1
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Do J, Jung MW, Lee D. Automating licking bias correction in a two-choice delayed match-to-sample task to accelerate learning. Sci Rep 2023; 13:22768. [PMID: 38123637 PMCID: PMC10733387 DOI: 10.1038/s41598-023-49862-z] [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: 08/03/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
Animals often display choice bias, or a preference for one option over the others, which can significantly impede learning new tasks. Delayed match-to-sample (DMS) tasks with two-alternative choices of lickports on the left and right have been widely used to study sensory processing, working memory, and associative memory in head-fixed animals. However, extensive training time, primarily due to the animals' biased licking responses, limits their practical utility. Here, we present the implementation of an automated side bias correction system in an olfactory DMS task, where the lickport positions and the ratio of left- and right-rewarded trials are dynamically adjusted to counterbalance mouse's biased licking responses during training. The correction algorithm moves the preferred lickport farther away from the mouse's mouth and the non-preferred lickport closer, while also increasing the proportion of non-preferred side trials when biased licking occurs. We found that adjusting lickport distances and the proportions of left- versus right-rewarded trials effectively reduces the mouse's side bias. Further analyses reveal that these adjustments also correlate with subsequent improvements in behavioral performance. Our findings suggest that the automated side bias correction system is a valuable tool for enhancing the applicability of behavioral tasks involving two-alternative lickport choices.
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Affiliation(s)
- Jongrok Do
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Min Whan Jung
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, 34141, Republic of Korea.
| | - Doyun Lee
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
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2
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Buonomano DV, Buzsáki G, Davachi L, Nobre AC. Time for Memories. J Neurosci 2023; 43:7565-7574. [PMID: 37940593 PMCID: PMC10634580 DOI: 10.1523/jneurosci.1430-23.2023] [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: 07/27/2023] [Revised: 08/22/2023] [Accepted: 08/25/2023] [Indexed: 11/10/2023] Open
Abstract
The ability to store information about the past to dynamically predict and prepare for the future is among the most fundamental tasks the brain performs. To date, the problems of understanding how the brain stores and organizes information about the past (memory) and how the brain represents and processes temporal information for adaptive behavior have generally been studied as distinct cognitive functions. This Symposium explores the inherent link between memory and temporal cognition, as well as the potential shared neural mechanisms between them. We suggest that working memory and implicit timing are interconnected and may share overlapping neural mechanisms. Additionally, we explore how temporal structure is encoded in associative and episodic memory and, conversely, the influences of episodic memory on subsequent temporal anticipation and the perception of time. We suggest that neural sequences provide a general computational motif that contributes to timing and working memory, as well as the spatiotemporal coding and recall of episodes.
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Affiliation(s)
- Dean V Buonomano
- Department of Neurobiology, University of California, Los Angeles, California 90095
- Department of Psychology, University of California, Los Angeles, Los Angeles, California 90095
- Integrative Center for Learning and Memory, UCLA, Los Angeles, California 90025
| | - György Buzsáki
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, New York 10016
- Center for Neural Science, New York University, New York, New York 10003
| | - Lila Davachi
- Department of Psychology, Columbia University, New York, New York 10027
- Center for Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | - Anna C Nobre
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
- Department of Psychology, Yale University, New Haven, Connecticut 06510
- Wu Tsai Center for Neurocognition and Behavior, Wu Tsai Institute, Yale University, New Haven, Connecticut 06510
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3
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Zhou S, Seay M, Taxidis J, Golshani P, Buonomano DV. Multiplexing working memory and time in the trajectories of neural networks. Nat Hum Behav 2023; 7:1170-1184. [PMID: 37081099 PMCID: PMC10913811 DOI: 10.1038/s41562-023-01592-y] [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: 11/08/2022] [Accepted: 03/22/2023] [Indexed: 04/22/2023]
Abstract
Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases, the task-irrelevant component influenced performance. We then developed recurrent neural network (RNN) simulations that revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioural findings. During training, RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to-excitatory connections. Our results indicate that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.
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Affiliation(s)
- Shanglin Zhou
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael Seay
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Jiannis Taxidis
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Semel Institute for Neuroscience and Behavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- West Los Angeles VA Medical Center, Los Angeles, CA, USA
| | - Dean V Buonomano
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, CA, USA.
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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4
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Domanski APF, Kucewicz MT, Russo E, Tricklebank MD, Robinson ESJ, Durstewitz D, Jones MW. Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall. Curr Biol 2023; 33:1220-1236.e4. [PMID: 36898372 PMCID: PMC10728550 DOI: 10.1016/j.cub.2023.02.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/05/2023] [Accepted: 02/08/2023] [Indexed: 03/11/2023]
Abstract
Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.
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Affiliation(s)
- Aleksander P F Domanski
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; The Alan Turing Institute, British Library, 96 Euston Rd, London, UK; The Francis Crick Institute, 1 Midland Road, London, UK
| | - Michal T Kucewicz
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; BioTechMed Center, Brain & Mind Electrophysiology Laboratory, Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.
| | - Eleonora Russo
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany; Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany
| | - Mark D Tricklebank
- Centre for Neuroimaging Science, King's College London, Denmark Hill, London SE5 8AF, UK
| | - Emma S J Robinson
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Matt W Jones
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
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5
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Farrokhi A, Tafakori S, Daliri MR. Dynamic theta-modulated high frequency oscillations in rat medial prefrontal cortex during spatial working memory task. Physiol Behav 2022; 254:113912. [PMID: 35835179 DOI: 10.1016/j.physbeh.2022.113912] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/14/2022] [Accepted: 07/08/2022] [Indexed: 11/15/2022]
Abstract
Interaction of oscillatory rhythms at different frequencies is considered to provide a neuronal mechanism for information processing and transmission. These interactions have been suggested to have a vital role in cognitive functions such as working memory and decision-making. Here, we investigated the medial prefrontal cortex (mPFC), which is known to have a critical role in successful execution of spatial working memory tasks. We recorded local field potential oscillations from mPFC while rats performed a delayed-non-match-to-place (DNMTP) task. In the DNMTP task, the rat needed to decide actively about the pathway based on the information remembered in the first phase of each trial. Our analysis revealed a dynamic phase-amplitude coupling (PAC) between theta and high frequency oscillations (HFOs). This dynamic coupling emerged near the turning point and diminished afterward. Further, theta activity during the delay period, which is thought of as the maintenance phase, in the absence of the coupling, can predict task completion time. We previously reported diminished rat performance in the DNMTP task in response to electromagnetic radiation. Here, we report an increase in the theta rhythm during delay activity besides diminishing the coupling after electromagnetic radiation. These findings suggest that the different roles of the mPFC in working memory could be supported by separate mechanisms: Theta activity during the delay period for information maintenance and theta-HFOs phase-amplitude coupling relating to the decision-making procedure.
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Affiliation(s)
- Ashkan Farrokhi
- Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran, 16846-13114 Iran
| | - Shiva Tafakori
- Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran, 16846-13114 Iran
| | - Mohammad Reza Daliri
- Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran, 16846-13114 Iran.
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6
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Pittolo S, Yokoyama S, Willoughby DD, Taylor CR, Reitman ME, Tse V, Wu Z, Etchenique R, Li Y, Poskanzer KE. Dopamine activates astrocytes in prefrontal cortex via α1-adrenergic receptors. Cell Rep 2022; 40:111426. [PMID: 36170823 PMCID: PMC9555850 DOI: 10.1016/j.celrep.2022.111426] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/19/2022] [Accepted: 09/08/2022] [Indexed: 12/31/2022] Open
Abstract
The prefrontal cortex (PFC) is a hub for cognitive control, and dopamine profoundly influences its functions. In other brain regions, astrocytes sense diverse neurotransmitters and neuromodulators and, in turn, orchestrate regulation of neuroactive substances. However, basic physiology of PFC astrocytes, including which neuromodulatory signals they respond to and how they contribute to PFC function, is unclear. Here, we characterize divergent signaling signatures in mouse astrocytes of the PFC and primary sensory cortex, which show differential responsiveness to locomotion. We find that PFC astrocytes express receptors for dopamine but are unresponsive through the Gs/Gi-cAMP pathway. Instead, fast calcium signals in PFC astrocytes are time locked to dopamine release and are mediated by α1-adrenergic receptors both ex vivo and in vivo. Further, we describe dopamine-triggered regulation of extracellular ATP at PFC astrocyte territories. Thus, we identify astrocytes as active players in dopaminergic signaling in the PFC, contributing to PFC function though neuromodulator receptor crosstalk. Pittolo et al. demonstrate that the neuromodulator dopamine targets astrocytes, a type of brain cell, via receptors specific to another neuromodulator—norepinephrine. This study provides groundwork on how dopamine affects non-neuronal brain cells and suggests that crosstalk between neuromodulatory pathways occurs in vivo, with possible clinical implications.
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Affiliation(s)
- Silvia Pittolo
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Sae Yokoyama
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Drew D Willoughby
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Charlotte R Taylor
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Michael E Reitman
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Vincent Tse
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Zhaofa Wu
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Roberto Etchenique
- Departamento de Química Inorgánica, Analítica y Química Física, INQUIMAE, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Intendente Güiraldes 2160, Ciudad Universitaria, Pabellón 2, C1428EGA, Buenos Aires, Argentina
| | - Yulong Li
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Kira E Poskanzer
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA; Kavli Institute for Fundamental Neuroscience, San Francisco, CA, USA.
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7
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Voitov I, Mrsic-Flogel TD. Cortical feedback loops bind distributed representations of working memory. Nature 2022; 608:381-389. [PMID: 35896749 PMCID: PMC9365695 DOI: 10.1038/s41586-022-05014-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 06/22/2022] [Indexed: 11/16/2022]
Abstract
Working memory—the brain’s ability to internalize information and use it flexibly to guide behaviour—is an essential component of cognition. Although activity related to working memory has been observed in several brain regions1–3, how neural populations actually represent working memory4–7 and the mechanisms by which this activity is maintained8–12 remain unclear13–15. Here we describe the neural implementation of visual working memory in mice alternating between a delayed non-match-to-sample task and a simple discrimination task that does not require working memory but has identical stimulus, movement and reward statistics. Transient optogenetic inactivations revealed that distributed areas of the neocortex were required selectively for the maintenance of working memory. Population activity in visual area AM and premotor area M2 during the delay period was dominated by orderly low-dimensional dynamics16,17 that were, however, independent of working memory. Instead, working memory representations were embedded in high-dimensional population activity, present in both cortical areas, persisted throughout the inter-stimulus delay period, and predicted behavioural responses during the working memory task. To test whether the distributed nature of working memory was dependent on reciprocal interactions between cortical regions18–20, we silenced one cortical area (AM or M2) while recording the feedback it received from the other. Transient inactivation of either area led to the selective disruption of inter-areal communication of working memory. Therefore, reciprocally interconnected cortical areas maintain bound high-dimensional representations of working memory. Experiments in mice alternating between a visual working memory task and a task that is independent of working memory provide insight into the neural representation of working memory and the distributed nature of its maintenance.
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Affiliation(s)
- Ivan Voitov
- Sainsbury Wellcome Centre, University College London, London, UK. .,Biozentrum, University of Basel, Basel, Switzerland.
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8
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Roussy M, Mendoza-Halliday D, Martinez-Trujillo JC. Neural Substrates of Visual Perception and Working Memory: Two Sides of the Same Coin or Two Different Coins? Front Neural Circuits 2021; 15:764177. [PMID: 34899197 PMCID: PMC8662382 DOI: 10.3389/fncir.2021.764177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/25/2021] [Indexed: 11/18/2022] Open
Abstract
Visual perception occurs when a set of physical signals emanating from the environment enter the visual system and the brain interprets such signals as a percept. Visual working memory occurs when the brain produces and maintains a mental representation of a percept while the physical signals corresponding to that percept are not available. Early studies in humans and non-human primates demonstrated that lesions of the prefrontal cortex impair performance during visual working memory tasks but not during perceptual tasks. These studies attributed a fundamental role in working memory and a lesser role in visual perception to the prefrontal cortex. Indeed, single cell recording studies have found that neurons in the lateral prefrontal cortex of macaques encode working memory representations via persistent firing, validating the results of lesion studies. However, other studies have reported that neurons in some areas of the parietal and temporal lobe-classically associated with visual perception-similarly encode working memory representations via persistent firing. This prompted a line of enquiry about the role of the prefrontal and other associative cortices in working memory and perception. Here, we review evidence from single neuron studies in macaque monkeys examining working memory representations across different areas of the visual hierarchy and link them to studies examining the role of the same areas in visual perception. We conclude that neurons in early visual areas of both ventral (V1-V2-V4) and dorsal (V1-V3-MT) visual pathways of macaques mainly encode perceptual signals. On the other hand, areas downstream from V4 and MT contain subpopulations of neurons that encode both perceptual and/or working memory signals. Differences in cortical architecture (neuronal types, layer composition, and synaptic density and distribution) may be linked to the differential encoding of perceptual and working memory signals between early visual areas and higher association areas.
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Affiliation(s)
- Megan Roussy
- Department of Physiology and Pharmacology, Schulich School of Medicine & Dentistry, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Diego Mendoza-Halliday
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Julio C. Martinez-Trujillo
- Department of Physiology and Pharmacology, Schulich School of Medicine & Dentistry, Robarts Research Institute, University of Western Ontario, London, ON, Canada
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9
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Slow manifolds within network dynamics encode working memory efficiently and robustly. PLoS Comput Biol 2021; 17:e1009366. [PMID: 34525089 PMCID: PMC8475983 DOI: 10.1371/journal.pcbi.1009366] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 09/27/2021] [Accepted: 08/19/2021] [Indexed: 11/19/2022] Open
Abstract
Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.
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10
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Bae JW, Jeong H, Yoon YJ, Bae CM, Lee H, Paik SB, Jung MW. Parallel processing of working memory and temporal information by distinct types of cortical projection neurons. Nat Commun 2021; 12:4352. [PMID: 34272368 PMCID: PMC8285375 DOI: 10.1038/s41467-021-24565-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
It is unclear how different types of cortical projection neurons work together to support diverse cortical functions. We examined the discharge characteristics and inactivation effects of intratelencephalic (IT) and pyramidal tract (PT) neurons-two major types of cortical excitatory neurons that project to cortical and subcortical structures, respectively-in the deep layer of the medial prefrontal cortex in mice performing a delayed response task. We found stronger target-dependent firing of IT than PT neurons during the delay period. We also found the inactivation of IT neurons, but not PT neurons, impairs behavioral performance. In contrast, PT neurons carry more temporal information than IT neurons during the delay period. Our results indicate a division of labor between IT and PT projection neurons in the prefrontal cortex for the maintenance of working memory and for tracking the passage of time, respectively.
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Affiliation(s)
- Jung Won Bae
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Huijeong Jeong
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, Korea
| | - Young Ju Yoon
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Chan Mee Bae
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, Korea
| | - Hyeonsu Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Se-Bum Paik
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Min Whan Jung
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, Korea.
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11
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Gordleeva SY, Tsybina YA, Krivonosov MI, Ivanchenko MV, Zaikin AA, Kazantsev VB, Gorban AN. Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes. Front Cell Neurosci 2021; 15:631485. [PMID: 33867939 PMCID: PMC8044545 DOI: 10.3389/fncel.2021.631485] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 03/04/2021] [Indexed: 01/07/2023] Open
Abstract
We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.
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Affiliation(s)
- Susanna Yu Gordleeva
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
| | - Yuliya A Tsybina
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Mikhail I Krivonosov
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Mikhail V Ivanchenko
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey A Zaikin
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Center for Analysis of Complex Systems, Sechenov First Moscow State Medical University, Sechenov University, Moscow, Russia.,Institute for Women's Health and Department of Mathematics, University College London, London, United Kingdom
| | - Victor B Kazantsev
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia.,Neuroscience Research Institute, Samara State Medical University, Samara, Russia
| | - Alexander N Gorban
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Department of Mathematics, University of Leicester, Leicester, United Kingdom
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12
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Active maintenance of eligibility trace in rodent prefrontal cortex. Sci Rep 2020; 10:18860. [PMID: 33139778 PMCID: PMC7608665 DOI: 10.1038/s41598-020-75820-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/29/2020] [Indexed: 12/05/2022] Open
Abstract
Even though persistent neural activity has been proposed as a mechanism for maintaining eligibility trace, direct empirical evidence for active maintenance of eligibility trace has been lacking. We recorded neuronal activity in the medial prefrontal cortex (mPFC) in rats performing a dynamic foraging task in which a choice must be remembered until its outcome on the timescale of seconds for correct credit assignment. We found that mPFC neurons maintain significant choice signals during the time period between action selection and choice outcome. We also found that neural signals for choice, outcome, and action value converge in the mPFC when choice outcome was revealed. Our results indicate that the mPFC maintains choice signals necessary for temporal credit assignment in the form of persistent neural activity in our task. They also suggest that the mPFC might update action value by combining actively maintained eligibility trace with action value and outcome signals.
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郭 娅, 李 启, 姜 劲, 曹 勇, 冯 静, 楚 洪, 王 宏, 焦 学. [Review of cognitive enhancement techniques based on the combination of cognitive training and transcranial direct current stimulation]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2020; 37:903-909. [PMID: 33140616 PMCID: PMC10320545 DOI: 10.7507/1001-5515.201911079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Indexed: 11/03/2022]
Abstract
Cognitive enhancement refers to the technology of enhancing or expanding the cognitive and emotional abilities of people without psychosis based on relevant knowledge of neurobiology. The common methods of cognitive enhancement include transcranial direct current stimulation (tDCS) and cognitive training (CT). tDCS takes effect quickly, with a short effective time, while CT takes longer to work, requiring several weeks of training, with a longer effective time. In recent years, some researchers have begun to use the method of tDCS combined with CT to regulate the cognitive function. This paper will sort out and summarize this topic from five aspects: perception, attention, working memory, decision-making and other cognitive abilities. Finally, the application prospect and challenges of technology are prospected.
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Affiliation(s)
- 娅美 郭
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
- 中国航天员科研训练中心 人因工程国防科技重点实验室(北京 100094)Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing 100094, P.R.China
| | - 启杰 李
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
| | - 劲 姜
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
| | - 勇 曹
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
| | - 静达 冯
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
| | - 洪祚 楚
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
| | - 宏伟 王
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
| | - 学军 焦
- 航天工程大学 研究生院(北京 101416)Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China
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Using rat operant delayed match-to-sample task to identify neural substrates recruited with increased working memory load. ACTA ACUST UNITED AC 2020; 27:467-476. [PMID: 33060284 PMCID: PMC7571269 DOI: 10.1101/lm.052134.120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/17/2020] [Indexed: 11/25/2022]
Abstract
The delayed match-to-sample task (DMS) is used to probe working memory (WM) across species. While the involvement of the PFC in this task has been established, limited information exists regarding the recruitment of broader circuitry, especially under the low- versus high-WM load. We sought to address this question by using a variable-delay operant DMS task. Male Sprague-Dawley rats were trained and tested to determine their baseline WM performance across all (0- to 24-sec) delays. Next, rats were tested in a single DMS test with either 0- or 24-sec fixed delay, to assess low-/high-load WM performance. c-Fos mRNA expression was quantified within cortical and subcortical regions and correlated with WM performance. High WM load up-regulated overall c-Fos mRNA expression within the PrL, as well as within a subset of mGlu5+ cells, with load-dependent, local activation of protein kinase C (PKC) as the proposed underlying molecular mechanism. The PrL activity negatively correlated with choice accuracy during high load WM performance. A broader circuitry, including several subcortical regions, was found to be activated under low and/or high load conditions. These findings highlight the role of mGlu5- and/or PKC-dependent signaling within the PrL, and corresponding recruitment of subcortical regions during high-load WM performance.
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