexed: 11/21/2022]
Abstract
Economic choices between goods are thought to rely on the orbitofrontal cortex (OFC), but the decision mechanisms remain poorly understood. To shed light on this fundamental issue, we recorded from the OFC of monkeys choosing between two juices offered sequentially. An analysis of firing rates across time windows revealed the presence of different groups of neurons similar to those previously identified under simultaneous offers. This observation suggested that economic decisions in the two modalities are formed in the same neural circuit. We then examined several hypotheses on the decision mechanisms. OFC neurons encoded good identities and values in a juice-based representation (labeled lines). Contrary to previous assessments, our data argued against the idea that decisions rely on mutual inhibition at the level of offer values. In fact, we showed that previous arguments for mutual inhibition were confounded by differences in value ranges. Instead, decisions seemed to involve mechanisms of circuit inhibition, whereby each offer value indirectly inhibited neurons encoding the opposite choice outcome. Our results reconcile a variety of previous findings and provide a general account for the neuronal underpinnings of economic choices.
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29
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Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics. Curr Opin Neurobiol 2019; 58:122-129. [DOI: 10.1016/j.conb.2019.09.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 07/16/2019] [Accepted: 09/03/2019] [Indexed: 12/19/2022]
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30
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Soukup T, Lamb BW, Weigl M, Green JSA, Sevdalis N. An Integrated Literature Review of Time-on-Task Effects With a Pragmatic Framework for Understanding and Improving Decision-Making in Multidisciplinary Oncology Team Meetings. Front Psychol 2019; 10:1245. [PMID: 31354555 PMCID: PMC6634259 DOI: 10.3389/fpsyg.2019.01245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 05/13/2019] [Indexed: 01/04/2023] Open
Abstract
Multidisciplinary oncology team meetings (MDMs) or tumor boards, like other MDMs in healthcare, facilitate the incorporation of diverse clinical expertise into treatment planning for patients. Decision-making (DM) in relation to treatment planning in MDMs is carried out repeatedly until all patients put forward for discussion have been reviewed. Despite continuing financial pressure and staff shortages, the workload of cancer MDMs, and therefore meeting duration continue to increase (up to 5 h) with patients often receiving less than 2 min of team input. This begs the question as to whether the current set-up is conducive to achieve optimal DM, which these multi-specialty teams were set out to achieve in the first place. Much of what it is known, however, about the effects of prolonged cognitive activity comes from various subfields of science, leaving a gap in applied knowledge relating to complex healthcare environments. The objective of this review was thus to synthesize theory, evidence and clinical practice in order to bring the current understanding of prolonged, repeated DM into the context of cancer MDMs. We explore how and why time spent on a task affects performance in such settings, and what strategies can be employed by cancer teams to counteract negative effects and improve quality and safety. In the process, we propose a pragmatic framework of repeated DM that encompasses the strength, the process and the cost-benefit models of self-control as applied to real-world contexts of cancer MDMs. We also highlight promising research avenues for closing the research-to-practice gap. Theoretical and empirical evidence reviewed in this paper suggests that over prolonged time spent on a task, repeated DM is cognitively taxing, leading to performance detriments. This deterioration is associated with various cognitive-behavioral pitfalls, including decreased attentional capacity and reduced ability to effectively evaluate choices, as well as less analytical DM and increased reliance on heuristics. As a short to medium term improvement for ensuring safety, consistently high quality of care for all patients, and the clinician wellbeing, future research and interventions in cancer MDMs should address time-on-task effects with a combination of evidence-based cognitive strategies. We propose in this review multiple measures that range from food intake, short breaks, rewards, and mental exercises. As a long term imperative, however, capacity within cancer services needs to be reviewed as well as how best to plan workforce development and service delivery models to achieve population coverage whilst maintaining safety and quality of care. Hence the performance detriments that arise in healthcare workers as a result of the intensity (time spent on a task) and complexity of the workload require not only more research, but also wider regulatory focus and recognition.
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Affiliation(s)
- Tayana Soukup
- Centre for Implementation Science, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Benjamin W. Lamb
- Department of Urology, Cambridge University Healthcare NHS Foundation Trust, Cambridge, United Kingdom
| | - Matthias Weigl
- Institute and Outpatient Clinic for Occupational, Social, and Environmental Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - James S. A. Green
- Centre for Implementation Science, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Whipps Cross University Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Nick Sevdalis
- Centre for Implementation Science, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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Carroll TJ, McNamee D, Ingram JN, Wolpert DM. Rapid Visuomotor Responses Reflect Value-Based Decisions. J Neurosci 2019; 39:3906-3920. [PMID: 30850511 PMCID: PMC6520503 DOI: 10.1523/jneurosci.1934-18.2019] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 12/18/2022] Open
Abstract
Cognitive decision-making is known to be sensitive to the values of potential options, which are the probability and size of rewards associated with different choices. Here, we examine whether rapid motor responses to perturbations of visual feedback about movement, which mediate low-level and involuntary feedback control loops, reflect computations associated with high-level value-based decision-making. In three experiments involving human participants, we varied the value associated with different potential targets for reaching movements by controlling the distributions of rewards across the targets (Experiment 1), the probability with which each target could be specified (Experiment 2), or both (Experiment 3). We found that the size of rapid and involuntary feedback responses to movement perturbations was strongly influenced by the relative value between targets. A statistical model of relative value that includes a term for risk sensitivity provided the best fit to the visuomotor response data, illustrating that feedback control policies are biased to favor more frequent task success at the expense of the overall extrinsic reward accumulated through movement. Importantly however, the regulation of rapid feedback responses was associated with successful pursuit of high-value task outcomes. This implies that when we move, the brain specifies a set of feedback control gains that enable low-level motor areas not only to generate efficient and accurate movement, but also to rapidly and adaptively respond to evolving sensory information in a manner consistent with value-based decision-making.SIGNIFICANCE STATEMENT Current theories of sensorimotor control suggest that, rather than selecting and planning the details of movements in advance, the role of the brain is to set time-varying feedback gains that continuously transform sensory information into motor commands by feedback control. Here, we examine whether the fastest motor responses to perturbations of movement, which mediate low-level and involuntary feedback control loops (i.e., reflexes), reflect computations associated with high-level, value-based decision-making. We find that rapid feedback responses during reaching reflect the relative probabilities and rewards associated with target options. This suggests that low-order components of the sensorimotor control hierarchy, which generate rapid and automatic responses, can continuously evaluate evolving sensory evidence and initiate responses according to the prospect of reward.
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Affiliation(s)
- Timothy J Carroll
- Centre for Sensorimotor Performance, School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane 4072, Queensland, Australia,
| | - Daniel McNamee
- Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom, and
| | - James N Ingram
- Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom, and
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, New York
| | - Daniel M Wolpert
- Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom, and
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, New York
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32
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Gollo LL, Karim M, Harris JA, Morley JW, Breakspear M. Hierarchical and Nonlinear Dynamics in Prefrontal Cortex Regulate the Precision of Perceptual Beliefs. Front Neural Circuits 2019; 13:27. [PMID: 31068794 PMCID: PMC6491505 DOI: 10.3389/fncir.2019.00027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 03/29/2019] [Indexed: 11/13/2022] Open
Abstract
Actions are shaped not only by the content of our percepts but also by our confidence in them. To study the cortical representation of perceptual precision in decision making, we acquired functional imaging data whilst participants performed two vibrotactile forced-choice discrimination tasks: a fast-slow judgment, and a same-different judgment. The first task requires a comparison of the perceived vibrotactile frequencies to decide which one is faster. However, the second task requires that the estimated difference between those frequencies is weighed against the precision of each percept-if both stimuli are very precisely perceived, then any slight difference is more likely to be identified than if the percepts are uncertain. We additionally presented either pure sinusoidal or temporally degraded "noisy" stimuli, whose frequency/period differed slightly from cycle to cycle. In this way, we were able to manipulate the perceptual precision. We report a constellation of cortical regions in the rostral prefrontal cortex (PFC), dorsolateral PFC (DLPFC) and superior frontal gyrus (SFG) associated with the perception of stimulus difference, the presence of stimulus noise and the interaction between these factors. Dynamic causal modeling (DCM) of these data suggested a nonlinear, hierarchical model, whereby activity in the rostral PFC (evoked by the presence of stimulus noise) mutually interacts with activity in the DLPFC (evoked by stimulus differences). This model of effective connectivity outperformed competing models with serial and parallel interactions, hence providing a unique insight into the hierarchical architecture underlying the representation and appraisal of perceptual belief and precision in the PFC.
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Affiliation(s)
- Leonardo L Gollo
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Centre of Excellence for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Muhsin Karim
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,The Black Dog Institute, Sydney, NSW, Australia
| | - Justin A Harris
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | - John W Morley
- School of Medicine, Western Sydney University, Sydney, NSW, Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Centre of Excellence for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,The Black Dog Institute, Sydney, NSW, Australia.,Metro North Mental Health Service, Brisbane, QLD, Australia.,Hunter Medical Research Institute, University of Newcastle, New Lambton Heights, NSW, Australia
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33
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Domenech P, Redouté J, Koechlin E, Dreher JC. The Neuro-Computational Architecture of Value-Based Selection in the Human Brain. Cereb Cortex 2019; 28:585-601. [PMID: 28057725 DOI: 10.1093/cercor/bhw396] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 12/05/2016] [Indexed: 11/14/2022] Open
Abstract
Current neural models of value-based decision-making consider choices as a 2-stage process, proceeding from the "valuation" of each option under consideration to the "selection" of the best option on the basis of their subjective values. However, little is known about the computational mechanisms at play at the selection stage and its implementation in the human brain. Here, we used drift-diffusion models combined with model-based functional magnetic resonance imaging, effective connectivity, and multivariate pattern analysis to characterize the neuro-computational architecture of value-based decisions. We found that 2 key drift-diffusion computations at the selection stage, namely integration and choice readout, engage distinct brain regions, with the dorsolateral prefrontal cortex integrating a decision value signal computed in the ventromedial prefrontal cortex, and the posterior parietal cortex reading out choice outcomes. Our findings suggest that this prefronto-parietal network acts as a hub implementing behavioral selection through a distributed drift-diffusion process.
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Affiliation(s)
- Philippe Domenech
- Neuroeconomics, Reward, and Decision-making group, Institut des Sciences Cognitives Marc Jeannerod, Centre National pour la Recherche Scientifique, 69675 Bron, France.,Département de Biologie Humaine, University of Lyon 1, 69622 Villeurbanne, France
| | - Jérôme Redouté
- Neuroeconomics, Reward, and Decision-making group, Institut des Sciences Cognitives Marc Jeannerod, Centre National pour la Recherche Scientifique, 69675 Bron, France.,Département de Biologie Humaine, University of Lyon 1, 69622 Villeurbanne, France
| | - Etienne Koechlin
- Laboratoire de Neuroscience Cognitive, Ecole Normale Supérieure, INSERM, 75005 Paris, France
| | - Jean-Claude Dreher
- Neuroeconomics, Reward, and Decision-making group, Institut des Sciences Cognitives Marc Jeannerod, Centre National pour la Recherche Scientifique, 69675 Bron, France.,Département de Biologie Humaine, University of Lyon 1, 69622 Villeurbanne, France
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34
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Pascucci D, Mancuso G, Santandrea E, Della Libera C, Plomp G, Chelazzi L. Laws of concatenated perception: Vision goes for novelty, decisions for perseverance. PLoS Biol 2019; 17:e3000144. [PMID: 30835720 PMCID: PMC6400421 DOI: 10.1371/journal.pbio.3000144] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 01/28/2019] [Indexed: 12/04/2022] Open
Abstract
Every instant of perception depends on a cascade of brain processes calibrated to the history of sensory and decisional events. In the present work, we show that human visual perception is constantly shaped by two contrasting forces exerted by sensory adaptation and past decisions. In a series of experiments, we used multilevel modeling and cross-validation approaches to investigate the impact of previous stimuli and decisions on behavioral reports during adjustment and forced-choice tasks. Our results revealed that each perceptual report is permeated by opposite biases from a hierarchy of serially dependent processes: Low-level adaptation repels perception away from previous stimuli, whereas decisional traces attract perceptual reports toward the recent past. In this hierarchy of serial dependence, "continuity fields" arise from the inertia of decisional templates and not from low-level sensory processes. This finding is consistent with a Two-process model of serial dependence in which the persistence of readout weights in a decision unit compensates for sensory adaptation, leading to attractive biases in sequential perception. We propose a unified account of serial dependence in which functionally distinct mechanisms, operating at different stages, promote the differentiation and integration of visual information over time.
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Affiliation(s)
- David Pascucci
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Giovanni Mancuso
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Elisa Santandrea
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Chiara Della Libera
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- National Institute of Neuroscience, Verona, Italy
| | - Gijs Plomp
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Leonardo Chelazzi
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- National Institute of Neuroscience, Verona, Italy
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35
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Langille JJ, Brown RE. The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition. Front Syst Neurosci 2018; 12:52. [PMID: 30416432 PMCID: PMC6212519 DOI: 10.3389/fnsys.2018.00052] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 09/28/2018] [Indexed: 01/12/2023] Open
Abstract
Trettenbrein (2016) has argued that the concept of the synapse as the locus of memory is outdated and has made six critiques of this concept. In this article, we examine these six critiques and suggest that the current theories of the neurobiology of memory and the empirical data indicate that synaptic activation is the first step in a chain of cellular and biochemical events that lead to memories formed in cell assemblies and neural networks that rely on synaptic modification for their formation. These neural networks and their modified synaptic connections can account for the cognitive basis of learning and memory and for memory deterioration in neurological disorders. We first discuss Hebb's (1949) theory that synaptic change and the formation of cell assemblies and phase sequences can link neurophysiology to cognitive processes. We then examine each of Trettenbrein's (2016) critiques of the synaptic theory in light of Hebb's theories and recent empirical data. We examine the biochemical basis of memory formation and the necessity of synaptic modification to form the neural networks underlying learning and memory. We then examine the use of Hebb's theories of synaptic change and cell assemblies for integrating neurophysiological and cognitive conceptions of learning and memory. We conclude with an examination of the applications of the Hebb synapse and cell assembly theories to the study of the neuroscience of learning and memory, the development of computational models of memory and the construction of "intelligent" robots. We conclude that the synaptic theory of memory has not met its demise, but is essential to our understanding of the neural basis of memory, which has two components: synaptic plasticity and intrinsic plasticity.
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Affiliation(s)
| | - Richard E. Brown
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS, Canada
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36
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Having More Choices Changes How Human Observers Weight Stable Sensory Evidence. J Neurosci 2018; 38:8635-8649. [PMID: 30143576 DOI: 10.1523/jneurosci.0440-18.2018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Revised: 08/07/2018] [Accepted: 08/14/2018] [Indexed: 02/04/2023] Open
Abstract
Decision-making becomes slower when more choices are available. Existing models attribute this slowing to poor sensory processing, to attenuated rates of sensory evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). However, studies have not isolated the effects of having more choices on sensory and decision-related processes from changes in task difficulty and divided attention. Here, we controlled task difficulty while independently manipulating the distribution of attention and the number of choices available to male and female human observers. We used EEG to measure steady-state visually evoked potentials (SSVEPs) and a frontal late positive deflection (LPD), EEG markers of sensory and postsensory decision-related processes, respectively. We found that dividing attention decreased SSVEP and LPD amplitudes, consistent with dampened sensory responses and slower rates of evidence accumulation, respectively. In contrast, having more choices did not alter SSVEP amplitude and led to a larger LPD. These results suggest that having more options largely spares early sensory processing and slows down decision-making via a selective increase in decision thresholds.SIGNIFICANCE STATEMENT When more choices are available, decision-making becomes slower. We tested whether this phenomenon is due to poor sensory processing, to reduced rates of evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). We measured choice modulations of sensory and decision-related neural responses using EEG. We also minimized potential confounds from changes in the distribution of attention and task difficulty, which often covary with having more choices. Dividing attention reduced the activity levels of both sensory and decision-related responses. However, having more choices did not change sensory processing and led to larger decision-related responses. These results suggest that having more choices spares sensory processing and selectively increases decision thresholds.
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37
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Handa T, Takekawa T, Harukuni R, Isomura Y, Fukai T. Medial Frontal Circuit Dynamics Represents Probabilistic Choices for Unfamiliar Sensory Experience. Cereb Cortex 2018; 27:3818-3831. [PMID: 28184411 DOI: 10.1093/cercor/bhx031] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 01/21/2017] [Indexed: 12/11/2022] Open
Abstract
Neurons in medial frontal cortex (MFC) receive sensory signals that are crucial for decision-making behavior. While decision-making is easy for familiar sensory signals, it becomes more elaborative when sensory signals are less familiar to animals. It remains unclear how the population of neurons enables the coordinate transformation of such a sensory input into ambiguous choice responses. Furthermore, whether and how cortical oscillations temporally coordinate neuronal firing during this transformation has not been extensively studied. Here, we recorded neuronal population responses to familiar or unfamiliar auditory cues in rat MFC and computed their probabilistic evolution. Population responses to familiar sounds organize into neuronal trajectories containing multiplexed sensory, motor, and choice information. Unfamiliar sounds, in contrast, evoke trajectories that travel under the guidance of familiar paths and eventually diverge to unique decision states. Local field potentials exhibited beta- (15-20 Hz) and gamma-band (50-60 Hz) oscillations to which neuronal firing showed modest phase locking. Interestingly, gamma oscillation, but not beta oscillation, increased its power abruptly at some timepoint by which neural trajectories for different choices were near maximally separated. Our results emphasize the importance of the evolution of neural trajectories in rapid probabilistic decisions that utilize unfamiliar sensory information.
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Affiliation(s)
- Takashi Handa
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Takashi Takekawa
- Faculty of Informatics, Kogakuin University, Shinjuku-ku, Tokyo 163-8677, Japan
| | - Rie Harukuni
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Yoshikazu Isomura
- Brain Science Institute, Tamagawa University, Machida, Tokyo 194-8610, Japan
| | - Tomoki Fukai
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
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38
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Dumont G, Maex R, Gutkin B. Dopaminergic Neurons in the Ventral Tegmental Area and Their Dysregulation in Nicotine Addiction. COMPUTATIONAL PSYCHIATRY 2018. [DOI: 10.1016/b978-0-12-809825-7.00003-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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39
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Hramov AE, Maksimenko VA, Pchelintseva SV, Runnova AE, Grubov VV, Musatov VY, Zhuravlev MO, Koronovskii AA, Pisarchik AN. Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks. Front Neurosci 2017; 11:674. [PMID: 29255403 PMCID: PMC5722852 DOI: 10.3389/fnins.2017.00674] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/20/2017] [Indexed: 01/04/2023] Open
Abstract
In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.
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Affiliation(s)
- Alexander E Hramov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Vladimir A Maksimenko
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Svetlana V Pchelintseva
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Anastasiya E Runnova
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vadim V Grubov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vyacheslav Yu Musatov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Maksim O Zhuravlev
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Alexey A Koronovskii
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Alexander N Pisarchik
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
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40
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Working Memory and Decision-Making in a Frontoparietal Circuit Model. J Neurosci 2017; 37:12167-12186. [PMID: 29114071 DOI: 10.1523/jneurosci.0343-17.2017] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 08/24/2017] [Accepted: 09/19/2017] [Indexed: 12/25/2022] Open
Abstract
Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and distinct roles of these areas and the nature of their coordination in cognitive function remain poorly understood. Biophysically based computational models of cortical circuits have provided insights into the mechanisms supporting these functions, yet they have primarily focused on the local microcircuit level, raising questions about the principles for distributed cognitive computation in multiregional networks. To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits. The circuit architecture includes hierarchical differences in local recurrent structure and implements reciprocal long-range projections. This parsimonious model captures a range of behavioral and neuronal features of frontoparietal circuits across multiple WM and DM paradigms. In the context of WM, both areas exhibit persistent activity, but, in response to intervening distractors, PPC transiently encodes distractors while PFC filters distractors and supports WM robustness. With regard to DM, the PPC module generates graded representations of accumulated evidence supporting target selection, while the PFC module generates more categorical responses related to action or choice. These findings suggest computational principles for distributed, hierarchical processing in cortex during cognitive function and provide a framework for extension to multiregional models.SIGNIFICANCE STATEMENT Working memory and decision-making are fundamental "building blocks" of cognition, and deficits in these functions are associated with neuropsychiatric disorders such as schizophrenia. These cognitive functions engage distributed networks with prefrontal cortex (PFC) and posterior parietal cortex (PPC) at the core. It is not clear, however, what the contributions of PPC and PFC are in light of the computations that subserve working memory and decision-making. We constructed a biophysical model of a reciprocally connected frontoparietal circuit that revealed shared and distinct functions for the PFC and PPC across working memory and decision-making tasks. Our parsimonious model connects circuit-level properties to cognitive functions and suggests novel design principles beyond those of local circuits for cognitive processing in multiregional brain networks.
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41
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Duverne S, Koechlin E. Rewards and Cognitive Control in the Human Prefrontal Cortex. Cereb Cortex 2017; 27:5024-5039. [DOI: 10.1093/cercor/bhx210] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 07/24/2017] [Indexed: 12/19/2022] Open
Affiliation(s)
- Sandrine Duverne
- Département d'Etudes Cognitives, Ecole Normale Supérieure, 75005 Paris, France
- Laboratoire de Neurosciences Cognitives, Institut National de la Santé et de la Recherche Médicale, 75005 Paris, France
| | - Etienne Koechlin
- Département d'Etudes Cognitives, Ecole Normale Supérieure, 75005 Paris, France
- Laboratoire de Neurosciences Cognitives, Institut National de la Santé et de la Recherche Médicale, 75005 Paris, France
- Centre de Neuroimagerie pour la Recherche, Université Pierre et Marie Curie, 75013 Paris, France
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42
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Inferring cortical function in the mouse visual system through large-scale systems neuroscience. Proc Natl Acad Sci U S A 2017; 113:7337-44. [PMID: 27382147 DOI: 10.1073/pnas.1512901113] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort.
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Hurtado-López J, Ramirez-Moreno DF, Sejnowski TJ. Decision-making neural circuits mediating social behaviors : An attractor network model. J Comput Neurosci 2017; 43:127-142. [PMID: 28660531 DOI: 10.1007/s10827-017-0654-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 06/12/2017] [Accepted: 06/13/2017] [Indexed: 11/24/2022]
Abstract
We propose a mathematical model of a continuous attractor network that controls social behaviors. The model is examined with bifurcation analysis and computer simulations. The results show that the model exhibits stable steady states and thresholds for steady state transitions corresponding to some experimentally observed behaviors, such as aggression control. The performance of the model and the relation with experimental evidence are discussed.
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Affiliation(s)
- Julián Hurtado-López
- Department of Mathematics, Universidad Autónoma de Occidente, Cll 25 No. 115-85 Km 2 vía Cali-Jamundí, 760030, Cali, Colombia.
| | | | - Terrence J Sejnowski
- Howard Hughes Medical Institute, the Salk Institute for Biological Studies, La Jolla, California, 92037, USA
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44
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A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements. PLoS Comput Biol 2017; 13:e1005542. [PMID: 28574992 PMCID: PMC5456035 DOI: 10.1371/journal.pcbi.1005542] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 04/26/2017] [Indexed: 01/21/2023] Open
Abstract
The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.
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45
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Hunt LT, Hayden BY. A distributed, hierarchical and recurrent framework for reward-based choice. Nat Rev Neurosci 2017; 18:172-182. [PMID: 28209978 PMCID: PMC5621622 DOI: 10.1038/nrn.2017.7] [Citation(s) in RCA: 137] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this Opinion article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex. This account also suggests that certain correlates of value are emergent rather than represented explicitly in the brain.
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Affiliation(s)
- Laurence T Hunt
- Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK
| | - Benjamin Y Hayden
- Department of Brain and Cognitive Sciences, University of Rochester, 309 Meliora Hall, Rochester, New York 14618, USA
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46
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Insabato A, Pannunzi M, Deco G. Multiple Choice Neurodynamical Model of the Uncertain Option Task. PLoS Comput Biol 2017; 13:e1005250. [PMID: 28076355 PMCID: PMC5226718 DOI: 10.1371/journal.pcbi.1005250] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 11/07/2016] [Indexed: 11/19/2022] Open
Abstract
The uncertain option task has been recently adopted to investigate the neural systems underlying the decision confidence. Latterly single neurons activity has been recorded in lateral intraparietal cortex of monkeys performing an uncertain option task, where the subject is allowed to opt for a small but sure reward instead of making a risky perceptual decision. We propose a multiple choice model implemented in a discrete attractors network. This model is able to reproduce both behavioral and neurophysiological experimental data and therefore provides support to the numerous perspectives that interpret the uncertain option task as a sensory-motor association. The model explains the behavioral and neural data recorded in monkeys as the result of the multistable attractor landscape and produces several testable predictions. One of these predictions may help distinguish our model from a recently proposed continuous attractor model.
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Affiliation(s)
- Andrea Insabato
- Universitat Pompeu Fabra, Center for Brain and Cognition, Barcelona, Spain
- * E-mail:
| | - Mario Pannunzi
- Universitat Pompeu Fabra, Center for Brain and Cognition, Barcelona, Spain
| | - Gustavo Deco
- Universitat Pompeu Fabra, Center for Brain and Cognition, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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47
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Bonaiuto JJ, de Berker A, Bestmann S. Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models. eLife 2016; 5:e20047. [PMID: 28005007 PMCID: PMC5243027 DOI: 10.7554/elife.20047] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 12/19/2016] [Indexed: 12/21/2022] Open
Abstract
Animals and humans have a tendency to repeat recent choices, a phenomenon known as choice hysteresis. The mechanism for this choice bias remains unclear. Using an established, biophysically informed model of a competitive attractor network for decision making, we found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices. In the model, choice variability could be directionally altered through amplification or dampening of post-trial activity decay through simulated depolarizing or hyperpolarizing network stimulation. An analogous intervention using transcranial direct current stimulation (tDCS) over left dorsolateral prefrontal cortex (dlPFC) yielded a close match between model predictions and experimental results: net soma depolarizing currents increased choice hysteresis, while hyperpolarizing currents suppressed it. Residual activity in competitive attractor networks within dlPFC may thus give rise to biases in perceptual choices, which can be directionally controlled through non-invasive brain stimulation.
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Affiliation(s)
- James J Bonaiuto
- Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Archy de Berker
- Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Sven Bestmann
- Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London, United Kingdom
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48
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Cavanagh SE, Wallis JD, Kennerley SW, Hunt LT. Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice. eLife 2016; 5. [PMID: 27705742 PMCID: PMC5052031 DOI: 10.7554/elife.18937] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 09/15/2016] [Indexed: 01/28/2023] Open
Abstract
Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations. DOI:http://dx.doi.org/10.7554/eLife.18937.001
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Affiliation(s)
- Sean E Cavanagh
- Sobell Department of Motor Neuroscience, University College London, London, United Kingdom
| | - Joni D Wallis
- Department of Psychology, University of California, Berkeley, Berkeley, United States.,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Steven W Kennerley
- Sobell Department of Motor Neuroscience, University College London, London, United Kingdom.,Department of Psychology, University of California, Berkeley, Berkeley, United States.,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Laurence T Hunt
- Sobell Department of Motor Neuroscience, University College London, London, United Kingdom.,Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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49
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Morcos AS, Harvey CD. History-dependent variability in population dynamics during evidence accumulation in cortex. Nat Neurosci 2016; 19:1672-1681. [PMID: 27694990 PMCID: PMC5127723 DOI: 10.1038/nn.4403] [Citation(s) in RCA: 142] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 09/06/2016] [Indexed: 12/15/2022]
Abstract
We studied how the posterior parietal cortex combined new information with ongoing activity dynamics as mice accumulated evidence during a virtual-navigation task. Using new methods to analyze population activity on single trials, we found that activity transitioned rapidly between different sets of active neurons. Each event in a trial — whether an evidence cue or a behavioral choice — caused seconds-long modifications to the probabilities that govern how one activity pattern transitions to the next, forming a short-term memory. A sequence of evidence cues triggered a chain of these modifications resulting in a signal for accumulated evidence. Multiple distinguishable activity patterns were possible for the same accumulated evidence because representations of ongoing events were influenced by previous within and across trial events. Therefore, evidence accumulation need not require the explicit competition between groups of neurons, as in winner-take-all models, but could instead emerge implicitly from general dynamical properties that instantiate short-term memory.
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Affiliation(s)
- Ari S Morcos
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA
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50
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Cao R, Pastukhov A, Mattia M, Braun J. Collective Activity of Many Bistable Assemblies Reproduces Characteristic Dynamics of Multistable Perception. J Neurosci 2016; 36:6957-72. [PMID: 27358454 PMCID: PMC6604901 DOI: 10.1523/jneurosci.4626-15.2016] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 05/11/2016] [Accepted: 05/16/2016] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED The timing of perceptual decisions depends on both deterministic and stochastic factors, as the gradual accumulation of sensory evidence (deterministic) is contaminated by sensory and/or internal noise (stochastic). When human observers view multistable visual displays, successive episodes of stochastic accumulation culminate in repeated reversals of visual appearance. Treating reversal timing as a "first-passage time" problem, we ask how the observed timing densities constrain the underlying stochastic accumulation. Importantly, mean reversal times (i.e., deterministic factors) differ enormously between displays/observers/stimulation levels, whereas the variance and skewness of reversal times (i.e., stochastic factors) keep characteristic proportions of the mean. What sort of stochastic process could reproduce this highly consistent "scaling property?" Here we show that the collective activity of a finite population of bistable units (i.e., a generalized Ehrenfest process) quantitatively reproduces all aspects of the scaling property of multistable phenomena, in contrast to other processes under consideration (Poisson, Wiener, or Ornstein-Uhlenbeck process). The postulated units express the spontaneous dynamics of attractor assemblies transitioning between distinct activity states. Plausible candidates are cortical columns, or clusters of columns, as they are preferentially connected and spontaneously explore a restricted repertoire of activity states. Our findings suggests that perceptual representations are granular, probabilistic, and operate far from equilibrium, thereby offering a suitable substrate for statistical inference. SIGNIFICANCE STATEMENT Spontaneous reversals of high-level perception, so-called multistable perception, conform to highly consistent and characteristic statistics, constraining plausible neural representations. We show that the observed perceptual dynamics would be reproduced quantitatively by a finite population of distinct neural assemblies, each with locally bistable activity, operating far from the collective equilibrium (generalized Ehrenfest process). Such a representation would be consistent with the intrinsic stochastic dynamics of neocortical activity, which is dominated by preferentially connected assemblies, such as cortical columns or clusters of columns. We predict that local neuron assemblies will express bistable dynamics, with spontaneous active-inactive transitions, whenever they contribute to high-level perception.
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Affiliation(s)
- Robin Cao
- Institute of Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany, Istituto Superiore di Sanità, 00161 Rome, Italy, and
| | | | | | - Jochen Braun
- Institute of Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany, Center for Behavioral Brain Sciences, 39120 Magdeburg, Germany,
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