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Zhao S, Zhou J, Zhang Y, Wang DH. γ And β Band Oscillation in Working Memory Given Sequential or Concurrent Multiple Items: A Spiking Network Model. eNeuro 2023; 10:ENEURO.0373-22.2023. [PMID: 37903618 PMCID: PMC10630927 DOI: 10.1523/eneuro.0373-22.2023] [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: 09/09/2022] [Revised: 10/10/2023] [Accepted: 10/22/2023] [Indexed: 11/01/2023] Open
Abstract
Working memory (WM) can maintain sequential and concurrent information, and the load enhances the γ band oscillation during the delay period. To provide a unified account for these phenomena in working memory, we investigated a continuous network model consisting of pyramidal cells, high-threshold fast-spiking interneurons (FS), and low-threshold nonfast-spiking interneurons (nFS) for working memory of sequential and concurrent directional cues. Our model exhibits the γ (30-100 Hz) and β (10-30 Hz) band oscillation during the retention of both concurrent cues and sequential cues. We found that the β oscillation results from the interaction between pyramidal cells and nFS, whereas the γ oscillation emerges from the interaction between pyramidal cells and FS because of the strong excitation elicited by cue presentation, shedding light on the mechanism underlying the enhancement of γ power in many cognitive executions.
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Affiliation(s)
- Shukuo Zhao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Jinpu Zhou
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Yongwen Zhang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Da-Hui Wang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
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2
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Hahn G, Kumar A, Schmidt H, Knösche TR, Deco G. Rate and oscillatory switching dynamics of a multilayer visual microcircuit model. eLife 2022; 11:77594. [PMID: 35994330 PMCID: PMC9395191 DOI: 10.7554/elife.77594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
The neocortex is organized around layered microcircuits consisting of a variety of excitatory and inhibitory neuronal types which perform rate- and oscillation-based computations. Using modeling, we show that both superficial and deep layers of the primary mouse visual cortex implement two ultrasensitive and bistable switches built on mutual inhibitory connectivity motives between somatostatin, parvalbumin, and vasoactive intestinal polypeptide cells. The switches toggle pyramidal neurons between high and low firing rate states that are synchronized across layers through translaminar connectivity. Moreover, inhibited and disinhibited states are characterized by low- and high-frequency oscillations, respectively, with layer-specific differences in frequency and power which show asymmetric changes during state transitions. These findings are consistent with a number of experimental observations and embed firing rate together with oscillatory changes within a switch interpretation of the microcircuit.
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Affiliation(s)
- Gerald Hahn
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Arvind Kumar
- Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Helmut Schmidt
- Brain Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Thomas R Knösche
- Brain Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Institute of Biomedical Engineering and Informatics, Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats, Barcelona, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
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3
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Oesterle J, Krämer N, Hennig P, Berens P. Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J Comput Neurosci 2022; 50:485-503. [PMID: 35932442 PMCID: PMC9666333 DOI: 10.1007/s10827-022-00827-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/14/2022] [Accepted: 07/02/2022] [Indexed: 11/30/2022]
Abstract
Understanding neural computation on the mechanistic level requires models of neurons and neuronal networks. To analyze such models one typically has to solve coupled ordinary differential equations (ODEs), which describe the dynamics of the underlying neural system. These ODEs are solved numerically with deterministic ODE solvers that yield single solutions with either no, or only a global scalar error indicator on precision. It can therefore be challenging to estimate the effect of numerical uncertainty on quantities of interest, such as spike-times and the number of spikes. To overcome this problem, we propose to use recently developed sampling-based probabilistic solvers, which are able to quantify such numerical uncertainties. They neither require detailed insights into the kinetics of the models, nor are they difficult to implement. We show that numerical uncertainty can affect the outcome of typical neuroscience simulations, e.g. jittering spikes by milliseconds or even adding or removing individual spikes from simulations altogether, and demonstrate that probabilistic solvers reveal these numerical uncertainties with only moderate computational overhead.
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Affiliation(s)
- Jonathan Oesterle
- Institute of Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Nicholas Krämer
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Philipp Hennig
- Department of Computer Science, University of Tübingen, Tübingen, Germany.,Max Planck Institute for Intelligent Systems, Tübingen, Germany.,Tübingen AI Center, University of Tübingen, Tübingen, Germany
| | - Philipp Berens
- Institute of Ophthalmic Research, University of Tübingen, Tübingen, Germany. .,Tübingen AI Center, University of Tübingen, Tübingen, Germany.
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4
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Guan A, Wang S, Huang A, Qiu C, Li Y, Li X, Wang J, Wang Q, Deng B. The role of gamma oscillations in central nervous system diseases: Mechanism and treatment. Front Cell Neurosci 2022; 16:962957. [PMID: 35966207 PMCID: PMC9374274 DOI: 10.3389/fncel.2022.962957] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/11/2022] [Indexed: 12/15/2022] Open
Abstract
Gamma oscillation is the synchronization with a frequency of 30–90 Hz of neural oscillations, which are rhythmic electric processes of neuron groups in the brain. The inhibitory interneuron network is necessary for the production of gamma oscillations, but certain disruptions such as brain inflammation, oxidative stress, and metabolic imbalances can cause this network to malfunction. Gamma oscillations specifically control the connectivity between different brain regions, which is crucial for perception, movement, memory, and emotion. Studies have linked abnormal gamma oscillations to conditions of the central nervous system, including Alzheimer’s disease, Parkinson’s disease, and schizophrenia. Evidence suggests that gamma entrainment using sensory stimuli (GENUS) provides significant neuroprotection. This review discusses the function of gamma oscillations in advanced brain activities from both a physiological and pathological standpoint, and it emphasizes gamma entrainment as a potential therapeutic approach for a range of neuropsychiatric diseases.
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Affiliation(s)
- Ao Guan
- Department of Anesthesiology, Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- School of Medicine, Xiamen University, Xiamen, China
| | - Shaoshuang Wang
- Department of Anesthesiology, Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Ailing Huang
- Department of Anesthesiology, School of Medicine, Xiang’an Hospital of Xiamen University, Xiamen University, Xiamen, China
| | - Chenyue Qiu
- Department of Anesthesiology, School of Medicine, Xiang’an Hospital of Xiamen University, Xiamen University, Xiamen, China
| | - Yansong Li
- Department of Anesthesiology, Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Xuying Li
- Department of Anesthesiology, Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Department of Anesthesiology, School of Medicine, Xiang’an Hospital of Xiamen University, Xiamen University, Xiamen, China
| | - Jinfei Wang
- School of Medicine, Xiamen University, Xiamen, China
| | - Qiang Wang
- Department of Anesthesiology, Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Qiang Wang,
| | - Bin Deng
- Department of Anesthesiology, Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Department of Anesthesiology, School of Medicine, Xiang’an Hospital of Xiamen University, Xiamen University, Xiamen, China
- *Correspondence: Bin Deng,
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Banaie Boroujeni K, Tiesinga P, Womelsdorf T. Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex. eLife 2021; 10:69111. [PMID: 34142661 PMCID: PMC8248985 DOI: 10.7554/elife.69111] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/17/2021] [Indexed: 12/27/2022] Open
Abstract
Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well-characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35–45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC). In LPFC, this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.
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Affiliation(s)
| | - Paul Tiesinga
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Thilo Womelsdorf
- Department of Psychology, Vanderbilt University, Nashville, United States.,Department of Biology, Centre for Vision Research, York University, Toronto, Canada
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