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Carandini M. Sensory choices as logistic classification. Neuron 2024:S0896-6273(24)00451-3. [PMID: 39013468 DOI: 10.1016/j.neuron.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/13/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement it by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.
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Affiliation(s)
- Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London WC1 6BT, UK.
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2
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Carandini M. Sensory choices as logistic classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576029. [PMID: 38979189 PMCID: PMC11230223 DOI: 10.1101/2024.01.17.576029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.
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Affiliation(s)
- Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London WC1 6BT, UK
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3
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Chen Y, Chien J, Dai B, Lin D, Chen ZS. Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network. J Neural Eng 2024; 21:10.1088/1741-2552/ad5702. [PMID: 38861996 PMCID: PMC11246699 DOI: 10.1088/1741-2552/ad5702] [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: 01/09/2024] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Objective.Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors.Approach:We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system and a discrete-state hidden semi-Markov model. We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively.Main results:Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.Significance:Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.
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Affiliation(s)
- Yibo Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Program in Artificial Intelligence, University of Science and Technology of China, Hefei, Anhui, China
- Equal contributions (Y.C. and J.C.)
| | - Jonathan Chien
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Equal contributions (Y.C. and J.C.)
| | - Bing Dai
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
| | - Dayu Lin
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
| | - Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA
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4
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Kadam PS, Yang Z, Lu Y, Zhu H, Atiyas Y, Shah N, Fisher S, Nordgren E, Kim J, Issadore D, Eberwine J. Single-Mitochondrion Sequencing Uncovers Distinct Mutational Patterns and Heteroplasmy Landscape in Mouse Astrocytes and Neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598906. [PMID: 38915628 PMCID: PMC11195285 DOI: 10.1101/2024.06.13.598906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Background Mitochondrial (mt) heteroplasmy can cause adverse biological consequences when deleterious mtDNA mutations accumulate disrupting 'normal' mt-driven processes and cellular functions. To investigate the heteroplasmy of such mtDNA changes we developed a moderate throughput mt isolation procedure to quantify the mt single-nucleotide variant (SNV) landscape in individual mouse neurons and astrocytes In this study we amplified mt-genomes from 1,645 single mitochondria (mts) isolated from mouse single astrocytes and neurons to 1. determine the distribution and proportion of mt-SNVs as well as mutation pattern in specific target regions across the mt-genome, 2. assess differences in mtDNA SNVs between neurons and astrocytes, and 3. Study cosegregation of variants in the mouse mtDNA. Results 1. The data show that specific sites of the mt-genome are permissive to SNV presentation while others appear to be under stringent purifying selection. Nested hierarchical analysis at the levels of mitochondrion, cell, and mouse reveals distinct patterns of inter- and intra-cellular variation for mt-SNVs at different sites. 2. Further, differences in the SNV incidence were observed between mouse neurons and astrocytes for two mt-SNV 9027:G>A and 9419:C>T showing variation in the mutational propensity between these cell types. Purifying selection was observed in neurons as shown by the Ka/Ks statistic, suggesting that neurons are under stronger evolutionary constraint as compared to astrocytes. 3. Intriguingly, these data show strong linkage between the SNV sites at nucleotide positions 9027 and 9461. Conclusion This study suggests that segregation as well as clonal expansion of mt-SNVs is specific to individual genomic loci, which is important foundational data in understanding of heteroplasmy and disease thresholds for mutation of pathogenic variants.
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Affiliation(s)
- Parnika S Kadam
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zijian Yang
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Youtao Lu
- Department of Biology, School of Arts and Sciences; University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hua Zhu
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yasemin Atiyas
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishal Shah
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephen Fisher
- Department of Biology, School of Arts and Sciences; University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erik Nordgren
- Department of Biology, School of Arts and Sciences; University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Junhyong Kim
- Department of Biology, School of Arts and Sciences; University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David Issadore
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James Eberwine
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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5
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Wang R, Chen ZS. Large-scale foundation models and generative AI for BigData neuroscience. Neurosci Res 2024:S0168-0102(24)00075-0. [PMID: 38897235 DOI: 10.1016/j.neures.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/15/2024] [Accepted: 05/15/2024] [Indexed: 06/21/2024]
Abstract
Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.
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Affiliation(s)
- Ran Wang
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience and Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
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Piet A, Ponvert N, Ollerenshaw D, Garrett M, Groblewski PA, Olsen S, Koch C, Arkhipov A. Behavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex. Neuron 2024; 112:1876-1890.e4. [PMID: 38447579 PMCID: PMC11156560 DOI: 10.1016/j.neuron.2024.02.008] [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: 05/31/2023] [Revised: 11/17/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024]
Abstract
In complex environments, animals can adopt diverse strategies to find rewards. How distinct strategies differentially engage brain circuits is not well understood. Here, we investigate this question, focusing on the cortical Vip-Sst disinhibitory circuit between vasoactive intestinal peptide-postive (Vip) interneurons and somatostatin-positive (Sst) interneurons. We characterize the behavioral strategies used by mice during a visual change detection task. Using a dynamic logistic regression model, we find that individual mice use mixtures of a visual comparison strategy and a statistical timing strategy. Separately, mice also have periods of task engagement and disengagement. Two-photon calcium imaging shows large strategy-dependent differences in neural activity in excitatory, Sst inhibitory, and Vip inhibitory cells in response to both image changes and image omissions. In contrast, task engagement has limited effects on neural population activity. We find that the diversity of neural correlates of strategy can be understood parsimoniously as the increased activation of the Vip-Sst disinhibitory circuit during the visual comparison strategy, which facilitates task-appropriate responses.
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Affiliation(s)
- Alex Piet
- Allen Institute, Mindscope Program, Seattle, WA, USA.
| | - Nick Ponvert
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | | | | | - Shawn Olsen
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Christof Koch
- Allen Institute, Mindscope Program, Seattle, WA, USA
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7
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Oesch LT, Ryan MB, Churchland AK. From innate to instructed: A new look at perceptual decision-making. Curr Opin Neurobiol 2024; 86:102871. [PMID: 38569230 PMCID: PMC11162954 DOI: 10.1016/j.conb.2024.102871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024]
Abstract
Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.
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Affiliation(s)
- Lukas T Oesch
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
| | - Michael B Ryan
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States. https://twitter.com/NeuroMikeRyan
| | - Anne K Churchland
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States.
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8
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Marrero K, Aruljothi K, Delgadillo C, Kabbara S, Swatch L, Zagha E. Goal-Directed Learning is Multidimensional and Accompanied by Diverse and Widespread Changes in Neocortical Signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.13.528412. [PMID: 36824924 PMCID: PMC9948952 DOI: 10.1101/2023.02.13.528412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of licking-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.
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Affiliation(s)
- Krista Marrero
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Krithiga Aruljothi
- Department of Psychology, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Christian Delgadillo
- Division of Biomedical Sciences, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Sarah Kabbara
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Lovleen Swatch
- College of Natural & Agricultural Sciences, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
| | - Edward Zagha
- Neuroscience Graduate Program, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
- Department of Psychology, University of California, Riverside 900 University Avenue, Riverside CA 92521 USA
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9
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Bandi AC, Runyan CA. Different state-dependence of population codes across cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595581. [PMID: 38826351 PMCID: PMC11142168 DOI: 10.1101/2024.05.23.595581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
During perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an 'optimal' state and two 'sub-optimal' states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.
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Affiliation(s)
- Akhil C Bandi
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA
| | - Caroline A Runyan
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA
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10
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Zhu Z, Kuchibhotla KV. Performance errors during rodent learning reflect a dynamic choice strategy. Curr Biol 2024; 34:2107-2117.e5. [PMID: 38677279 DOI: 10.1016/j.cub.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/10/2024] [Accepted: 04/08/2024] [Indexed: 04/29/2024]
Abstract
Humans, even as infants, use cognitive strategies, such as exploration and hypothesis testing, to learn about causal interactions in the environment. In animal learning studies, however, it is challenging to disentangle higher-order behavioral strategies from errors arising from imperfect task knowledge or inherent biases. Here, we trained head-fixed mice on a wheel-based auditory two-choice task and exploited the intra- and inter-animal variability to understand the drivers of errors during learning. During learning, performance errors are dominated by a choice bias, which, despite appearing maladaptive, reflects a dynamic strategy. Early in learning, mice develop an internal model of the task contingencies such that violating their expectation of reward on correct trials (by using short blocks of non-rewarded "probe" trials) leads to an abrupt shift in strategy. During the probe block, mice behave more accurately with less bias, thereby using their learned stimulus-action knowledge to test whether the outcome contingencies have changed. Despite having this knowledge, mice continued to exhibit a strong choice bias during reinforced trials. This choice bias operates on a timescale of tens to hundreds of trials with a dynamic structure, shifting between left, right, and unbiased epochs. Biased epochs also coincided with faster motor kinematics. Although bias decreased across learning, expert mice continued to exhibit short bouts of biased choices interspersed with longer bouts of unbiased choices and higher performance. These findings collectively suggest that during learning, rodents actively probe their environment in a structured manner to refine their decision-making and maintain long-term flexibility.
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Affiliation(s)
- Ziyi Zhu
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA; Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA; The Solomon Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Kishore V Kuchibhotla
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA; Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA; The Solomon Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
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11
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Yin C, Melin MD, Rojas-Bowe G, Sun XR, Couto J, Gluf S, Kostiuk A, Musall S, Churchland AK. Spontaneous movements and their impact on neural activity fluctuate with latent engagement states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.26.546404. [PMID: 37425720 PMCID: PMC10327038 DOI: 10.1101/2023.06.26.546404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Existing work demonstrates that animals alternate between engaged and disengaged states during perceptual decision-making. To understand the neural signature of these states, we performed cortex-wide measurements of neural activity in mice making auditory decisions. The trial-averaged magnitude of neural activity was similar in the two states. However, the trial-to-trial variance in neural activity was higher during disengagement. To understand this increased variance, we trained separate linear encoding models on neural data from each state. The models demonstrated that although task variables and task-aligned movements impacted neural activity similarly during the two states, movements that are independent of task events explained more variance during disengagement. Behavioral analyses uncovered that during disengagement, movements become uncoupled to task events. Taken together, these results argue that the neural signature of disengagement, though obscured in trial-averaged neural activity, is evident in trial-to-trial variability driven by changing patterns of spontaneous movements.
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Affiliation(s)
- Chaoqun Yin
- UCLA Neuroscience Interdepartmental Program
- Department of Neurobiology, University of California, Los Angeles
| | - Maxwell D Melin
- UCLA Neuroscience Interdepartmental Program
- UCLA-Caltech Medical Scientist Training Program
- Department of Neurobiology, University of California, Los Angeles
| | - Gabriel Rojas-Bowe
- UCLA Neuroscience Interdepartmental Program
- Department of Neurobiology, University of California, Los Angeles
| | | | - João Couto
- Department of Neurobiology, University of California, Los Angeles
| | | | - Alex Kostiuk
- UCLA Neuroscience Interdepartmental Program
- UCLA-Caltech Medical Scientist Training Program
- Department of Neurobiology, University of California, Los Angeles
| | - Simon Musall
- Institute of Biological Information Processing (IBI-3), Forschungszentrum Jülich
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12
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Noel JP, Balzani E, Acerbi L, Benson J, Savin C, Angelaki DE. A common computational and neural anomaly across mouse models of autism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593232. [PMID: 38766250 PMCID: PMC11100696 DOI: 10.1101/2024.05.08.593232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Computational psychiatry has suggested that humans within the autism spectrum disorder (ASD) inflexibly update their expectations (i.e., Bayesian priors). Here, we leveraged high-yield rodent psychophysics (n = 75 mice), extensive behavioral modeling (including principled and heuristics), and (near) brain-wide single cell extracellular recordings (over 53k units in 150 brain areas) to ask (1) whether mice with different genetic perturbations associated with ASD show this same computational anomaly, and if so, (2) what neurophysiological features are shared across genotypes in subserving this deficit. We demonstrate that mice harboring mutations in Fmr1 , Cntnap2 , and Shank3B show a blunted update of priors during decision-making. Neurally, the differentiating factor between animals flexibly and inflexibly updating their priors was a shift in the weighting of prior encoding from sensory to frontal cortices. Further, in mouse models of ASD frontal areas showed a preponderance of units coding for deviations from the animals' long-run prior, and sensory responses did not differentiate between expected and unexpected observations. These findings demonstrate that distinct genetic instantiations of ASD may yield common neurophysiological and behavioral phenotypes.
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13
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Rmus M, Pan TF, Xia L, Collins AGE. Artificial neural networks for model identification and parameter estimation in computational cognitive models. PLoS Comput Biol 2024; 20:e1012119. [PMID: 38748770 PMCID: PMC11132492 DOI: 10.1371/journal.pcbi.1012119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 05/28/2024] [Accepted: 04/27/2024] [Indexed: 05/28/2024] Open
Abstract
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.
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Affiliation(s)
- Milena Rmus
- Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America
| | - Ti-Fen Pan
- Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America
| | - Liyu Xia
- Department of Mathematics, University of California, Berkeley, Berkeley, California, United States of America
| | - Anne G. E. Collins
- Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
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14
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Chakravarty S, Delgado-Sallent C, Kane GA, Xia H, Do QH, Senne RA, Scott BB. A cross-species framework for investigating perceptual evidence accumulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.17.589945. [PMID: 38659929 PMCID: PMC11042372 DOI: 10.1101/2024.04.17.589945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Cross-species studies are important for a comprehensive understanding of brain functions. However, direct quantitative comparison of behaviors across species presents a significant challenge. To enable such comparisons in perceptual decision-making, we developed a synchronized evidence accumulation task for rodents and humans, by aligning mechanics, stimuli, and training. Rats, mice and humans readily learned the task and exhibited qualitatively similar performance. Quantitative model comparison revealed that all three species employed an evidence accumulation strategy, but differed in speed, accuracy, and key decision parameters. Human performance prioritized accuracy, whereas rodent performance was limited by internal time-pressure. Rats optimized reward rate, while mice appeared to switch between evidence accumulation and other strategies trial-to-trial. Together, these results reveal striking similarities and species-specific priorities in decision-making. Furthermore, the synchronized behavioral framework we present may facilitate future studies involving cross-species comparisons, such as evaluating the face validity of animal models of neuropsychiatric disorders. Highlights Development of a free response evidence accumulation task for rats and miceSynchronized video game allows direct comparisons with humansRat, mouse and human behavior are well fit by the same decision modelsModel parameters reveal species-specific priorities in accumulation strategy.
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15
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Rmus M, Pan TF, Xia L, Collins AGE. Artificial neural networks for model identification and parameter estimation in computational cognitive models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.14.557793. [PMID: 37767088 PMCID: PMC10521012 DOI: 10.1101/2023.09.14.557793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.
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16
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Griffiths CS, Lebert JM, Sollini J, Bizley JK. Gradient boosted decision trees reveal nuances of auditory discrimination behavior. PLoS Comput Biol 2024; 20:e1011985. [PMID: 38626220 PMCID: PMC11051626 DOI: 10.1371/journal.pcbi.1011985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 04/26/2024] [Accepted: 03/09/2024] [Indexed: 04/18/2024] Open
Abstract
Animal psychophysics can generate rich behavioral datasets, often comprised of many 1000s of trials for an individual subject. Gradient-boosted models are a promising machine learning approach for analyzing such data, partly due to the tools that allow users to gain insight into how the model makes predictions. We trained ferrets to report a target word's presence, timing, and lateralization within a stream of consecutively presented non-target words. To assess the animals' ability to generalize across pitch, we manipulated the fundamental frequency (F0) of the speech stimuli across trials, and to assess the contribution of pitch to streaming, we roved the F0 from word token to token. We then implemented gradient-boosted regression and decision trees on the trial outcome and reaction time data to understand the behavioral factors behind the ferrets' decision-making. We visualized model contributions by implementing SHAPs feature importance and partial dependency plots. While ferrets could accurately perform the task across all pitch-shifted conditions, our models reveal subtle effects of shifting F0 on performance, with within-trial pitch shifting elevating false alarms and extending reaction times. Our models identified a subset of non-target words that animals commonly false alarmed to. Follow-up analysis demonstrated that the spectrotemporal similarity of target and non-target words rather than similarity in duration or amplitude waveform was the strongest predictor of the likelihood of false alarming. Finally, we compared the results with those obtained with traditional mixed effects models, revealing equivalent or better performance for the gradient-boosted models over these approaches.
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Affiliation(s)
| | - Jules M. Lebert
- Ear Institute, University College London, London, United Kingdom
| | - Joseph Sollini
- Ear Institute, University College London, London, United Kingdom
- Hearing Sciences, University of Nottingham, Nottingham, United Kingdom
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17
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Couto J, Lebreton M, van Maanen L. Specificity and sensitivity of the fixed-point test for binary mixture distributions. Behav Res Methods 2024; 56:2977-2991. [PMID: 37957433 PMCID: PMC11133060 DOI: 10.3758/s13428-023-02244-9] [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] [Accepted: 09/17/2023] [Indexed: 11/15/2023]
Abstract
When two cognitive processes contribute to a behavioral output-each process producing a specific distribution of the behavioral variable of interest-and when the mixture proportion of these two processes varies as a function of an experimental condition, a common density point should be present in the observed distributions of the data across said conditions. In principle, one can statistically test for the presence (or absence) of a fixed point in experimental data to provide evidence in favor of (or against) the presence of a mixture of processes, whose proportions are affected by an experimental manipulation. In this paper, we provide an empirical diagnostic of this test to detect a mixture of processes. We do so using resampling of real experimental data under different scenarios, which mimic variations in the experimental design suspected to affect the sensitivity and specificity of the fixed-point test (i.e., mixture proportion, time on task, and sample size). Resampling such scenarios with real data allows us to preserve important features of data which are typically observed in real experiments while maintaining tight control over the properties of the resampled scenarios. This is of particular relevance considering such stringent assumptions underlying the fixed-point test. With this paper, we ultimately aim at validating the fixed-point property of binary mixture data and at providing some performance metrics to researchers aiming at testing the fixed-point property on their experimental data.
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Affiliation(s)
- Joaquina Couto
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
- Department of Experimental Psychology, Utrecht University, Utrecht, Netherlands.
| | - Maël Lebreton
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
- Paris School of Economics, Paris, France
| | - Leendert van Maanen
- Department of Experimental Psychology, Utrecht University, Utrecht, Netherlands
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18
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Paunov A, L'Hôtellier M, Guo D, He Z, Yu A, Meyniel F. Multiple and subject-specific roles of uncertainty in reward-guided decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587016. [PMID: 38585958 PMCID: PMC10996615 DOI: 10.1101/2024.03.27.587016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Decision-making in noisy, changing, and partially observable environments entails a basic tradeoff between immediate reward and longer-term information gain, known as the exploration-exploitation dilemma. Computationally, an effective way to balance this tradeoff is by leveraging uncertainty to guide exploration. Yet, in humans, empirical findings are mixed, from suggesting uncertainty-seeking to indifference and avoidance. In a novel bandit task that better captures uncertainty-driven behavior, we find multiple roles for uncertainty in human choices. First, stable and psychologically meaningful individual differences in uncertainty preferences actually range from seeking to avoidance, which can manifest as null group-level effects. Second, uncertainty modulates the use of basic decision heuristics that imperfectly exploit immediate rewards: a repetition bias and win-stay-lose-shift heuristic. These heuristics interact with uncertainty, favoring heuristic choices under higher uncertainty. These results, highlighting the rich and varied structure of reward-based choice, are a step to understanding its functional basis and dysfunction in psychopathology.
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Affiliation(s)
- Alexander Paunov
- INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris, Paris, France
- Institut de Neuromodulation, GHU Paris, Psychiatrie et Neurosciences, Centre Hospitalier Sainte-Anne, Pôle Hospitalo-universitaire 15, Université Paris Cité, Paris, France
| | - Maëva L'Hôtellier
- INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris, Paris, France
| | - Dalin Guo
- Department of Cognitive Science, University of California San Diego, San Diego, CA, USA
| | - Zoe He
- Department of Cognitive Science, University of California San Diego, San Diego, CA, USA
| | - Angela Yu
- Department of Cognitive Science, University of California San Diego, San Diego, CA, USA
- Centre for Cognitive Science & Hessian AI Center, Technical University of Darmstadt, Germany
| | - Florent Meyniel
- INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris, Paris, France
- Institut de Neuromodulation, GHU Paris, Psychiatrie et Neurosciences, Centre Hospitalier Sainte-Anne, Pôle Hospitalo-universitaire 15, Université Paris Cité, Paris, France
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19
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Venditto SJC, Miller KJ, Brody CD, Daw ND. Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.28.582617. [PMID: 38464244 PMCID: PMC10925334 DOI: 10.1101/2024.02.28.582617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step,reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.
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20
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Maggi S, Hock RM, O'Neill M, Buckley M, Moran PM, Bast T, Sami M, Humphries MD. Tracking subjects' strategies in behavioural choice experiments at trial resolution. eLife 2024; 13:e86491. [PMID: 38426402 PMCID: PMC10959529 DOI: 10.7554/elife.86491] [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: 01/29/2023] [Accepted: 02/23/2024] [Indexed: 03/02/2024] Open
Abstract
Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here, we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. Both when subjects learn and when rules change the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that subjects have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.
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Affiliation(s)
- Silvia Maggi
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
| | - Rebecca M Hock
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
| | - Martin O'Neill
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
- Department of Health & Nutritional Sciences, Atlantic Technological UniversitySligoIreland
| | - Mark Buckley
- Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Paula M Moran
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
- Department of Neuroscience, University of NottinghamNottinghamUnited Kingdom
| | - Tobias Bast
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
- Department of Neuroscience, University of NottinghamNottinghamUnited Kingdom
| | - Musa Sami
- Institute of Mental Health, University of NottinghamNottinghamUnited Kingdom
| | - Mark D Humphries
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
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21
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Colas JT, O’Doherty JP, Grafton ST. Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. PLoS Comput Biol 2024; 20:e1011950. [PMID: 38552190 PMCID: PMC10980507 DOI: 10.1371/journal.pcbi.1011950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/26/2024] [Indexed: 04/01/2024] Open
Abstract
Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.
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Affiliation(s)
- Jaron T. Colas
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, California, United States of America
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America
- Computation and Neural Systems Program, California Institute of Technology, Pasadena, California, United States of America
| | - John P. O’Doherty
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America
- Computation and Neural Systems Program, California Institute of Technology, Pasadena, California, United States of America
| | - Scott T. Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, California, United States of America
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22
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Hulsey D, Zumwalt K, Mazzucato L, McCormick DA, Jaramillo S. Decision-making dynamics are predicted by arousal and uninstructed movements. Cell Rep 2024; 43:113709. [PMID: 38280196 PMCID: PMC11016285 DOI: 10.1016/j.celrep.2024.113709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/05/2023] [Accepted: 01/10/2024] [Indexed: 01/29/2024] Open
Abstract
During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, using computational modeling of visual and auditory task performance data from mice, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance.
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Affiliation(s)
- Daniel Hulsey
- Institute of Neuroscience, University of Oregon, Eugene, OR 97405, USA
| | - Kevin Zumwalt
- Institute of Neuroscience, University of Oregon, Eugene, OR 97405, USA
| | - Luca Mazzucato
- Institute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA; Departments of Physics and Mathematics, University of Oregon, Eugene, OR 97405, USA.
| | - David A McCormick
- Institute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA.
| | - Santiago Jaramillo
- Institute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA.
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23
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Jha A, Ashwood ZC, Pillow JW. Active Learning for Discrete Latent Variable Models. Neural Comput 2024; 36:437-474. [PMID: 38363661 DOI: 10.1162/neco_a_01646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/13/2023] [Indexed: 02/18/2024]
Abstract
Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of linear regressions (MLR). While it is well known that active learning confers no advantage for linear-gaussian regression models, we use Fisher information to show analytically that active learning can nevertheless achieve large gains for mixtures of such models, and we validate this improvement using both simulations and real-world data. We then consider a powerful class of temporally structured latent variable models given by a hidden Markov model (HMM) with generalized linear model (GLM) observations, which has recently been used to identify discrete states from animal decision-making data. We show that our method substantially reduces the amount of data needed to fit GLM-HMMs and outperforms a variety of approximate methods based on variational and amortized inference. Infomax learning for latent variable models thus offers a powerful approach for characterizing temporally structured latent states, with a wide variety of applications in neuroscience and beyond.
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Affiliation(s)
- Aditi Jha
- Princeton Neuroscience Institute and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Zoe C Ashwood
- Princeton Neuroscience Institute and Department of Computer Science, Princeton University, Princeton, NJ 08544, U.S.A.
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, U.S.A.
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24
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Li JJ, Shi C, Li L, Collins AGE. Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.19.545524. [PMID: 38328176 PMCID: PMC10849494 DOI: 10.1101/2023.06.19.545524] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the ϵ -softmax policy). However, this assumption is not guaranteed to hold - for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically infer the levels of noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (e.g., fully engaged and random). Using simulations, we show that modeling noise levels dynamically instead of statically can substantially improve model fit and parameter estimation, especially in the presence of long periods of noisy behavior, such as prolonged attentional lapses. We further demonstrate the empirical benefits of dynamic noise estimation at the individual and group levels by validating it on four published datasets featuring diverse populations, tasks, and models. Based on the theoretical and empirical evaluation of the method reported in the current work, we expect that dynamic noise estimation will improve modeling in many decision-making paradigms over the static noise estimation method currently used in the modeling literature, while keeping additional model complexity and assumptions minimal.
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Affiliation(s)
- Jing-Jing Li
- Helen Wills Neuroscience Institute, University of California, Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, United States
| | - Chengchun Shi
- Department of Statistics, London School of Economics and Political Science, 69 Aldwych, London, WC2B 4RR, United Kingdom
| | - Lexin Li
- Helen Wills Neuroscience Institute, University of California, Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, United States
- Department of Biostatistics and Epidemiology, University of California, Berkeley, 2121 Berkeley Way, Berkeley, 94720, CA, United States
| | - Anne G E Collins
- Helen Wills Neuroscience Institute, University of California, Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, United States
- Department of Psychology, University of California, Berkeley, Berkeley, 94720, CA, United States
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25
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Gupta D, DePasquale B, Kopec CD, Brody CD. Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making. Nat Commun 2024; 15:662. [PMID: 38253526 PMCID: PMC10803295 DOI: 10.1038/s41467-024-44880-5] [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: 01/22/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, previous work has suggested that they covary in their prevalence and that their proposed neural substrates overlap. Here we demonstrate that during decision-making, history biases and apparent lapses can both arise from a common cognitive process that is optimal under mistaken beliefs that the world is changing i.e. nonstationary. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model's predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct decision-making datasets of male rats, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.
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Affiliation(s)
- Diksha Gupta
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Sainsbury Wellcome Centre, University College London, London, UK.
| | - Brian DePasquale
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Charles D Kopec
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA.
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26
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Ger Y, Nachmani E, Wolf L, Shahar N. Harnessing the flexibility of neural networks to predict dynamic theoretical parameters underlying human choice behavior. PLoS Comput Biol 2024; 20:e1011678. [PMID: 38175848 DOI: 10.1371/journal.pcbi.1011678] [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: 04/24/2023] [Revised: 01/17/2024] [Accepted: 11/12/2023] [Indexed: 01/06/2024] Open
Abstract
Reinforcement learning (RL) models are used extensively to study human behavior. These rely on normative models of behavior and stress interpretability over predictive capabilities. More recently, neural network models have emerged as a descriptive modeling paradigm that is capable of high predictive power yet with limited interpretability. Here, we seek to augment the expressiveness of theoretical RL models with the high flexibility and predictive power of neural networks. We introduce a novel framework, which we term theoretical-RNN (t-RNN), whereby a recurrent neural network is trained to predict trial-by-trial behavior and to infer theoretical RL parameters using artificial data of RL agents performing a two-armed bandit task. In three studies, we then examined the use of our approach to dynamically predict unseen behavior along with time-varying theoretical RL parameters. We first validate our approach using synthetic data with known RL parameters. Next, as a proof-of-concept, we applied our framework to two independent datasets of humans performing the same task. In the first dataset, we describe differences in theoretical RL parameters dynamic among clinical psychiatric vs. healthy controls. In the second dataset, we show that the exploration strategies of humans varied dynamically in response to task phase and difficulty. For all analyses, we found better performance in the prediction of actions for t-RNN compared to the stationary maximum-likelihood RL method. We discuss the use of neural networks to facilitate the estimation of latent RL parameters underlying choice behavior.
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Affiliation(s)
- Yoav Ger
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Eliya Nachmani
- School of Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel
- Meta AI Research, Tel-Aviv, Israel
| | - Lior Wolf
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Nitzan Shahar
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
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27
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Song CY, Shanechi MM. Unsupervised learning of stationary and switching dynamical system models from Poisson observations. J Neural Eng 2023; 20:066029. [PMID: 38083862 PMCID: PMC10714100 DOI: 10.1088/1741-2552/ad038d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/15/2023] [Accepted: 10/16/2023] [Indexed: 12/18/2023]
Abstract
Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases, reliable regime labels are not available, thus demanding accurate unsupervised learning methods for Poisson observations. Existing learning methods, however, rely on inference of latent states in neural activity using the Laplace approximation, which may not capture the broader properties of densities and may lead to inaccurate learning. Thus, there is a need for new inference methods that can enable accurate model learning.Approach. To achieve accurate model learning, we derive a novel inference method based on deterministic sampling for Poisson observations called the Poisson Cubature Filter (PCF) and embed it in an unsupervised learning framework. This method takes a minimum mean squared error approach to estimation. Terms that are difficult to find analytically for Poisson observations are approximated in a novel way with deterministic sampling based on numerical integration and cubature rules.Main results. PCF enabled accurate unsupervised learning in both stationary and switching dynamical systems and largely outperformed prior Laplace approximation-based learning methods in both simulations and motor cortical spiking data recorded during a reaching task. These improvements were larger for smaller data sizes, showing that PCF-based learning was more data efficient and enabled more reliable regime identification. In experimental data and unsupervised with respect to behavior, PCF-based learning uncovered interpretable behavior-relevant regimes unlike prior learning methods.Significance. The developed unsupervised learning methods for switching dynamical systems can accurately uncover latent regimes and states in population spiking activity, with important applications in both basic neuroscience and neurotechnology.
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Affiliation(s)
- Christian Y Song
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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28
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Narasimhan S, Schriver BJ, Wang Q. Adaptive decision-making depends on pupil-linked arousal in rats performing tactile discrimination tasks. J Neurophysiol 2023; 130:1541-1551. [PMID: 37964751 PMCID: PMC11068411 DOI: 10.1152/jn.00309.2022] [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: 07/22/2022] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/16/2023] Open
Abstract
Perceptual decision-making is a dynamic cognitive process and is shaped by many factors, including behavioral state, reward contingency, and sensory environment. To understand the extent to which adaptive behavior in decision-making is dependent on pupil-linked arousal, we trained head-fixed rats to perform perceptual decision-making tasks and systematically manipulated the probability of Go and No-go stimuli while simultaneously measuring their pupil size in the tasks. Our data demonstrated that the animals adaptively modified their behavior in response to the changes in the sensory environment. The response probability to both Go and No-go stimuli decreased as the probability of the Go stimulus being presented decreased. Analyses within the signal detection theory framework showed that while the animals' perceptual sensitivity was invariant, their decision criterion increased as the probability of the Go stimulus decreased. Simulation results indicated that the adaptive increase in the decision criterion will increase possible water rewards during the task. Moreover, the adaptive decision-making is dependent on pupil-linked arousal as the increase in the decision criterion was the largest during low pupil-linked arousal periods. Taken together, our results demonstrated that the rats were able to adjust their decision-making to maximize rewards in the tasks, and that adaptive behavior in perceptual decision-making is dependent on pupil-linked arousal.NEW & NOTEWORTHY Perceptual decision-making is a dynamic cognitive process and is shaped by many factors. However, the extent to which changes in sensory environment result in adaptive decision-making remains poorly understood. Our data provided new experimental evidence demonstrating that the rats were able to adaptively modify their decision criterion to maximize water reward in response to changes in the statistics of the sensory environment. Furthermore, the adaptive decision-making is dependent on pupil-linked arousal.
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Affiliation(s)
- Shreya Narasimhan
- Department of Biomedical Engineering, Columbia University, New York City, New York, United States
| | - Brian J Schriver
- Department of Biomedical Engineering, Columbia University, New York City, New York, United States
| | - Qi Wang
- Department of Biomedical Engineering, Columbia University, New York City, New York, United States
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29
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Weilnhammer V, Stuke H, Standvoss K, Sterzer P. Sensory processing in humans and mice fluctuates between external and internal modes. PLoS Biol 2023; 21:e3002410. [PMID: 38064502 PMCID: PMC10732408 DOI: 10.1371/journal.pbio.3002410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 12/20/2023] [Accepted: 10/30/2023] [Indexed: 12/21/2023] Open
Abstract
Perception is known to cycle through periods of enhanced and reduced sensitivity to external information. Here, we asked whether such slow fluctuations arise as a noise-related epiphenomenon of limited processing capacity or, alternatively, represent a structured mechanism of perceptual inference. Using 2 large-scale datasets, we found that humans and mice alternate between externally and internally oriented modes of sensory analysis. During external mode, perception aligns more closely with the external sensory information, whereas internal mode is characterized by enhanced biases toward perceptual history. Computational modeling indicated that dynamic changes in mode are enabled by 2 interlinked factors: (i) the integration of subsequent inputs over time and (ii) slow antiphase oscillations in the impact of external sensory information versus internal predictions that are provided by perceptual history. We propose that between-mode fluctuations generate unambiguous error signals that enable optimal inference in volatile environments.
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Affiliation(s)
- Veith Weilnhammer
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Charité-Universitätsmedizin Berlin and Max Delbrück Center, Berlin, Germany
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, United States of America
| | - Heiner Stuke
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Charité-Universitätsmedizin Berlin and Max Delbrück Center, Berlin, Germany
| | - Kai Standvoss
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Philipp Sterzer
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
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30
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Breault MS, Sacré P, Fitzgerald ZB, Gale JT, Cullen KE, González-Martínez JA, Sarma SV. Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks. Nat Commun 2023; 14:7837. [PMID: 38030611 PMCID: PMC10687170 DOI: 10.1038/s41467-023-43257-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/03/2023] [Indexed: 12/01/2023] Open
Abstract
Humans' ability to adapt and learn relies on reflecting on past performance. These experiences form latent representations called internal states that induce movement variability that improves how we interact with our environment. Our study uncovered temporal dynamics and neural substrates of two states from ten subjects implanted with intracranial depth electrodes while they performed a goal-directed motor task with physical perturbations. We identified two internal states using state-space models: one tracking past errors and the other past perturbations. These states influenced reaction times and speed errors, revealing how subjects strategize from trial history. Using local field potentials from over 100 brain regions, we found large-scale brain networks such as the dorsal attention and default mode network modulate visuospatial attention based on recent performance and environmental feedback. Notably, these networks were more prominent in higher-performing subjects, emphasizing their role in improving motor performance by regulating movement variability through internal states.
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Affiliation(s)
- Macauley Smith Breault
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Pierre Sacré
- Department of Electrical Engineering and Computer Science, School of Engineering, University of Liège, Liège, Belgium
| | - Zachary B Fitzgerald
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Kathleen E Cullen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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31
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Guo X, Zeng D, Wang Y. A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders. J Am Stat Assoc 2023; 119:27-38. [PMID: 38706706 PMCID: PMC11068237 DOI: 10.1080/01621459.2023.2261184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 09/03/2023] [Indexed: 05/07/2024]
Abstract
Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to characterize the reward-based decision-making of MDD patients. The model assumes that a subject's decision-making process is updated based on a reward prediction error weighted by the subject-specific learning rate. To account for the fact that one favors a decision leading to a potentially high reward, but this decision process is not necessarily linear, we model reward sensitivity with a non-decreasing and nonlinear function. For inference, we estimate the latter via approximation by I-splines and then maximize the joint conditional log-likelihood. We show that the resulting estimators are consistent and asymptotically normal. Through extensive simulation studies, we demonstrate that under different reward-generating distributions, the semiparametric inverse RL outperforms the parametric inverse RL. We apply the proposed method to EMBARC and find that MDD and control groups have similar learning rates but different reward sensitivity functions. There is strong statistical evidence that reward sensitivity functions have nonlinear forms. Using additional brain imaging data in the same study, we find that both reward sensitivity and learning rate are associated with brain activities in the negative affect circuitry under an emotional conflict task.
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Affiliation(s)
- Xingche Guo
- Department of Biostatistics, Columbia University
| | - Donglin Zeng
- Department of Biostatistics, University of Michigan
| | - Yuanjia Wang
- Departments of Biostatistics and Psychiatry, Columbia University
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32
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Nakuci J, Samaha J, Rahnev D. Brain signatures indexing variation in internal processing during perceptual decision-making. iScience 2023; 26:107750. [PMID: 37727738 PMCID: PMC10505979 DOI: 10.1016/j.isci.2023.107750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 06/29/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023] Open
Abstract
Brain activity is highly variable during a task. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimental manipulations. However, changes in internal processing could arise from many factors independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials. Subjects (N = 25) performed a motion discrimination task with six interleaved levels of coherence. Clustering identified two discrete subtypes of trials with different patterns of activity. Surprisingly, Subtype 1 occurred more frequently in trials with lower motion coherence but was associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight across-trial variability in decision processes traditionally hidden to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.
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Affiliation(s)
- Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jason Samaha
- Department of Psychology, The University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332, USA
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33
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Boucher PO, Wang T, Carceroni L, Kane G, Shenoy KV, Chandrasekaran C. Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex. Nat Commun 2023; 14:6510. [PMID: 37845221 PMCID: PMC10579235 DOI: 10.1038/s41467-023-41752-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/18/2023] [Indexed: 10/18/2023] Open
Abstract
We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and fast initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial conditions combine with sensory evidence to induce decision-related dynamics in PMd.
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Affiliation(s)
- Pierre O Boucher
- Department of Biomedical Engineering, Boston University, Boston, 02115, MA, USA
| | - Tian Wang
- Department of Biomedical Engineering, Boston University, Boston, 02115, MA, USA
| | - Laura Carceroni
- Undergraduate Program in Neuroscience, Boston University, Boston, 02115, MA, USA
| | - Gary Kane
- Department of Psychological and Brain Sciences, Boston University, Boston, 02115, MA, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, 94305, CA, USA
- Department of Neurobiology, Stanford University, Stanford, 94305, CA, USA
- Howard Hughes Medical Institute, HHMI, Chevy Chase, 20815-6789, MD, USA
- Department of Bioengineering, Stanford University, Stanford, 94305, CA, USA
- Stanford Neurosciences Institute, Stanford University, Stanford, 94305, CA, USA
- Bio-X Program, Stanford University, Stanford, 94305, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, 94305, CA, USA
| | - Chandramouli Chandrasekaran
- Department of Biomedical Engineering, Boston University, Boston, 02115, MA, USA.
- Department of Psychological and Brain Sciences, Boston University, Boston, 02115, MA, USA.
- Center for Systems Neuroscience, Boston University, Boston, 02115, MA, USA.
- Department of Anatomy & Neurobiology, Boston University, Boston, 02118, MA, USA.
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34
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Böing S, Fabius JH, Hakkenberg M, Nijboer TCW, Van der Stigchel S. More (corrective) consecutive saccades after a lesion to the posterior parietal cortex. Eur J Neurosci 2023; 58:3650-3670. [PMID: 37605452 DOI: 10.1111/ejn.16121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
To reach a target, primary saccades (S1s) are often followed by (corrective) consecutive saccades (S2, and potentially S3, S4, S5), which are based on retinal and extraretinal feedback. Processing these extraretinal signals was found to be significantly impaired by lesions to the posterior parietal cortex (PPC). Recent studies, however, added a more nuanced view to the role of the PPC, where patients with PPC lesions still used extraretinal signals for S2s and perceptual judgements (Fabius et al., 2020; Rath-Wilson & Guitton, 2015). Hence, it seems that a PPC lesion is not disrupting extraretinal processing per se. Yet, a lesion might still result in less reliable processing of extraretinal signals. Here, we investigated whether this lower reliability manifests as decreased or delayed S2 initiation. Patients with PPC lesions (n = 7) and controls (n = 26) performed a prosaccade task where the target either remained visible or was removed after S1 onset. When S1 is removed, accurate S2s (corrections of S1 error) rely solely on extraretinal signals. We analysed S2 quantity and timing using linear mixed-effects modelling and additive hazards analyses. Patients demonstrated slower S1 execution and lower S1 amplitudes than controls, but their S2s still compensated the S1 undershoot, also when they only relied on extraretinal information. Surprisingly, patients showed an increased amount of S2s. This deviation from control behaviour can be seen as suboptimal, but given the decreased accuracy of the primary saccade, it could be optimal for patients to employ more (corrective) consecutive saccades to overcome this inaccuracy.
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Affiliation(s)
- Sanne Böing
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands
| | - Jasper H Fabius
- Institute of Neuroscience & Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland, UK
| | - Marjoleine Hakkenberg
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands
| | - Tanja C W Nijboer
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands
- Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht and De Hoogstraat Rehabilitation, Utrecht, the Netherlands
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35
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Kane GA, Senne RA, Scott BB. Rat movements reflect internal decision dynamics in an evidence accumulation task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.556575. [PMID: 37745309 PMCID: PMC10515875 DOI: 10.1101/2023.09.11.556575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Perceptual decision-making involves multiple cognitive processes, including accumulation of sensory evidence, planning, and executing a motor action. How these processes are intertwined is unclear; some models assume that decision-related processes precede motor execution, whereas others propose that movements reflecting on-going decision processes occur before commitment to a choice. Here we develop and apply two complementary methods to study the relationship between decision processes and the movements leading up to a choice. The first is a free response pulse-based evidence accumulation task, in which stimuli continue until choice is reported. The second is a motion-based drift diffusion model (mDDM), in which movement variables from video pose estimation constrain decision parameters on a trial-by-trial basis. We find the mDDM provides a better model fit to rats' decisions in the free response accumulation task than traditional DDM models. Interestingly, on each trial we observed a period of time, prior to choice, that was characterized by head immobility. The length of this period was positively correlated with the rats' decision bounds and stimuli presented during this period had the greatest impact on choice. Together these results support a model in which internal decision dynamics are reflected in movements and demonstrate that inclusion of movement parameters improves the performance of diffusion-to-bound decision models.
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Affiliation(s)
- Gary A. Kane
- Department of Psychological and Brain Sciences and Center for Systems Neuroscience, Boston University, Boston MA
| | - Ryan A. Senne
- Graduate Program in Neuroscience, Boston University, Boston MA
| | - Benjamin B. Scott
- Department of Psychological and Brain Sciences and Center for Systems Neuroscience, Boston University, Boston MA
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36
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de Vries SEJ, Siegle JH, Koch C. Sharing neurophysiology data from the Allen Brain Observatory. eLife 2023; 12:e85550. [PMID: 37432073 PMCID: PMC10335829 DOI: 10.7554/elife.85550] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/27/2023] [Indexed: 07/12/2023] Open
Abstract
Nullius in verba ('trust no one'), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative claims-are a defining feature of empirical science. As the complexity of modern scientific instrumentation has made exact replications prohibitive, sharing data is now essential for ensuring the trustworthiness of one's findings. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. Data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these.
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37
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Moore S, Wang Z, Zhu Z, Sun R, Lee A, Charles A, Kuchibhotla KV. Revealing abrupt transitions from goal-directed to habitual behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547783. [PMID: 37461576 PMCID: PMC10349993 DOI: 10.1101/2023.07.05.547783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
A fundamental tenet of animal behavior is that decision-making involves multiple 'controllers.' Initially, behavior is goal-directed, driven by desired outcomes, shifting later to habitual control, where cues trigger actions independent of motivational state. Clark Hull's question from 1943 still resonates today: "Is this transition abrupt, or is it gradual and progressive?"1 Despite a century-long belief in gradual transitions, this question remains unanswered2,3 as current methods cannot disambiguate goal-directed versus habitual control in real-time. Here, we introduce a novel 'volitional engagement' approach, motivating animals by palatability rather than biological need. Offering less palatable water in the home cage4,5 reduced motivation to 'work' for plain water in an auditory discrimination task when compared to water-restricted animals. Using quantitative behavior and computational modeling6, we found that palatability-driven animals learned to discriminate as quickly as water-restricted animals but exhibited state-like fluctuations when responding to the reward-predicting cue-reflecting goal-directed behavior. These fluctuations spontaneously and abruptly ceased after thousands of trials, with animals now always responding to the reward-predicting cue. In line with habitual control, post-transition behavior displayed motor automaticity, decreased error sensitivity (assessed via pupillary responses), and insensitivity to outcome devaluation. Bilateral lesions of the habit-related dorsolateral striatum7 blocked transitions to habitual behavior. Thus, 'volitional engagement' reveals spontaneous and abrupt transitions from goal-directed to habitual behavior, suggesting the involvement of a higher-level process that arbitrates between the two.
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Affiliation(s)
- Sharlen Moore
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Zyan Wang
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Ziyi Zhu
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Ruolan Sun
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Angel Lee
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Adam Charles
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kishore V. Kuchibhotla
- Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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38
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Nakuci J, Samaha J, Rahnev D. Brain signatures indexing variation in internal processing during perceptual decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.10.523502. [PMID: 36711566 PMCID: PMC9882071 DOI: 10.1101/2023.01.10.523502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Brain activity is highly variable even while performing the same cognitive task with consequences for performance. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimentally inducing changes (e.g., via attention manipulation) to identify neuronal and behavioral consequences or studying spontaneous changes in ongoing brain dynamics. However, changes in internal processing could arise from many factors, such as variation in strategy or arousal, that are independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials and relate this activity to behavioral performance. Subjects (N = 25) performed a motion direction discrimination task with six interleaved levels of motion coherence. Modularity-maximization based clustering identified two discrete spatial-temporal clusters, or subtypes, of trials with different patterns of brain activity. Surprisingly, even though Subtype 1 occurred more frequently with lower motion coherence, it was nonetheless associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight trial-to-trial variability in decision processes usually masked to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.
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39
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Talley J, Pusdekar S, Feltenberger A, Ketner N, Evers J, Liu M, Gosh A, Palmer SE, Wardill TJ, Gonzalez-Bellido PT. Predictive saccades and decision making in the beetle-predating saffron robber fly. Curr Biol 2023:S0960-9822(23)00770-4. [PMID: 37379842 DOI: 10.1016/j.cub.2023.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 04/28/2023] [Accepted: 06/06/2023] [Indexed: 06/30/2023]
Abstract
Internal predictions about the sensory consequences of self-motion, encoded by corollary discharge, are ubiquitous in the animal kingdom, including for fruit flies, dragonflies, and humans. In contrast, predicting the future location of an independently moving external target requires an internal model. With the use of internal models for predictive gaze control, vertebrate predatory species compensate for their sluggish visual systems and long sensorimotor latencies. This ability is crucial for the timely and accurate decisions that underpin a successful attack. Here, we directly demonstrate that the robber fly Laphria saffrana, a specialized beetle predator, also uses predictive gaze control when head tracking potential prey. Laphria uses this predictive ability to perform the difficult categorization and perceptual decision task of differentiating a beetle from other flying insects with a low spatial resolution retina. Specifically, we show that (1) this predictive behavior is part of a saccade-and-fixate strategy, (2) the relative target angular position and velocity, acquired during fixation, inform the subsequent predictive saccade, and (3) the predictive saccade provides Laphria with additional fixation time to sample the frequency of the prey's specular wing reflections. We also demonstrate that Laphria uses such wing reflections as a proxy for the wingbeat frequency of the potential prey and that consecutively flashing LEDs to produce apparent motion elicits attacks when the LED flicker frequency matches that of the beetle's wingbeat cycle.
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Affiliation(s)
- Jennifer Talley
- Air Force Research Laboratory, Munitions Directorate, Eglin AFB, FL 32542, USA.
| | - Siddhant Pusdekar
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108, USA
| | - Aaron Feltenberger
- Air Force Research Laboratory, Munitions Directorate, Eglin AFB, FL 32542, USA
| | - Natalie Ketner
- Air Force Research Laboratory, Munitions Directorate, Eglin AFB, FL 32542, USA
| | - Johnny Evers
- Air Force Research Laboratory, Munitions Directorate, Eglin AFB, FL 32542, USA
| | - Molly Liu
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108, USA
| | - Atishya Gosh
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108, USA; Department of Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Stephanie E Palmer
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL 60637, USA
| | - Trevor J Wardill
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108, USA; Department of Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Paloma T Gonzalez-Bellido
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN 55108, USA; Department of Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA.
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40
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Kay K, Bonnen K, Denison RN, Arcaro MJ, Barack DL. Tasks and their role in visual neuroscience. Neuron 2023; 111:1697-1713. [PMID: 37040765 DOI: 10.1016/j.neuron.2023.03.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/13/2023] [Accepted: 03/15/2023] [Indexed: 04/13/2023]
Abstract
Vision is widely used as a model system to gain insights into how sensory inputs are processed and interpreted by the brain. Historically, careful quantification and control of visual stimuli have served as the backbone of visual neuroscience. There has been less emphasis, however, on how an observer's task influences the processing of sensory inputs. Motivated by diverse observations of task-dependent activity in the visual system, we propose a framework for thinking about tasks, their role in sensory processing, and how we might formally incorporate tasks into our models of vision.
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Affiliation(s)
- Kendrick Kay
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Kathryn Bonnen
- School of Optometry, Indiana University, Bloomington, IN 47405, USA
| | - Rachel N Denison
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Mike J Arcaro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19146, USA
| | - David L Barack
- Departments of Neuroscience and Philosophy, University of Pennsylvania, Philadelphia, PA 19146, USA
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Cazettes F, Mazzucato L, Murakami M, Morais JP, Augusto E, Renart A, Mainen ZF. A reservoir of foraging decision variables in the mouse brain. Nat Neurosci 2023; 26:840-849. [PMID: 37055628 PMCID: PMC10280691 DOI: 10.1038/s41593-023-01305-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 03/15/2023] [Indexed: 04/15/2023]
Abstract
In any given situation, the environment can be parsed in different ways to yield decision variables (DVs) defining strategies useful for different tasks. It is generally presumed that the brain only computes a single DV defining the current behavioral strategy. Here to test this assumption, we recorded neural ensembles in the frontal cortex of mice performing a foraging task admitting multiple DVs. Methods developed to uncover the currently employed DV revealed the use of multiple strategies and occasional switches in strategy within sessions. Optogenetic manipulations showed that the secondary motor cortex (M2) is needed for mice to use the different DVs in the task. Surprisingly, we found that regardless of which DV best explained the current behavior, M2 activity concurrently encoded a full basis set of computations defining a reservoir of DVs appropriate for alternative tasks. This form of neural multiplexing may confer considerable advantages for learning and adaptive behavior.
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Affiliation(s)
| | - Luca Mazzucato
- Departments of Biology, Mathematics & Physics, Institute of Neuroscience, University of Oregon, Eugene, OR, USA
| | - Masayoshi Murakami
- Champalimaud Foundation, Lisbon, Portugal
- Department of Neurophysiology, University of Yamanashi, Yamanashi, Japan
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42
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Hulsey D, Zumwalt K, Mazzucato L, McCormick DA, Jaramillo S. Decision-making dynamics are predicted by arousal and uninstructed movements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.02.530651. [PMID: 37034793 PMCID: PMC10081205 DOI: 10.1101/2023.03.02.530651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, combining behavioral experiments in mice with computational modeling, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states. Optimal state epochs were predicted by intermediate levels, and reduced variability, of pupil diameter, along with reduced variability in face movements and locomotion. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states, and suggest mice regulate their arousal during optimal performance.
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Affiliation(s)
- Daniel Hulsey
- Institute of Neuroscience, University of Oregon, Eugene, OR, USA
| | - Kevin Zumwalt
- Institute of Neuroscience, University of Oregon, Eugene, OR, USA
| | - Luca Mazzucato
- Institute of Neuroscience, University of Oregon, Eugene, OR, USA
- Department of Biology, University of Oregon, Eugene, OR, USA
- Departments of Physics and Mathematics, University of Oregon, Eugene, OR, USA
| | - David A. McCormick
- Institute of Neuroscience, University of Oregon, Eugene, OR, USA
- Department of Biology, University of Oregon, Eugene, OR, USA
| | - Santiago Jaramillo
- Institute of Neuroscience, University of Oregon, Eugene, OR, USA
- Department of Biology, University of Oregon, Eugene, OR, USA
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43
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Abstract
Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.
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Affiliation(s)
- Gouki Okazawa
- Center for Neural Science, New York University, New York, NY, USA;
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY, USA;
- Department of Psychology, New York University, New York, NY, USA
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44
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Nogueira R, Rodgers CC, Bruno RM, Fusi S. The geometry of cortical representations of touch in rodents. Nat Neurosci 2023; 26:239-250. [PMID: 36624277 DOI: 10.1038/s41593-022-01237-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/16/2022] [Indexed: 01/11/2023]
Abstract
Neurons often encode highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects touched by their whiskers. High-speed videos of the whiskers revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects non-linear integration of spatio-temporal features of the sensory inputs. Although the responses at first appeared disorganized, we identified an interesting structure in the representational geometry: different whisker contacts are disentangled variables represented in approximately, but not fully, orthogonal subspaces of the neural activity space. This geometry allows linear readouts to perform a broad class of tasks of different complexities without compromising the ability to generalize to novel situations.
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Affiliation(s)
- Ramon Nogueira
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Department of Neuroscience, Columbia University, New York, NY, USA.
| | - Chris C Rodgers
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - Randy M Bruno
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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45
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Gupta D, DePasquale B, Kopec CD, Brody CD. Trial-history biases in evidence accumulation can give rise to apparent lapses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524599. [PMID: 36778392 PMCID: PMC9915493 DOI: 10.1101/2023.01.18.524599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, several hints in the literature suggest that they covary in their prevalence and that their proposed neural substrates overlap - what could underlie these links? Here we demonstrate that history biases and apparent lapses can both arise from a common cognitive process that is normative under misbeliefs about non-stationarity in the world. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model's predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct rat decision-making datasets, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.
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Affiliation(s)
- Diksha Gupta
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
| | - Brian DePasquale
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
| | - Charles D Kopec
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Howard Hughes Medical Institute, Princeton University, Princeton, United States
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46
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Probing top-down information in neocortical layer 1. Trends Neurosci 2023; 46:20-31. [PMID: 36428192 DOI: 10.1016/j.tins.2022.11.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/26/2022] [Accepted: 11/01/2022] [Indexed: 11/23/2022]
Abstract
Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally generated top-down information. Decades of work have elucidated how sensory neocortex processes physical stimulus features. By contrast, examining how top-down information is encoded and integrated with bottom-up signals has been challenging using traditional neuroscience methods. Recent technological advances in functional imaging of brain-wide afferents in behaving mice have enabled the direct measurement of top-down information. Here, we review the emerging literature on encoding of these internally generated signals by different projection systems enriched in neocortical layer 1 during defined brain functions, including memory, attention, and predictive coding. Moreover, we identify gaps in current knowledge and highlight future directions for this rapidly advancing field.
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47
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Shoenhard H, Jain RA, Granato M. The calcium-sensing receptor (CaSR) regulates zebrafish sensorimotor decision making via a genetically defined cluster of hindbrain neurons. Cell Rep 2022; 41:111790. [PMID: 36476852 PMCID: PMC9813870 DOI: 10.1016/j.celrep.2022.111790] [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: 05/04/2022] [Revised: 09/21/2022] [Accepted: 11/15/2022] [Indexed: 12/12/2022] Open
Abstract
Decision making is a fundamental nervous system function that ranges widely in complexity and speed of execution. We previously established larval zebrafish as a model for sensorimotor decision making and identified the G-protein-coupled calcium-sensing receptor (CaSR) to be critical for this process. Here, we report that CaSR functions in neurons to dynamically regulate the bias between two behavioral outcomes: escapes and reorientations. By employing a computational guided transgenic strategy, we identify a genetically defined neuronal cluster in the hindbrain as a key candidate site for CaSR function. Finally, we demonstrate that transgenic CaSR expression targeting this cluster consisting of a few hundred neurons shifts behavioral bias in wild-type animals and restores decision making deficits in CaSR mutants. Combined, our data provide a rare example of a G-protein-coupled receptor that biases vertebrate sensorimotor decision making via a defined neuronal cluster.
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Affiliation(s)
- Hannah Shoenhard
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Roshan A. Jain
- Department of Biology, Haverford College, Haverford, PA 19041, USA
| | - Michael Granato
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Lead contact,Correspondence:
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48
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Drevet J, Drugowitsch J, Wyart V. Efficient stabilization of imprecise statistical inference through conditional belief updating. Nat Hum Behav 2022; 6:1691-1704. [PMID: 36138224 DOI: 10.1038/s41562-022-01445-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 08/11/2022] [Indexed: 01/14/2023]
Abstract
Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behaviour. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence and explains human behaviour better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations but also, counterintuitively, increases the accuracy of resulting decisions.
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Affiliation(s)
- Julie Drevet
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.
- Département d'Études Cognitives, École Normale Supérieure, Université PSL, Paris, France.
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.
- Département d'Études Cognitives, École Normale Supérieure, Université PSL, Paris, France.
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49
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Moore S, Kuchibhotla KV. Slow or sudden: Re-interpreting the learning curve for modern systems neuroscience. IBRO Neurosci Rep 2022; 13:9-14. [PMID: 35669385 PMCID: PMC9163689 DOI: 10.1016/j.ibneur.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 10/27/2022] Open
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50
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Song CY, Hsieh HL, Pesaran B, Shanechi MM. Modeling and inference methods for switching regime-dependent dynamical systems with multiscale neural observations. J Neural Eng 2022; 19. [PMID: 36261030 DOI: 10.1088/1741-2552/ac9b94] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 10/19/2022] [Indexed: 01/11/2023]
Abstract
Objective.Realizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases.Approach.Here, we address both challenges by developing a switching multiscale dynamical system model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity.Main results.We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show that these methods can successfully combine the spiking and field potential observations to simultaneously track the regime and brain states accurately. Doing so, these methods lead to better state estimation compared with single-scale switching methods or stationary multiscale methods. Also, for single-scale linear Gaussian observations, the new switching smoother can better generalize to diverse system settings compared to prior switching smoothers.Significance.These modeling and inference methods effectively incorporate both regime-detection and multiscale observations. As such, they could facilitate investigation of latent switching neural population dynamics and improve future brain-machine interfaces by enabling inference in naturalistic scenarios where regime-dependent multiscale activity and behavior arise.
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Affiliation(s)
- Christian Y Song
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Han-Lin Hsieh
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Bijan Pesaran
- Departments of Neurosurgery, Neuroscience, and Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.,Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America.,Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.,Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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