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Hattori R, Hedrick NG, Jain A, Chen S, You H, Hattori M, Choi JH, Lim BK, Yasuda R, Komiyama T. Meta-reinforcement learning via orbitofrontal cortex. Nat Neurosci 2023; 26:2182-2191. [PMID: 37957318 PMCID: PMC10689244 DOI: 10.1038/s41593-023-01485-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 10/06/2023] [Indexed: 11/15/2023]
Abstract
The meta-reinforcement learning (meta-RL) framework, which involves RL over multiple timescales, has been successful in training deep RL models that generalize to new environments. It has been hypothesized that the prefrontal cortex may mediate meta-RL in the brain, but the evidence is scarce. Here we show that the orbitofrontal cortex (OFC) mediates meta-RL. We trained mice and deep RL models on a probabilistic reversal learning task across sessions during which they improved their trial-by-trial RL policy through meta-learning. Ca2+/calmodulin-dependent protein kinase II-dependent synaptic plasticity in OFC was necessary for this meta-learning but not for the within-session trial-by-trial RL in experts. After meta-learning, OFC activity robustly encoded value signals, and OFC inactivation impaired the RL behaviors. Longitudinal tracking of OFC activity revealed that meta-learning gradually shapes population value coding to guide the ongoing behavioral policy. Our results indicate that two distinct RL algorithms with distinct neural mechanisms and timescales coexist in OFC to support adaptive decision-making.
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Affiliation(s)
- Ryoma Hattori
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA.
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA.
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
- Department of Neuroscience, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, Jupiter, FL, USA.
| | - Nathan G Hedrick
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Anant Jain
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | - Shuqi Chen
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Hanjia You
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Mariko Hattori
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Jun-Hyeok Choi
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
| | - Byung Kook Lim
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
| | - Ryohei Yasuda
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | - Takaki Komiyama
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA.
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA.
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
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Danskin BP, Hattori R, Zhang YE, Babic Z, Aoi M, Komiyama T. Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior. SCIENCE ADVANCES 2023; 9:eadj4897. [PMID: 38019904 PMCID: PMC10686558 DOI: 10.1126/sciadv.adj4897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Animals use past experience to guide future choices. The integration of experiences typically follows a hyperbolic, rather than exponential, decay pattern with a heavy tail for distant history. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends. However, it is unknown how the brain implements hyperbolic integration. We found that mouse behavior in a foraging task showed hyperbolic decay of past experience, but the activity of cortical neurons showed exponential decay. We resolved this apparent mismatch by observing that cortical neurons encode history information with heterogeneous exponential time constants that vary across neurons. A model combining these diverse timescales recreated the heavy-tailed, hyperbolic history integration observed in behavior. In particular, the time constants of retrosplenial cortex (RSC) neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced behavioral history dependence. These results indicate that behavior-relevant history information is maintained across multiple timescales in parallel and that RSC is a critical reservoir of information guiding decision-making.
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Affiliation(s)
- Bethanny P. Danskin
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Ryoma Hattori
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Yu E. Zhang
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Zeljana Babic
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Mikio Aoi
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Takaki Komiyama
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
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Nolan SO, Melugin PR, Erickson KR, Adams WR, Farahbakhsh ZZ, Mcgonigle CE, Kwon MH, Costa VD, Lapish CC, Hackett TA, Cuzon Carlson VC, Constantinidis C, Grant KA, Siciliano CA. Recurrent activity within microcircuits of macaque dorsolateral prefrontal cortex tracks cognitive flexibility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.23.559125. [PMID: 38529503 PMCID: PMC10962741 DOI: 10.1101/2023.09.23.559125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Human and non-human primate data clearly implicate the dorsolateral prefrontal cortex (dlPFC) as critical for advanced cognitive functions 1,2 . It is thought that intracortical synaptic architectures within dlPFC are the integral neurobiological substrate that gives rise to these processes, including working memory, inferential reasoning, and decision-making 3-7 . In the prevailing model, each cortical column makes up one fundamental processing unit composed of dense intrinsic connectivity, conceptualized as the 'canonical' cortical microcircuit 3,8 . Each cortical microcircuit receives sensory and cognitive information from a variety of sources which are represented by sustained activity within the microcircuit, referred to as persistent or recurrent activity 4,9 . Via recurrent connections within the microcircuit, activity can propagate for a variable length of time, thereby allowing temporary storage and computations to occur locally before ultimately passing a transformed representation to a downstream output 4,5,10 . Competing theories regarding how microcircuit activity is coordinated have proven difficult to reconcile in vivo where intercortical and intracortical computations cannot be fully dissociated 5,9,11,12 . Here, we interrogated the intrinsic features of isolated microcircuit networks using high-density calcium imaging of macaque dlPFC ex vivo . We found that spontaneous activity is intrinsically maintained by microcircuit architecture, persisting at a high rate in the absence of extrinsic connections. Further, using perisulcal stimulation to evoke persistent activity in deep layers, we found that activity propagates through stochastically assembled intracortical networks, creating predictable population-level events from largely non-overlapping ensembles. Microcircuit excitability covaried with individual cognitive performance, thus anchoring heuristic models of abstract cortical functions within quantifiable constraints imposed by the underlying synaptic architecture.
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Ahanonu B, Crowther A, Kania A, Casillas MR, Basbaum A. Long-term optical imaging of the spinal cord in awake, behaving animals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541477. [PMID: 37292913 PMCID: PMC10245895 DOI: 10.1101/2023.05.22.541477] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Advances in optical imaging approaches and fluorescent biosensors have enabled an understanding of the spatiotemporal and long-term neural dynamics in the brain of awake animals. However, methodological difficulties and the persistence of post-laminectomy fibrosis have greatly limited similar advances in the spinal cord. To overcome these technical obstacles, we combined in vivo application of fluoropolymer membranes that inhibit fibrosis; a redesigned, cost-effective implantable spinal imaging chamber; and improved motion correction methods that together permit imaging of the spinal cord in awake, behaving mice, for months to over a year. We also demonstrate a robust ability to monitor axons, identify a spinal cord somatotopic map, conduct Ca2+ imaging of neural dynamics in behaving animals responding to pain-provoking stimuli, and observe persistent microglial changes after nerve injury. The ability to couple neural activity and behavior at the spinal cord level will drive insights not previously possible at a key location for somatosensory transmission to the brain.
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Affiliation(s)
- Biafra Ahanonu
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA
- These authors contributed equally
| | - Andrew Crowther
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA
- These authors contributed equally
| | - Artur Kania
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, H2W 1R7, Canada
- Department of Cell Biology and Anatomy, and Division of Experimental Medicine, McGill University, Montréal, QC, H3A 2B2, Canada
| | - Mariela Rosa Casillas
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Allan Basbaum
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA
- Lead Contact
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Blaeser AS, Sugden AU, Zhao J, Carneiro-Nascimento S, Shipley FB, Carrié H, Andermann ML, Levy D. Trigeminal afferents sense locomotion-related meningeal deformations. Cell Rep 2022; 41:111648. [PMID: 36384109 PMCID: PMC9713852 DOI: 10.1016/j.celrep.2022.111648] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/24/2022] [Accepted: 10/20/2022] [Indexed: 11/18/2022] Open
Abstract
The trigeminal sensory innervation of the cranial meninges is thought to serve a nociceptive function and mediate headache pain. However, the activity of meningeal afferents under natural conditions in awake animals remains unexplored. Here, we used two- and three-dimensional two-photon calcium imaging to track the activity of meningeal afferent fibers in awake mice. Surprisingly, a large subset of afferents was activated during non-noxious conditions such as locomotion. We estimated locomotion-related meningeal deformations and found afferents with distinct dynamics and tuning to various levels of meningeal expansion, compression, shearing, and Z-axis motion. Further, these mechanosensitive afferents were often tuned to distinct directions of meningeal expansion or compression. Thus, in addition to their role in headache-related pain, meningeal sensory neurons track the dynamic mechanical state of the meninges under natural conditions.
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Affiliation(s)
- Andrew S Blaeser
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Arthur U Sugden
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Jun Zhao
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Simone Carneiro-Nascimento
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Frederick B Shipley
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Hanaé Carrié
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Mark L Andermann
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
| | - Dan Levy
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
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Benisty H, Song A, Mishne G, Charles AS. Review of data processing of functional optical microscopy for neuroscience. NEUROPHOTONICS 2022; 9:041402. [PMID: 35937186 PMCID: PMC9351186 DOI: 10.1117/1.nph.9.4.041402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/15/2022] [Indexed: 05/04/2023]
Abstract
Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.
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Affiliation(s)
- Hadas Benisty
- Yale Neuroscience, New Haven, Connecticut, United States
| | - Alexander Song
- Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | - Gal Mishne
- UC San Diego, Halıcığlu Data Science Institute, Department of Electrical and Computer Engineering and the Neurosciences Graduate Program, La Jolla, California, United States
| | - Adam S. Charles
- Johns Hopkins University, Kavli Neuroscience Discovery Institute, Center for Imaging Science, Department of Biomedical Engineering, Department of Neuroscience, and Mathematical Institute for Data Science, Baltimore, Maryland, United States
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Abstract
Neural activity is heterogeneous across different cortical areas and can change during learning. Here, we describe a protocol for longitudinal in vivo two-photon calcium imaging with an ultra-large cranial window that exposes most of the dorsal cortex in head-fixed mice. The large cranial window allows optical access to any dorsal cortical areas in individual mice. This protocol enables longitudinal tracking of neural activity from various cortical areas at cellular resolution to understand the cortical computations during behavioral tasks. For complete details on the use and execution of this protocol, please refer to Hattori et al. (2019), and Hattori and Komiyama, 2022a.
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Affiliation(s)
- Ryoma Hattori
- Neurobiology Section, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - Takaki Komiyama
- Neurobiology Section, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA
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