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Federman N, Romano SA, Amigo-Duran M, Salomon L, Marin-Burgin A. Acquisition of non-olfactory encoding improves odour discrimination in olfactory cortex. Nat Commun 2024; 15:5572. [PMID: 38956072 PMCID: PMC11220071 DOI: 10.1038/s41467-024-49897-4] [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: 09/20/2023] [Accepted: 06/20/2024] [Indexed: 07/04/2024] Open
Abstract
Olfaction is influenced by contextual factors, past experiences, and the animal's internal state. Whether this information is integrated at the initial stages of cortical odour processing is not known, nor how these signals may influence odour encoding. Here we revealed multiple and diverse non-olfactory responses in the primary olfactory (piriform) cortex (PCx), which dynamically enhance PCx odour discrimination according to behavioural demands. We performed recordings of PCx neurons from mice trained in a virtual reality task to associate odours with visual contexts to obtain a reward. We found that learning shifts PCx activity from encoding solely odours to a regime in which positional, contextual, and associative responses emerge on odour-responsive neurons that become mixed-selective. The modulation of PCx activity by these non-olfactory signals was dynamic, improving odour decoding during task engagement and in rewarded contexts. This improvement relied on the acquired mixed-selectivity, demonstrating how integrating extra-sensory inputs in sensory cortices can enhance sensory processing while encoding the behavioural relevance of stimuli.
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Grants
- 108878 Canadian International Development Agency (Agence Canadienne de Développement International)
- PICT2018-0880 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2020-0360 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2020-1536 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2016-2758 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PICT2017-4023 Ministry of Science, Technology and Productive Innovation, Argentina | National Agency for Science and Technology, Argentina | Fondo para la Investigación Científica y Tecnológica (Fund for Scientific and Technological Research)
- PIP2787 Ministerio de Ciencia, Tecnología e Innovación Productiva (Ministry of Science, Technology and Productive Innovation, Argentina)
- SPIRIT 216044 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
- Fondo para la convergencia estructural del Mercosur–FOCEM grant cOF 03/11
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Affiliation(s)
- Noel Federman
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina.
| | - Sebastián A Romano
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina.
| | - Macarena Amigo-Duran
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, PhD Program, Buenos Aires, Argentina
| | - Lucca Salomon
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, PhD Program, Buenos Aires, Argentina
| | - Antonia Marin-Burgin
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina.
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2
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Lee DG, McLachlan CA, Nogueira R, Kwon O, Carey AE, House G, Lagani GD, LaMay D, Fusi S, Chen JL. Perirhinal cortex learns a predictive map of the task environment. Nat Commun 2024; 15:5544. [PMID: 38956015 PMCID: PMC11219840 DOI: 10.1038/s41467-024-47365-7] [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: 08/09/2023] [Accepted: 03/25/2024] [Indexed: 07/04/2024] Open
Abstract
Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that can progressively be decoded earlier in the trial as training advances and that generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and systemic pharmacological perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.
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Affiliation(s)
- David G Lee
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
| | - Caroline A McLachlan
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Osung Kwon
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Alanna E Carey
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Garrett House
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Gavin D Lagani
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Danielle LaMay
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, 10027, USA
- Department of Neuroscience, Columbia University, New York, NY, 10027, USA
| | - Jerry L Chen
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA.
- Department of Biology, Boston University, Boston, MA, 02215, USA.
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA.
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3
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Locantore J, Liu Y, White J, Wallace JB, Beron CC, Sabatini BL, Wallace ML. Mixed representations of choice and outcome by GABA/glutamate cotransmitting neurons in the entopeduncular nucleus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597980. [PMID: 38895480 PMCID: PMC11185773 DOI: 10.1101/2024.06.07.597980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The basal ganglia (BG) are an evolutionarily conserved and phylogenetically old set of sub-cortical nuclei that guide action selection, evaluation, and reinforcement. The entopeduncular nucleus (EP) is a major BG output nucleus that contains a population of GABA/glutamate cotransmitting neurons (EP Sst+ ) that specifically target the lateral habenula (LHb) and whose function in behavior remains mysterious. Here we use a probabilistic switching task that requires an animal to maintain flexible relationships between action selection and evaluation to examine when and how GABA/glutamate cotransmitting neurons contribute to behavior. We find that EP Sst+ neurons are strongly engaged during this task and show bidirectional changes in activity during the choice and outcome periods of a trial. We then tested the effects of either permanently blocking cotransmission or modifying the GABA/glutamate ratio on behavior in well-trained animals. Neither manipulation produced detectable changes in behavior despite significant changes in synaptic transmission in the LHb, demonstrating that the outputs of these neurons are not required for on-going action-outcome updating in a probabilistic switching task.
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4
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Liang T, Brinkman BAW. Statistically inferred neuronal connections in subsampled neural networks strongly correlate with spike train covariances. Phys Rev E 2024; 109:044404. [PMID: 38755896 DOI: 10.1103/physreve.109.044404] [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: 11/08/2023] [Accepted: 02/29/2024] [Indexed: 05/18/2024]
Abstract
Statistically inferred neuronal connections from observed spike train data are often skewed from ground truth by factors such as model mismatch, unobserved neurons, and limited data. Spike train covariances, sometimes referred to as "functional connections," are often used as a proxy for the connections between pairs of neurons, but reflect statistical relationships between neurons, not anatomical connections. Moreover, covariances are not causal: spiking activity is correlated in both the past and the future, whereas neurons respond only to synaptic inputs in the past. Connections inferred by maximum likelihood inference, however, can be constrained to be causal. However, we show in this work that the inferred connections in spontaneously active networks modeled by stochastic leaky integrate-and-fire networks strongly correlate with the covariances between neurons, and may reflect noncausal relationships, when many neurons are unobserved or when neurons are weakly coupled. This phenomenon occurs across different network structures, including random networks and balanced excitatory-inhibitory networks. We use a combination of simulations and a mean-field analysis with fluctuation corrections to elucidate the relationships between spike train covariances, inferred synaptic filters, and ground-truth connections in partially observed networks.
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Affiliation(s)
- Tong Liang
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York 11794, USA
| | - Braden A W Brinkman
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York 11794, USA
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5
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Oude Lohuis MN, Marchesi P, Olcese U, Pennartz CMA. Triple dissociation of visual, auditory and motor processing in mouse primary visual cortex. Nat Neurosci 2024; 27:758-771. [PMID: 38307971 DOI: 10.1038/s41593-023-01564-5] [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: 07/15/2022] [Accepted: 12/19/2023] [Indexed: 02/04/2024]
Abstract
Primary sensory cortices respond to crossmodal stimuli-for example, auditory responses are found in primary visual cortex (V1). However, it remains unclear whether these responses reflect sensory inputs or behavioral modulation through sound-evoked body movement. We address this controversy by showing that sound-evoked activity in V1 of awake mice can be dissociated into auditory and behavioral components with distinct spatiotemporal profiles. The auditory component began at approximately 27 ms, was found in superficial and deep layers and originated from auditory cortex. Sound-evoked orofacial movements correlated with V1 neural activity starting at approximately 80-100 ms and explained auditory frequency tuning. Visual, auditory and motor activity were expressed by different laminar profiles and largely segregated subsets of neuronal populations. During simultaneous audiovisual stimulation, visual representations remained dissociable from auditory-related and motor-related activity. This three-fold dissociability of auditory, motor and visual processing is central to understanding how distinct inputs to visual cortex interact to support vision.
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Affiliation(s)
- Matthijs N Oude Lohuis
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal
| | - Pietro Marchesi
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
| | - Umberto Olcese
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
| | - Cyriel M A Pennartz
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands.
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands.
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6
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Amo R, Uchida N, Watabe-Uchida M. Glutamate inputs send prediction error of reward, but not negative value of aversive stimuli, to dopamine neurons. Neuron 2024; 112:1001-1019.e6. [PMID: 38278147 PMCID: PMC10957320 DOI: 10.1016/j.neuron.2023.12.019] [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: 03/26/2023] [Revised: 11/10/2023] [Accepted: 12/21/2023] [Indexed: 01/28/2024]
Abstract
Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs), but the mechanisms underlying RPE computation, particularly the contributions of different neurotransmitters, remain poorly understood. Here, we used a genetically encoded glutamate sensor to examine the pattern of glutamate inputs to dopamine neurons in mice. We found that glutamate inputs exhibit virtually all of the characteristics of RPE rather than conveying a specific component of RPE computation, such as reward or expectation. Notably, whereas glutamate inputs were transiently inhibited by reward omission, they were excited by aversive stimuli. Opioid analgesics altered dopamine negative responses to aversive stimuli into more positive responses, whereas excitatory responses of glutamate inputs remained unchanged. Our findings uncover previously unknown synaptic mechanisms underlying RPE computations; dopamine responses are shaped by both synergistic and competitive interactions between glutamatergic and GABAergic inputs to dopamine neurons depending on valences, with competitive interactions playing a role in responses to aversive stimuli.
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Affiliation(s)
- Ryunosuke Amo
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Naoshige Uchida
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Mitsuko Watabe-Uchida
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
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7
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Li J, Aoi MC, Miller CT. Representing the dynamics of natural marmoset vocal behaviors in frontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585423. [PMID: 38559173 PMCID: PMC10979968 DOI: 10.1101/2024.03.17.585423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Here we tested the respective contributions of primate premotor and prefrontal cortex to support vocal behavior. We applied a model-based GLM analysis that better accounts for the inherent variance in natural, continuous behaviors to characterize the activity of neurons throughout frontal cortex as freely-moving marmosets engaged in conversational exchanges. While analyses revealed functional clusters of neural activity related to the different processes involved in the vocal behavior, these clusters did not map to subfields of prefrontal or premotor cortex, as has been observed in more conventional task-based paradigms. Our results suggest a distributed functional organization for the myriad neural mechanisms underlying natural social interactions and has implications for our concepts of the role that frontal cortex plays in governing ethological behaviors in primates.
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8
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Lin S, Fan CY, Wang HR, Li XF, Zeng JL, Lan PX, Li HX, Zhang B, Hu C, Xu J, Luo JH. Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice. Mol Psychiatry 2024:10.1038/s41380-024-02505-9. [PMID: 38459194 DOI: 10.1038/s41380-024-02505-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 02/24/2024] [Accepted: 02/28/2024] [Indexed: 03/10/2024]
Abstract
Cognitive and behavioral rigidity are observed in various psychiatric diseases, including in autism spectrum disorder (ASD). However, the underlying mechanism remains to be elucidated. In this study, we found that neuroligin-3 (NL3) R451C knockin mouse model of autism (KI mice) exhibited deficits in behavioral flexibility in choice selection tasks. Single-unit recording of medium spiny neuron (MSN) activity in the nucleus accumbens (NAc) revealed altered encoding of decision-related cue and impaired updating of choice anticipation in KI mice. Additionally, fiber photometry demonstrated significant disruption in dynamic mesolimbic dopamine (DA) signaling for reward prediction errors (RPEs), along with reduced activity in medial prefrontal cortex (mPFC) neurons projecting to the NAc in KI mice. Interestingly, NL3 re-expression in the mPFC, but not in the NAc, rescued the deficit of flexible behaviors and simultaneously restored NAc-MSN encoding, DA dynamics, and mPFC-NAc output in KI mice. Taken together, this study reveals the frontostriatal circuit dysfunction underlying cognitive inflexibility and establishes a critical role of the mPFC NL3 deficiency in this deficit in KI mice. Therefore, these findings provide new insights into the mechanisms of cognitive and behavioral inflexibility and potential intervention strategies.
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Affiliation(s)
- Shen Lin
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China.
- Fujian Provincial Institutes of Brain Disorders and Brain Sciences, First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
| | - Cui-Ying Fan
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Hao-Ran Wang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
- Nanhu Brain-Computer Interface Institute, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
| | - Xiao-Fan Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia-Li Zeng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Pei-Xuan Lan
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Hui-Xian Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Bin Zhang
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, China
| | - Chun Hu
- Institute for Brain Research and Rehabilitation, Key Laboratory of Brain Cognition and Education Sciences of Ministry of Education, South China Normal University, Guangzhou, China
| | - Junyu Xu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China.
| | - Jian-Hong Luo
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China.
- Nanhu Brain-Computer Interface Institute, Hangzhou, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China.
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9
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Simpson EH, Akam T, Patriarchi T, Blanco-Pozo M, Burgeno LM, Mohebi A, Cragg SJ, Walton ME. Lights, fiber, action! A primer on in vivo fiber photometry. Neuron 2024; 112:718-739. [PMID: 38103545 PMCID: PMC10939905 DOI: 10.1016/j.neuron.2023.11.016] [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: 07/23/2023] [Revised: 10/16/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023]
Abstract
Fiber photometry is a key technique for characterizing brain-behavior relationships in vivo. Initially, it was primarily used to report calcium dynamics as a proxy for neural activity via genetically encoded indicators. This generated new insights into brain functions including movement, memory, and motivation at the level of defined circuits and cell types. Recently, the opportunity for discovery with fiber photometry has exploded with the development of an extensive range of fluorescent sensors for biomolecules including neuromodulators and peptides that were previously inaccessible in vivo. This critical advance, combined with the new availability of affordable "plug-and-play" recording systems, has made monitoring molecules with high spatiotemporal precision during behavior highly accessible. However, while opening exciting new avenues for research, the rapid expansion in fiber photometry applications has occurred without coordination or consensus on best practices. Here, we provide a comprehensive guide to help end-users execute, analyze, and suitably interpret fiber photometry studies.
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Affiliation(s)
- Eleanor H Simpson
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA.
| | - Thomas Akam
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
| | - Tommaso Patriarchi
- Institute of Pharmacology and Toxicology, University of Zürich, Zürich, Switzerland; Neuroscience Center Zürich, University and ETH Zürich, Zürich, Switzerland.
| | - Marta Blanco-Pozo
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Lauren M Burgeno
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Ali Mohebi
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Stephanie J Cragg
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Mark E Walton
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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10
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Han S, Helmchen F. Behavior-relevant top-down cross-modal predictions in mouse neocortex. Nat Neurosci 2024; 27:298-308. [PMID: 38177341 DOI: 10.1038/s41593-023-01534-x] [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: 03/07/2023] [Accepted: 11/27/2023] [Indexed: 01/06/2024]
Abstract
Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone-texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.
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Affiliation(s)
- Shuting Han
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
| | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland.
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland.
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11
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Lee DH, Kim JS, Ryun S, Chung CK. Discrete tactile feature comparison subprocess in human brain during a decision-making process. Cortex 2024; 171:383-396. [PMID: 38101274 DOI: 10.1016/j.cortex.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/03/2023] [Accepted: 11/02/2023] [Indexed: 12/17/2023]
Abstract
From sensory input to motor action, encoded sensory features flow sequentially along cortical networks for decision-making. Despite numerous studies probing the decision-making process, the subprocess that compares encoded sensory features before making a decision has not been fully elucidated in humans. In this study, we investigated sensory feature comparison by presenting two different tasks (a discrimination task, in which participants made decisions by comparing two sequential tactile stimuli; and a detection task, in which participants responded to the second tactile stimulus in two sequential stimuli) to epilepsy patients while recording electrocorticography (ECoG). By comparing tactile-specific gamma band (30-200 Hz) power between the two tasks, the decision-making process was divided into three subprocesses-categorization, comparison, and decision-consistent with a previous study (Heekeren et al., 2004). These subprocesses occurred sequentially in the dorsolateral prefrontal cortex, premotor cortex, secondary somatosensory cortex, and parietal lobe. Gamma power showed two different patterns of correlation with response time. In the inferior parietal lobule (IPL), there was a negative correlation. This means that as gamma power increased, response time decreased. In the secondary somatosensory cortex (S2), there was a positive correlation. Here, as gamma power increased, response time also increased. These results indicate that the IPL and S2 encode tactile feature comparison differently. Our connectivity analysis showed that the S2 transmitted tactile information to the IPL. Our findings suggest that multiple areas in the parietal lobe encode sensory feature comparison differently before making a decision.
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Affiliation(s)
- Dong Hyeok Lee
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - June Sic Kim
- The Research Institute of Basic Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Seokyun Ryun
- Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Chun Kee Chung
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea; Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, Republic of Korea; Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea.
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12
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Weng G, Clark K, Akbarian A, Noudoost B, Nategh N. Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas. Front Comput Neurosci 2024; 18:1273053. [PMID: 38348287 PMCID: PMC10859875 DOI: 10.3389/fncom.2024.1273053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 01/09/2024] [Indexed: 02/15/2024] Open
Abstract
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors' contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
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Affiliation(s)
- Geyu Weng
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Kelsey Clark
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Amir Akbarian
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Neda Nategh
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States
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13
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Guidera JA, Gramling DP, Comrie AE, Joshi A, Denovellis EL, Lee KH, Zhou J, Thompson P, Hernandez J, Yorita A, Haque R, Kirst C, Frank LM. Regional specialization manifests in the reliability of neural population codes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.25.576941. [PMID: 38328245 PMCID: PMC10849741 DOI: 10.1101/2024.01.25.576941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
The brain has the remarkable ability to learn and guide the performance of complex tasks. Decades of lesion studies suggest that different brain regions perform specialized functions in support of complex behaviors1-3. Yet recent large-scale studies of neural activity reveal similar patterns of activity and encoding distributed widely throughout the brain4-6. How these distributed patterns of activity and encoding are compatible with regional specialization of brain function remains unclear. Two frontal brain regions, the dorsal medial prefrontal cortex (dmPFC) and orbitofrontal cortex (OFC), are a paradigm of this conundrum. In the setting complex behaviors, the dmPFC is necessary for choosing optimal actions2,7,8, whereas the OFC is necessary for waiting for3,9 and learning from2,7,9-12 the outcomes of those actions. Yet both dmPFC and OFC encode both choice- and outcome-related quantities13-20. Here we show that while ensembles of neurons in the dmPFC and OFC of rats encode similar elements of a cognitive task with similar patterns of activity, the two regions differ in when that coding is consistent across trials ("reliable"). In line with the known critical functions of each region, dmPFC activity is more reliable when animals are making choices and less reliable preceding outcomes, whereas OFC activity shows the opposite pattern. Our findings identify the dynamic reliability of neural population codes as a mechanism whereby different brain regions may support distinct cognitive functions despite exhibiting similar patterns of activity and encoding similar quantities.
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Affiliation(s)
- Jennifer A. Guidera
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, 94158, USA and University of California, Berkeley; Berkely, 94720, USA
- Medical Scientist Training Program, University of California, San Francisco; San Francisco, 94158, USA
| | - Daniel P. Gramling
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
| | - Alison E. Comrie
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
| | - Abhilasha Joshi
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
| | - Eric L. Denovellis
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
| | - Kyu Hyun Lee
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
| | - Jenny Zhou
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Paige Thompson
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Jose Hernandez
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Allison Yorita
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Razi Haque
- Center for Micro- and Nano-Technology, Lawrence Livermore National Laboratory; Livermore, 94158, USA
| | - Christoph Kirst
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Department of Anatomy, University of California, San Francisco; San Francisco, 94158, USA
| | - Loren M. Frank
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, 94158, USA and University of California, Berkeley; Berkely, 94720, USA
- Departments of Physiology and Psychiatry, University of California, San Francisco; San Francisco, 94158, USA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco; San Francisco, 94158, USA
- Howard Hughes Medical Institute, University of California, San Francisco; San Francisco, 94158, USA
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14
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Tolooshams B, Matias S, Wu H, Temereanca S, Uchida N, Murthy VN, Masset P, Ba D. Interpretable deep learning for deconvolutional analysis of neural signals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574379. [PMID: 38260512 PMCID: PMC10802267 DOI: 10.1101/2024.01.05.574379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and function. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the responses of neurons in the piriform cortex. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural dynamics.
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Affiliation(s)
- Bahareh Tolooshams
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge MA, 02138
- Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125
| | - Sara Matias
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Hao Wu
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Simona Temereanca
- Carney Institute for Brain Science, Brown University, Providence, RI, 02906
| | - Naoshige Uchida
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Venkatesh N. Murthy
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Paul Masset
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
- Department of Psychology, McGill University, Montréal QC, H3A 1G1
| | - Demba Ba
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge MA, 02138
- Kempner Institute for the Study of Natural & Artificial Intelligence, Harvard University, Cambridge MA, 02138
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15
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Stagkourakis S, Spigolon G, Marks M, Feyder M, Kim J, Perona P, Pachitariu M, Anderson DJ. Anatomically distributed neural representations of instincts in the hypothalamus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.21.568163. [PMID: 38045312 PMCID: PMC10690204 DOI: 10.1101/2023.11.21.568163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.
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Affiliation(s)
- Stefanos Stagkourakis
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
| | - Giada Spigolon
- Biological Imaging Facility, California Institute of Technology, Pasadena, California 91125, USA
| | - Markus Marks
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Michael Feyder
- Division of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California 94305, USA
| | - Joseph Kim
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
| | - Pietro Perona
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Marius Pachitariu
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
| | - David J. Anderson
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
- Howard Hughes Medical Institute, California Institute of Technology, 1200 East California Blvd, Pasadena, California 91125, USA
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16
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Luo TZ, Kim TD, Gupta D, Bondy AG, Kopec CD, Elliot VA, DePasquale B, Brody CD. Transitions in dynamical regime and neural mode underlie perceptual decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.15.562427. [PMID: 37904994 PMCID: PMC10614809 DOI: 10.1101/2023.10.15.562427] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We show that contrary to prevailing hypotheses, attractors play a role only after a transition from a regime in the dynamics that is strongly driven by inputs to one dominated by the intrinsic dynamics. The initial regime mediates evidence accumulation, and the subsequent intrinsic-dominant regime subserves decision commitment. This regime transition is coupled to a rapid reorganization in the representation of the decision process in the neural population (a change in the "neural mode" along which the process develops). A simplified model approximating the coupled transition in the dynamics and neural mode allows inferring, from each trial's neural activity, the internal decision commitment time in that trial, and captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 3-5 . It also captures trial-averaged curved trajectories 6-8 , and reveals distinctions between brain regions. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest pairing deep learning and parsimonious models as a promising approach for understanding complex data.
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17
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Barbieri R, Töpfer FM, Soch J, Bogler C, Sprekeler H, Haynes JD. Encoding of continuous perceptual choices in human early visual cortex. Front Hum Neurosci 2023; 17:1277539. [PMID: 38021249 PMCID: PMC10679739 DOI: 10.3389/fnhum.2023.1277539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Research on the neural mechanisms of perceptual decision-making has typically focused on simple categorical choices, say between two alternative motion directions. Studies on such discrete alternatives have often suggested that choices are encoded either in a motor-based or in an abstract, categorical format in regions beyond sensory cortex. Methods In this study, we used motion stimuli that could vary anywhere between 0° and 360° to assess how the brain encodes choices for features that span the full sensory continuum. We employed a combination of neuroimaging and encoding models based on Gaussian process regression to assess how either stimuli or choices were encoded in brain responses. Results We found that single-voxel tuning patterns could be used to reconstruct the trial-by-trial physical direction of motion as well as the participants' continuous choices. Importantly, these continuous choice signals were primarily observed in early visual areas. The tuning properties in this region generalized between choice encoding and stimulus encoding, even for reports that reflected pure guessing. Discussion We found only little information related to the decision outcome in regions beyond visual cortex, such as parietal cortex, possibly because our task did not involve differential motor preparation. This could suggest that decisions for continuous stimuli take can place already in sensory brain regions, potentially using similar mechanisms to the sensory recruitment in visual working memory.
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Affiliation(s)
- Riccardo Barbieri
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health (BIH), Berlin, Germany
| | - Felix M. Töpfer
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health (BIH), Berlin, Germany
| | - Joram Soch
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health (BIH), Berlin, Germany
- German Center for Neurodegenerative Diseases, Göttingen, Germany
| | - Carsten Bogler
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health (BIH), Berlin, Germany
| | - Henning Sprekeler
- Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health (BIH), Berlin, Germany
- Berlin School of Mind and Brain and Institute of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
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18
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Amo R, Uchida N, Watabe-Uchida M. Glutamate inputs send prediction error of reward but not negative value of aversive stimuli to dopamine neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.09.566472. [PMID: 37986868 PMCID: PMC10659341 DOI: 10.1101/2023.11.09.566472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs) but the mechanisms underlying RPE computation, particularly contributions of different neurotransmitters, remain poorly understood. Here we used a genetically-encoded glutamate sensor to examine the pattern of glutamate inputs to dopamine neurons. We found that glutamate inputs exhibit virtually all of the characteristics of RPE, rather than conveying a specific component of RPE computation such as reward or expectation. Notably, while glutamate inputs were transiently inhibited by reward omission, they were excited by aversive stimuli. Opioid analgesics altered dopamine negative responses to aversive stimuli toward more positive responses, while excitatory responses of glutamate inputs remained unchanged. Our findings uncover previously unknown synaptic mechanisms underlying RPE computations; dopamine responses are shaped by both synergistic and competitive interactions between glutamatergic and GABAergic inputs to dopamine neurons depending on valences, with competitive interactions playing a role in responses to aversive stimuli.
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Affiliation(s)
- Ryunosuke Amo
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Naoshige Uchida
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Mitsuko Watabe-Uchida
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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19
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Walker EY, Pohl S, Denison RN, Barack DL, Lee J, Block N, Ma WJ, Meyniel F. Studying the neural representations of uncertainty. Nat Neurosci 2023; 26:1857-1867. [PMID: 37814025 DOI: 10.1038/s41593-023-01444-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 08/30/2023] [Indexed: 10/11/2023]
Abstract
The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.
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Affiliation(s)
- Edgar Y Walker
- Department of Physiology and Biophysics, Computational Neuroscience Center, University of Washington, Seattle, WA, USA
| | - Stephan Pohl
- Department of Philosophy, New York University, New York, NY, USA
| | - Rachel N Denison
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - David L Barack
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Department of Philosophy, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Lee
- Center for Neural Science, New York University, New York, NY, USA
| | - Ned Block
- Department of Philosophy, New York University, New York, NY, USA
| | - Wei Ji Ma
- Center for Neural Science, New York University, New York, NY, USA
- Department of Psychology, New York University, New York, NY, USA
| | - Florent Meyniel
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.
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20
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Chia XW, Tan JK, Ang LF, Kamigaki T, Makino H. Emergence of cortical network motifs for short-term memory during learning. Nat Commun 2023; 14:6869. [PMID: 37898638 PMCID: PMC10613236 DOI: 10.1038/s41467-023-42609-4] [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: 10/28/2022] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
Learning of adaptive behaviors requires the refinement of coordinated activity across multiple brain regions. However, how neural communications develop during learning remains poorly understood. Here, using two-photon calcium imaging, we simultaneously recorded the activity of layer 2/3 excitatory neurons in eight regions of the mouse dorsal cortex during learning of a delayed-response task. Across learning, while global functional connectivity became sparser, there emerged a subnetwork comprising of neurons in the anterior lateral motor cortex (ALM) and posterior parietal cortex (PPC). Neurons in this subnetwork shared a similar choice code during action preparation and formed recurrent functional connectivity across learning. Suppression of PPC activity disrupted choice selectivity in ALM and impaired task performance. Recurrent neural networks reconstructed from ALM activity revealed that PPC-ALM interactions rendered choice-related attractor dynamics more stable. Thus, learning constructs cortical network motifs by recruiting specific inter-areal communication channels to promote efficient and robust sensorimotor transformation.
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Affiliation(s)
- Xin Wei Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Jian Kwang Tan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Lee Fang Ang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Tsukasa Kamigaki
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Hiroshi Makino
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
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21
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Matsumiya K, Furukawa S. Perceptual decisions interfere more with eye movements than with reach movements. Commun Biol 2023; 6:882. [PMID: 37648896 PMCID: PMC10468498 DOI: 10.1038/s42003-023-05249-4] [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: 01/17/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Perceptual judgements are formed through invisible cognitive processes. Reading out these judgements is essential for advancing our understanding of decision making and requires inferring covert cognitive states based on overt motor actions. Although intuition suggests that these actions must be related to the formation of decisions about where to move body parts, actions have been reported to be influenced by perceptual judgements even when the action is irrelevant to the perceptual judgement. However, despite performing multiple actions in our daily lives, how perceptual judgements influence multiple judgement-irrelevant actions is unknown. Here we show that perceptual judgements affect only saccadic eye movements when simultaneous judgement-irrelevant saccades and reaches are made, demonstrating that perceptual judgement-related signals continuously flow into the oculomotor system alone when multiple judgement-irrelevant actions are performed. This suggests that saccades are useful for making inferences about covert perceptual decisions, even when the actions are not tied to decision making.
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Affiliation(s)
| | - Shota Furukawa
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
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22
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Safaai H, Wang AY, Kira S, Malerba SB, Panzeri S, Harvey CD. Specialized structure of neural population codes in parietal cortex outputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554635. [PMID: 37662297 PMCID: PMC10473762 DOI: 10.1101/2023.08.24.554635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Do cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.
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Affiliation(s)
- Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, USA
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Alice Y. Wang
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Simone Blanco Malerba
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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23
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Coen P, Sit TPH, Wells MJ, Carandini M, Harris KD. Mouse frontal cortex mediates additive multisensory decisions. Neuron 2023; 111:2432-2447.e13. [PMID: 37295419 PMCID: PMC10957398 DOI: 10.1016/j.neuron.2023.05.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/02/2022] [Accepted: 05/10/2023] [Indexed: 06/12/2023]
Abstract
The brain can combine auditory and visual information to localize objects. However, the cortical substrates underlying audiovisual integration remain uncertain. Here, we show that mouse frontal cortex combines auditory and visual evidence; that this combination is additive, mirroring behavior; and that it evolves with learning. We trained mice in an audiovisual localization task. Inactivating frontal cortex impaired responses to either sensory modality, while inactivating visual or parietal cortex affected only visual stimuli. Recordings from >14,000 neurons indicated that after task learning, activity in the anterior part of frontal area MOs (secondary motor cortex) additively encodes visual and auditory signals, consistent with the mice's behavioral strategy. An accumulator model applied to these sensory representations reproduced the observed choices and reaction times. These results suggest that frontal cortex adapts through learning to combine evidence across sensory cortices, providing a signal that is transformed into a binary decision by a downstream accumulator.
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Affiliation(s)
- Philip Coen
- UCL Queen Square Institute of Neurology, University College London, London, UK; UCL Institute of Ophthalmology, University College London, London, UK.
| | - Timothy P H Sit
- Sainsbury-Wellcome Center, University College London, London, UK
| | - Miles J Wells
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
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24
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Sachdeva P, Bak JH, Livezey J, Kirst C, Frank L, Bhattacharyya S, Bouchard KE. Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.11.548615. [PMID: 37503030 PMCID: PMC10370036 DOI: 10.1101/2023.07.11.548615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets.
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Affiliation(s)
- Pratik Sachdeva
- Physics Department, UC Berkeley
- Redwood Center for Theoretical Neuroscience, UC Berkeley
| | - Ji Hyun Bak
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
| | - Jesse Livezey
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
| | - Christoph Kirst
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Scientific Data Division, Lawrence Berkeley National Lab
- Deptartment of Anatomy, UC San Francisco
| | - Loren Frank
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Departments of Physiology and Psychiatry, UC San Francisco
- Howard Hughes Medical Institute
| | | | - Kristofer E. Bouchard
- Redwood Center for Theoretical Neuroscience, UC Berkeley
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
- Scientific Data Division, Lawrence Berkeley National Lab
- Helen Wills Neuroscience Institute, UC Berkeley
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25
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Khoury CF, Fala NG, Runyan CA. Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex. eNeuro 2023; 10:ENEURO.0136-23.2023. [PMID: 37169583 PMCID: PMC10216262 DOI: 10.1523/eneuro.0136-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/13/2023] Open
Abstract
Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.
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Affiliation(s)
- Christine F Khoury
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Noelle G Fala
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Caroline A Runyan
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
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26
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Kira S, Safaai H, Morcos AS, Panzeri S, Harvey CD. A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nat Commun 2023; 14:2121. [PMID: 37055431 PMCID: PMC10102117 DOI: 10.1038/s41467-023-37804-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/30/2023] [Indexed: 04/15/2023] Open
Abstract
Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.
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Affiliation(s)
- Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ari S Morcos
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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27
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Lee DG, McLachlan CA, Nogueira R, Kwon O, Carey AE, House G, Lagani GD, LaMay D, Fusi S, Chen JL. PERIRHINAL CORTEX LEARNS A PREDICTIVE MAP (INTERNAL MODEL) OF THE TASK ENVIRONMENT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.17.532214. [PMID: 36993645 PMCID: PMC10055158 DOI: 10.1101/2023.03.17.532214] [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
Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chemogenetic inactivation demonstrated that Prh is involved in task learning. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that expand in a retrospective manner and generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.
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Affiliation(s)
- David G Lee
- Department of Biomedical Engineering, Boston University, Boston MA 02215, USA
- Center for Neurophotonics, Boston University, Boston MA 02215, USA
| | - Caroline A McLachlan
- Center for Neurophotonics, Boston University, Boston MA 02215, USA
- Department of Biology, Boston University, Boston MA 02215, USA
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Columbia University, New York NY 10027, USA
| | - Osung Kwon
- Center for Neurophotonics, Boston University, Boston MA 02215, USA
- Department of Biology, Boston University, Boston MA 02215, USA
| | - Alanna E Carey
- Center for Neurophotonics, Boston University, Boston MA 02215, USA
- Department of Biology, Boston University, Boston MA 02215, USA
| | - Garrett House
- Department of Biology, Boston University, Boston MA 02215, USA
| | - Gavin D Lagani
- Department of Biology, Boston University, Boston MA 02215, USA
| | - Danielle LaMay
- Department of Biology, Boston University, Boston MA 02215, USA
| | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Columbia University, New York NY 10027, USA
| | - Jerry L Chen
- Department of Biomedical Engineering, Boston University, Boston MA 02215, USA
- Center for Neurophotonics, Boston University, Boston MA 02215, USA
- Department of Biology, Boston University, Boston MA 02215, USA
- Center for Systems Neuroscience, Boston University, Boston MA 02215, USA
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28
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Lakshminarasimhan KJ, Avila E, Pitkow X, Angelaki DE. Dynamical latent state computation in the male macaque posterior parietal cortex. Nat Commun 2023; 14:1832. [PMID: 37005470 PMCID: PMC10067966 DOI: 10.1038/s41467-023-37400-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 03/15/2023] [Indexed: 04/04/2023] Open
Abstract
Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey's displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states.
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Affiliation(s)
| | - Eric Avila
- Center for Neural Science, New York University, New York City, NY, USA
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Electrical & Computer Engineering, Rice University, Houston, TX, USA
| | - Dora E Angelaki
- Center for Neural Science, New York University, New York City, NY, USA
- Department of Mechanical and Aerospace Engineering, New York University, New York City, NY, USA
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29
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Capone C, De Luca C, De Bonis G, Gutzen R, Bernava I, Pastorelli E, Simula F, Lupo C, Tonielli L, Resta F, Allegra Mascaro AL, Pavone F, Denker M, Paolucci PS. Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse. Commun Biol 2023; 6:266. [PMID: 36914748 PMCID: PMC10011502 DOI: 10.1038/s42003-023-04580-0] [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: 06/16/2022] [Accepted: 02/10/2023] [Indexed: 03/16/2023] Open
Abstract
The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling waves dynamics. Here, we infer data-driven models from high-resolution in-vivo recordings of mouse brain obtained from wide-field calcium imaging. We then assimilate experimental and simulated data through the characterization of the spatio-temporal features of cortical waves in experimental recordings. Inference is built in two steps: an inner loop that optimizes a mean-field model by likelihood maximization, and an outer loop that optimizes a periodic neuro-modulation via direct comparison of observables that characterize cortical slow waves. The model reproduces most of the features of the non-stationary and non-linear dynamics present in the high-resolution in-vivo recordings of the mouse brain. The proposed approach offers new methods of characterizing and understanding cortical waves for experimental and computational neuroscientists.
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Affiliation(s)
| | - Chiara De Luca
- INFN, Sezione di Roma, Rome, Italy
- PhD Program in Behavioural Neuroscience, "Sapienza" University of Rome, Rome, Italy
| | | | - Robin Gutzen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| | | | | | | | | | | | - Francesco Resta
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Anna Letizia Allegra Mascaro
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
- Neuroscience Institute, National Research Council, Pisa, Italy
| | - Francesco Pavone
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
- University of Florence, Physics and Astronomy Department, Sesto Fiorentino, Italy
| | - Michael Denker
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
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30
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Expectation violations enhance neuronal encoding of sensory information in mouse primary visual cortex. Nat Commun 2023; 14:1196. [PMID: 36864037 PMCID: PMC9981605 DOI: 10.1038/s41467-023-36608-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/08/2023] [Indexed: 03/04/2023] Open
Abstract
The response of cortical neurons to sensory stimuli is shaped both by past events (adaptation) and the expectation of future events (prediction). Here we employed a visual stimulus paradigm with different levels of predictability to characterise how expectation influences orientation selectivity in the primary visual cortex (V1) of male mice. We recorded neuronal activity using two-photon calcium imaging (GCaMP6f) while animals viewed sequences of grating stimuli which either varied randomly in their orientations or rotated predictably with occasional transitions to an unexpected orientation. For single neurons and the population, there was significant enhancement in the gain of orientation-selective responses to unexpected gratings. This gain-enhancement for unexpected stimuli was prominent in both awake and anaesthetised mice. We implemented a computational model to demonstrate how trial-to-trial variability in neuronal responses were best characterised when adaptation and expectation effects were combined.
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31
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DePasquale B, Sussillo D, Abbott LF, Churchland MM. The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. Neuron 2023; 111:631-649.e10. [PMID: 36630961 PMCID: PMC10118067 DOI: 10.1016/j.neuron.2022.12.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/17/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023]
Abstract
Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.
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Affiliation(s)
- Brian DePasquale
- Princeton Neuroscience Institute, Princeton University, Princeton NJ, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - L F Abbott
- Department of Neuroscience, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
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32
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Latimer KW, Freedman DJ. Low-dimensional encoding of decisions in parietal cortex reflects long-term training history. Nat Commun 2023; 14:1010. [PMID: 36823109 PMCID: PMC9950136 DOI: 10.1038/s41467-023-36554-5] [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: 11/22/2021] [Accepted: 02/07/2023] [Indexed: 02/25/2023] Open
Abstract
Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category task. We consider two pairs of male macaque monkeys with different training histories: one trained only on the categorization task, and another first trained to perform fine motion-direction discrimination (i.e., pretrained). We introduce a novel analytical approach-generalized multilinear models-to quantify low-dimensional, task-relevant components in population activity. During the categorization task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the category-only monkeys, and that the pretrained monkeys' performance depended more heavily on fine discrimination between sample and test stimuli. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of considering training history in studies with complex behavioral tasks.
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Affiliation(s)
- Kenneth W Latimer
- Department of Neurobiology, University of Chicago, Chicago, IL, USA.
| | - David J Freedman
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
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33
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Cox J, Minerva AR, Fleming WT, Zimmerman CA, Hayes C, Zorowitz S, Bandi A, Ornelas S, McMannon B, Parker NF, Witten IB. A neural substrate of sex-dependent modulation of motivation. Nat Neurosci 2023; 26:274-284. [PMID: 36646878 DOI: 10.1038/s41593-022-01229-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/01/2022] [Indexed: 01/18/2023]
Abstract
While there is emerging evidence of sex differences in decision-making behavior, the neural substrates that underlie such differences remain largely unknown. Here we demonstrate that in mice performing a value-based decision-making task, while choices are similar between the sexes, motivation to engage in the task is modulated by action value more strongly in females than in males. Inhibition of activity in anterior cingulate cortex (ACC) neurons that project to the dorsomedial striatum (DMS) preferentially disrupts this relationship between value and motivation in females, without affecting choice in either sex. In line with these effects, in females compared to males, ACC-DMS neurons have stronger representations of negative outcomes and more neurons are active when the value of the chosen option is low. By contrast, the representation of each choice is similar between the sexes. Thus, we identify a neural substrate that contributes to sex-specific modulation of motivation by value.
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Affiliation(s)
- Julia Cox
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Adelaide R Minerva
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Weston T Fleming
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Cameron Hayes
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Samuel Zorowitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Akhil Bandi
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sharon Ornelas
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Brenna McMannon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Nathan F Parker
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ilana B Witten
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Department of Psychology, Princeton University, Princeton, NJ, USA.
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34
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Makino H. Arithmetic value representation for hierarchical behavior composition. Nat Neurosci 2023; 26:140-149. [PMID: 36550292 PMCID: PMC9829535 DOI: 10.1038/s41593-022-01211-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
The ability to compose new skills from a preacquired behavior repertoire is a hallmark of biological intelligence. Although artificial agents extract reusable skills from past experience and recombine them in a hierarchical manner, whether the brain similarly composes a novel behavior is largely unknown. In the present study, I show that deep reinforcement learning agents learn to solve a novel composite task by additively combining representations of prelearned action values of constituent subtasks. Learning efficacy in the composite task was further augmented by the introduction of stochasticity in behavior during pretraining. These theoretical predictions were empirically tested in mice, where subtask pretraining enhanced learning of the composite task. Cortex-wide, two-photon calcium imaging revealed analogous neural representations of combined action values, with improved learning when the behavior variability was amplified. Together, these results suggest that the brain composes a novel behavior with a simple arithmetic operation of preacquired action-value representations with stochastic policies.
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Affiliation(s)
- Hiroshi Makino
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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35
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Kim M, Young L, Anzellotti S. Exploring the Representational Structure of Trait Knowledge Using Perceived Similarity Judgments. SOCIAL COGNITION 2022. [DOI: 10.1521/soco.2022.40.6.549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
A large body of past work has sought to identify the underlying dimensions that capture our trait knowledge of other people. However, the importance of particular traits in determining our overall impressions of others is not well understood, and different traits may be fundamental for impressions of famous versus unfamiliar people. For instance, we may focus on competence when evaluating a famous person, but on trustworthiness when evaluating a stranger. To examine the structure of overall impressions of famous people and of unfamiliar people, we probed the contributions of 13 different trait judgments to perceived similarity judgments. We found that different sets of traits best predicted perceived similarity between famous people versus between unfamiliar people; however, the relationship between each trait and perceived similarity generalized to some extent from famous people to unfamiliar people, suggesting a degree of overlap in the structure of overall impressions.
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36
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Vijayan V, Wang Z, Chandra V, Chakravorty A, Li R, Sarbanes SL, Akhlaghpour H, Maimon G. An internal expectation guides Drosophila egg-laying decisions. SCIENCE ADVANCES 2022; 8:eabn3852. [PMID: 36306348 PMCID: PMC9616500 DOI: 10.1126/sciadv.abn3852] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
To better understand how animals make ethologically relevant decisions, we studied egg-laying substrate choice in Drosophila. We found that flies dynamically increase or decrease their egg-laying rates while exploring substrates so as to target eggs to the best, recently visited option. Visiting the best option typically yielded inhibition of egg laying on other substrates for many minutes. Our data support a model in which flies compare the current substrate's value with an internally constructed expectation on the value of available options to regulate the likelihood of laying an egg. We show that dopamine neuron activity is critical for learning and/or expressing this expectation, similar to its role in certain tasks in vertebrates. Integrating sensory experiences over minutes to generate an estimate of the quality of available options allows flies to use a dynamic reference point for judging the current substrate and might be a general way in which decisions are made.
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37
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Transition of distinct context-dependent ensembles from secondary to primary motor cortex in skilled motor performance. Cell Rep 2022; 41:111494. [DOI: 10.1016/j.celrep.2022.111494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/27/2022] [Accepted: 09/21/2022] [Indexed: 11/19/2022] Open
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38
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Lee JJ, Krumin M, Harris KD, Carandini M. Task specificity in mouse parietal cortex. Neuron 2022; 110:2961-2969.e5. [PMID: 35963238 PMCID: PMC9616730 DOI: 10.1016/j.neuron.2022.07.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/16/2022] [Accepted: 07/15/2022] [Indexed: 11/26/2022]
Abstract
Parietal cortex is implicated in a variety of behavioral processes, but it is unknown whether and how its individual neurons participate in multiple tasks. We trained head-fixed mice to perform two visual decision tasks involving a steering wheel or a virtual T-maze and recorded from the same parietal neurons during these two tasks. Neurons that were active during the T-maze task were typically inactive during the steering-wheel task and vice versa. Recording from the same neurons in the same apparatus without task stimuli yielded the same specificity as in the task, suggesting that task specificity depends on physical context. To confirm this, we trained some mice in a third task combining the steering wheel context with the visual environment of the T-maze. This hybrid task engaged the same neurons as those engaged in the steering-wheel task. Thus, participation by neurons in mouse parietal cortex is task specific, and this specificity is determined by physical context.
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Affiliation(s)
- Julie J Lee
- UCL Institute of Ophthalmology, University College London, Gower Street, London WC1E 6AE, UK.
| | - Michael Krumin
- UCL Institute of Ophthalmology, University College London, Gower Street, London WC1E 6AE, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, Gower Street, London WC1E 6AE, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, Gower Street, London WC1E 6AE, UK
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39
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Zhang Z, Yin C, Yang T. Evidence accumulation occurs locally in the parietal cortex. Nat Commun 2022; 13:4426. [PMID: 35907908 PMCID: PMC9339004 DOI: 10.1038/s41467-022-32210-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 07/20/2022] [Indexed: 11/09/2022] Open
Abstract
Decision making often entails evidence accumulation, a process that is represented by neural activities in a network of multiple brain areas. Yet, it has not been identified where exactly the accumulation originates. We reason that a candidate brain area should both represent evidence accumulation and information that is used to compute evidence. Therefore, we designed a two-stage probabilistic reasoning task in which the evidence for accumulation had to be first determined from sensory signals orthogonal to decisions. With a linear encoding model, we decomposed the responses of posterior parietal neurons to each stimulus into an early and a late component that represented two dissociable stages of decision making. The former reflected the transformation from sensory inputs to accumulable evidence, and the latter reflected the accumulation of evidence and the formation of decisions. The presence of both computational stages indicates that evidence accumulation signal in the parietal cortex is computed locally.
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Affiliation(s)
- Zhewei Zhang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chaoqun Yin
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tianming Yang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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40
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Oude Lohuis MN, Marchesi P, Pennartz CMA, Olcese U. Functional (ir)Relevance of Posterior Parietal Cortex during Audiovisual Change Detection. J Neurosci 2022; 42:5229-5245. [PMID: 35641187 PMCID: PMC9236290 DOI: 10.1523/jneurosci.2150-21.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 12/31/2022] Open
Abstract
The posterior parietal cortex (PPC) plays a key role in integrating sensory inputs from different modalities to support adaptive behavior. Neuronal activity in PPC reflects perceptual decision-making across behavioral tasks, but the mechanistic involvement of PPC is unclear. In an audiovisual change detection task, we tested the hypothesis that PPC is required to arbitrate between the noisy inputs from the two different modalities and help decide in which modality a sensory change occurred. In trained male mice, we found extensive single-neuron and population-level encoding of task-relevant visual and auditory stimuli, trial history, as well as upcoming behavioral responses. However, despite these rich neural correlates, which would theoretically be sufficient to solve the task, optogenetic inactivation of PPC did not affect visual or auditory performance. Thus, despite neural correlates faithfully tracking sensory variables and predicting behavioral responses, PPC was not relevant for audiovisual change detection. This functional dissociation questions the role of sensory- and task-related activity in parietal associative circuits during audiovisual change detection. Furthermore, our results highlight the necessity to dissociate functional correlates from mechanistic involvement when exploring the neural basis of perception and behavior.SIGNIFICANCE STATEMENT The posterior parietal cortex (PPC) is active during many daily tasks, but capturing its function has remained challenging. Specifically, it is proposed to function as an integration hub for multisensory inputs. Here, we tested the hypothesis that, rather than classical cue integration, mouse PPC is involved in the segregation and discrimination of sensory modalities. Surprisingly, although neural activity tracked current and past sensory stimuli and reflected the ongoing decision-making process, optogenetic inactivation did not affect task performance. Thus, we show an apparent redundancy of sensory and task-related activity in mouse PPC. These results narrow down the function of parietal circuits, as well as direct the search for those neural dynamics that causally drive perceptual decision-making.
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Affiliation(s)
- Matthijs N Oude Lohuis
- Cognitive and Systems Neuroscience Group, SILS, University of Amsterdam, Amsterdam 1098XH, The Netherlands
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam 1018WS, The Netherlands
| | - Pietro Marchesi
- Cognitive and Systems Neuroscience Group, SILS, University of Amsterdam, Amsterdam 1098XH, The Netherlands
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam 1018WS, The Netherlands
| | - Cyriel M A Pennartz
- Cognitive and Systems Neuroscience Group, SILS, University of Amsterdam, Amsterdam 1098XH, The Netherlands
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam 1018WS, The Netherlands
| | - Umberto Olcese
- Cognitive and Systems Neuroscience Group, SILS, University of Amsterdam, Amsterdam 1098XH, The Netherlands
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam 1018WS, The Netherlands
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41
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Suhaimi A, Lim AWH, Chia XW, Li C, Makino H. Representation learning in the artificial and biological neural networks underlying sensorimotor integration. SCIENCE ADVANCES 2022; 8:eabn0984. [PMID: 35658033 PMCID: PMC9166289 DOI: 10.1126/sciadv.abn0984] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/18/2022] [Indexed: 06/15/2023]
Abstract
The integration of deep learning and theories of reinforcement learning (RL) is a promising avenue to explore novel hypotheses on reward-based learning and decision-making in humans and other animals. Here, we trained deep RL agents and mice in the same sensorimotor task with high-dimensional state and action space and studied representation learning in their respective neural networks. Evaluation of thousands of neural network models with extensive hyperparameter search revealed that learning-dependent enrichment of state-value and policy representations of the task-performance-optimized deep RL agent closely resembled neural activity of the posterior parietal cortex (PPC). These representations were critical for the task performance in both systems. PPC neurons also exhibited representations of the internally defined subgoal, a feature of deep RL algorithms postulated to improve sample efficiency. Such striking resemblance between the artificial and biological networks and their functional convergence in sensorimotor integration offers new opportunities to better understand respective intelligent systems.
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42
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Oude Lohuis MN, Pie JL, Marchesi P, Montijn JS, de Kock CPJ, Pennartz CMA, Olcese U. Multisensory task demands temporally extend the causal requirement for visual cortex in perception. Nat Commun 2022; 13:2864. [PMID: 35606448 PMCID: PMC9126973 DOI: 10.1038/s41467-022-30600-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 05/09/2022] [Indexed: 01/02/2023] Open
Abstract
Primary sensory areas constitute crucial nodes during perceptual decision making. However, it remains unclear to what extent they mainly constitute a feedforward processing step, or rather are continuously involved in a recurrent network together with higher-order areas. We found that the temporal window in which primary visual cortex is required for the detection of identical visual stimuli was extended when task demands were increased via an additional sensory modality that had to be monitored. Late-onset optogenetic inactivation preserved bottom-up, early-onset responses which faithfully encoded stimulus features, and was effective in impairing detection only if it preceded a late, report-related phase of the cortical response. Increasing task demands were marked by longer reaction times and the effect of late optogenetic inactivation scaled with reaction time. Thus, independently of visual stimulus complexity, multisensory task demands determine the temporal requirement for ongoing sensory-related activity in V1, which overlaps with report-related activity. How primary sensory cortices contribute to decision making remains poorly understood. Here the authors report that increasing task demands extend the temporal window in which the primary visual cortex is required for detecting identical stimuli.
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43
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Miller CT, Gire D, Hoke K, Huk AC, Kelley D, Leopold DA, Smear MC, Theunissen F, Yartsev M, Niell CM. Natural behavior is the language of the brain. Curr Biol 2022; 32:R482-R493. [PMID: 35609550 PMCID: PMC10082559 DOI: 10.1016/j.cub.2022.03.031] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The breadth and complexity of natural behaviors inspires awe. Understanding how our perceptions, actions, and internal thoughts arise from evolved circuits in the brain has motivated neuroscientists for generations. Researchers have traditionally approached this question by focusing on stereotyped behaviors, either natural or trained, in a limited number of model species. This approach has allowed for the isolation and systematic study of specific brain operations, which has greatly advanced our understanding of the circuits involved. At the same time, the emphasis on experimental reductionism has left most aspects of the natural behaviors that have shaped the evolution of the brain largely unexplored. However, emerging technologies and analytical tools make it possible to comprehensively link natural behaviors to neural activity across a broad range of ethological contexts and timescales, heralding new modes of neuroscience focused on natural behaviors. Here we describe a three-part roadmap that aims to leverage the wealth of behaviors in their naturally occurring distributions, linking their variance with that of underlying neural processes to understand how the brain is able to successfully navigate the everyday challenges of animals' social and ecological landscapes. To achieve this aim, experimenters must harness one challenge faced by all neurobiological systems, namely variability, in order to gain new insights into the language of the brain.
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Affiliation(s)
- Cory T Miller
- Cortical Systems and Behavior Laboratory, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92039, USA.
| | - David Gire
- Department of Psychology, University of Washington, Guthrie Hall, Seattle, WA 98105, USA
| | - Kim Hoke
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
| | - Alexander C Huk
- Center for Perceptual Systems, Departments of Neuroscience and Psychology, University of Texas at Austin, 116 Inner Campus Drive, Austin, TX 78712, USA
| | - Darcy Kelley
- Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA
| | - David A Leopold
- Section of Cognitive Neurophysiology and Imaging, National Institute of Mental Health, 49 Convent Drive, Bethesda, MD 20892, USA
| | - Matthew C Smear
- Department of Psychology and Institute of Neuroscience, University of Oregon, 1227 University Street, Eugene, OR 97403, USA
| | - Frederic Theunissen
- Department of Psychology, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA 94720, USA
| | - Michael Yartsev
- Department of Bioengineering, University of California Berkeley, 306 Stanley Hall, Berkeley, CA 94720, USA
| | - Cristopher M Niell
- Department of Biology and Institute of Neuroscience, University of Oregon, 222 Huestis Hall, Eugene, OR 97403, USA.
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44
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Parker NF, Baidya A, Cox J, Haetzel LM, Zhukovskaya A, Murugan M, Engelhard B, Goldman MS, Witten IB. Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning. Cell Rep 2022; 39:110756. [PMID: 35584665 PMCID: PMC9218875 DOI: 10.1016/j.celrep.2022.110756] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 02/18/2022] [Accepted: 04/07/2022] [Indexed: 11/25/2022] Open
Abstract
How are actions linked with subsequent outcomes to guide choices? The nucleus accumbens, which is implicated in this process, receives glutamatergic inputs from the prelimbic cortex and midline regions of the thalamus. However, little is known about whether and how representations differ across these input pathways. By comparing these inputs during a reinforcement learning task in mice, we discovered that prelimbic cortical inputs preferentially represent actions and choices, whereas midline thalamic inputs preferentially represent cues. Choice-selective activity in the prelimbic cortical inputs is organized in sequences that persist beyond the outcome. Through computational modeling, we demonstrate that these sequences can support the neural implementation of reinforcement-learning algorithms, in both a circuit model based on synaptic plasticity and one based on neural dynamics. Finally, we test and confirm a prediction of our circuit models by direct manipulation of nucleus accumbens input neurons.
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Affiliation(s)
- Nathan F Parker
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Avinash Baidya
- Center for Neuroscience, University of California, Davis, Davis, CA 95616, USA; Department of Physics and Astronomy, University of California, Davis, Davis, CA 95616, USA
| | - Julia Cox
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Laura M Haetzel
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Anna Zhukovskaya
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Malavika Murugan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Ben Engelhard
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Mark S Goldman
- Center for Neuroscience, University of California, Davis, Davis, CA 95616, USA; Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA 95616, USA; Department of Ophthalmology and Vision Science, University of California, Davis, Davis, CA 95616, USA.
| | - Ilana B Witten
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Department of Psychology, Princeton University, Princeton, NJ 08544, USA.
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45
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Mice exhibit stochastic and efficient action switching during probabilistic decision making. Proc Natl Acad Sci U S A 2022; 119:e2113961119. [PMID: 35385355 PMCID: PMC9169659 DOI: 10.1073/pnas.2113961119] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
To obtain rewards in changing and uncertain environments, animals must adapt their behavior. We found that mouse choice and trial-to-trial switching behavior in a dynamic and probabilistic two-choice task could be modeled by equivalent theoretical, algorithmic, and descriptive models. These models capture components of evidence accumulation, choice history bias, and stochasticity in mouse behavior. Furthermore, they reveal that mice adapt their behavior in different environmental contexts by modulating their level of stickiness to their previous choice. Despite deviating from the behavior of a theoretically ideal observer, the empirical models achieve comparable levels of near-maximal reward. These results make predictions to guide interrogation of the neural mechanisms underlying flexible decision-making strategies. In probabilistic and nonstationary environments, individuals must use internal and external cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the process by which animals choose between actions, we trained mice in a task with time-varying reward probabilities. In our implementation of such a two-armed bandit task, thirsty mice use information about recent action and action–outcome histories to choose between two ports that deliver water probabilistically. Here we comprehensively modeled choice behavior in this task, including the trial-to-trial changes in port selection, i.e., action switching behavior. We find that mouse behavior is, at times, deterministic and, at others, apparently stochastic. The behavior deviates from that of a theoretically optimal agent performing Bayesian inference in a hidden Markov model (HMM). We formulate a set of models based on logistic regression, reinforcement learning, and sticky Bayesian inference that we demonstrate are mathematically equivalent and that accurately describe mouse behavior. The switching behavior of mice in the task is captured in each model by a stochastic action policy, a history-dependent representation of action value, and a tendency to repeat actions despite incoming evidence. The models parsimoniously capture behavior across different environmental conditionals by varying the stickiness parameter, and like the mice, they achieve nearly maximal reward rates. These results indicate that mouse behavior reaches near-maximal performance with reduced action switching and can be described by a set of equivalent models with a small number of relatively fixed parameters.
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46
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Neural structure of a sensory decoder for motor control. Nat Commun 2022; 13:1829. [PMID: 35383170 PMCID: PMC8983777 DOI: 10.1038/s41467-022-29457-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
The transformation of sensory input to motor output is often conceived as a decoder operating on neural representations. We seek a mechanistic understanding of sensory decoding by mimicking neural circuitry in the decoder’s design. The results of a simple experiment shape our approach. Changing the size of a target for smooth pursuit eye movements changes the relationship between the variance and mean of the evoked behavior in a way that contradicts the regime of “signal-dependent noise” and defies traditional decoding approaches. A theoretical analysis leads us to propose a circuit for pursuit that includes multiple parallel pathways and multiple sources of variation. Behavioral and neural responses with biomimetic statistics emerge from a biologically-motivated circuit model with noise in the pathway that is dedicated to flexibly adjusting the strength of visual-motor transmission. Our results demonstrate the power of re-imagining decoding as processing through the parallel pathways of neural systems. Behavioral variation is thought to result from noise in sensory representations or final motor commands. In this study, the authors investigate variability in eye movements and model that variability as resulting from noisy sensorimotor transformations occurring in the middle temporal visual area.
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47
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Pitsik EN, Frolov NS, Shusharina N, Hramov AE. Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network. SENSORS 2022; 22:s22072537. [PMID: 35408153 PMCID: PMC9003057 DOI: 10.3390/s22072537] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 02/01/2023]
Abstract
Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.
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Affiliation(s)
- Elena N. Pitsik
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia; (E.N.P.); (N.S.F.); (N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan 420500, Russia
| | - Nikita S. Frolov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia; (E.N.P.); (N.S.F.); (N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan 420500, Russia
| | - Natalia Shusharina
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia; (E.N.P.); (N.S.F.); (N.S.)
| | - Alexander E. Hramov
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia; (E.N.P.); (N.S.F.); (N.S.)
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan 420500, Russia
- Correspondence:
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48
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Rezaei MR, Hadjinicolaou AE, Cash SS, Eden UT, Yousefi A. Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data. Neural Comput 2022; 34:1100-1135. [PMID: 35344988 DOI: 10.1162/neco_a_01491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/08/2022] [Indexed: 11/04/2022]
Abstract
With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9.,Krembil Research Institute, University Health Network, Toronto, ON M5T 2S8.,KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada
| | - Alex E Hadjinicolaou
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, U.S.A.
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
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49
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To deconvolve, or not to deconvolve: Inferences of neuronal activities using calcium imaging data. J Neurosci Methods 2022; 366:109431. [PMID: 34856319 DOI: 10.1016/j.jneumeth.2021.109431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND With the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step. METHODS In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding. RESULTS We found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods. COMPARISON WITH EXISTING METHODS Our simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods. CONCLUSION We conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.
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50
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Condylis C, Ghanbari A, Manjrekar N, Bistrong K, Yao S, Yao Z, Nguyen TN, Zeng H, Tasic B, Chen JL. Dense functional and molecular readout of a circuit hub in sensory cortex. Science 2022; 375:eabl5981. [PMID: 34990233 DOI: 10.1126/science.abl5981] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Although single-cell transcriptomics of the neocortex has uncovered more than 300 putative cell types, whether this molecular classification predicts distinct functional roles is unclear. We combined two-photon calcium imaging with spatial transcriptomics to functionally and molecularly investigate cortical circuits. We characterized behavior-related responses across major neuronal subclasses in layers 2 or 3 of the primary somatosensory cortex as mice performed a tactile working memory task. We identified an excitatory intratelencephalic cell type, Baz1a, that exhibits high tactile feature selectivity. Baz1a neurons homeostatically maintain stimulus responsiveness during altered experience and show persistent enrichment of subsets of immediately early genes. Functional and anatomical connectivity reveals that Baz1a neurons residing in upper portions of layers 2 or 3 preferentially innervate somatostatin-expressing inhibitory neurons. This motif defines a circuit hub that orchestrates local sensory processing in superficial layers of the neocortex.
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Affiliation(s)
- Cameron Condylis
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.,Center for Neurophotonics, Boston University, Boston, MA 02215, USA
| | - Abed Ghanbari
- Department of Biology, Boston University, Boston, MA 02215, USA
| | | | - Karina Bistrong
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jerry L Chen
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.,Center for Neurophotonics, Boston University, Boston, MA 02215, USA.,Department of Biology, Boston University, Boston, MA 02215, USA.,Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
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