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De Paolis ML, Paoletti I, Zaccone C, Capone F, D'Amelio M, Krashia P. Transcranial alternating current stimulation (tACS) at gamma frequency: an up-and-coming tool to modify the progression of Alzheimer's Disease. Transl Neurodegener 2024; 13:33. [PMID: 38926897 DOI: 10.1186/s40035-024-00423-y] [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: 01/08/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
The last decades have witnessed huge efforts devoted to deciphering the pathological mechanisms underlying Alzheimer's Disease (AD) and to testing new drugs, with the recent FDA approval of two anti-amyloid monoclonal antibodies for AD treatment. Beyond these drug-based experimentations, a number of pre-clinical and clinical trials are exploring the benefits of alternative treatments, such as non-invasive stimulation techniques on AD neuropathology and symptoms. Among the different non-invasive brain stimulation approaches, transcranial alternating current stimulation (tACS) is gaining particular attention due to its ability to externally control gamma oscillations. Here, we outline the current knowledge concerning the clinical efficacy, safety, ease-of-use and cost-effectiveness of tACS on early and advanced AD, applied specifically at 40 Hz frequency, and also summarise pre-clinical results on validated models of AD and ongoing patient-centred trials.
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Affiliation(s)
- Maria Luisa De Paolis
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
| | - Ilaria Paoletti
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
| | - Claudio Zaccone
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
| | - Fioravante Capone
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128, Rome, Italy
| | - Marcello D'Amelio
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy.
- Department of Experimental Neurosciences, IRCCS Santa Lucia Foundation, Via del Fosso Di Fiorano, 64 - 00143, Rome, Italy.
| | - Paraskevi Krashia
- Department of Experimental Neurosciences, IRCCS Santa Lucia Foundation, Via del Fosso Di Fiorano, 64 - 00143, Rome, Italy
- Department of Sciences and Technologies for Sustainable Development and One Health, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
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2
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Voges N, Lima V, Hausmann J, Brovelli A, Battaglia D. Decomposing Neural Circuit Function into Information Processing Primitives. J Neurosci 2024; 44:e0157232023. [PMID: 38050070 PMCID: PMC10866194 DOI: 10.1523/jneurosci.0157-23.2023] [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: 01/27/2023] [Revised: 09/01/2023] [Accepted: 09/19/2023] [Indexed: 12/06/2023] Open
Abstract
It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.
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Affiliation(s)
- Nicole Voges
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Vinicius Lima
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
| | - Johannes Hausmann
- R&D Department, Hyland Switzerland Sarl, Corcelles NE 2035, Switzerland
| | - Andrea Brovelli
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Demian Battaglia
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67000, France
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3
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Ma L, Patel M. Mechanism of carbachol-induced 40 Hz gamma oscillations and the effects of NMDA activation on oscillatory dynamics in a model of the CA3 subfield of the hippocampus. J Theor Biol 2022; 548:111200. [PMID: 35716721 DOI: 10.1016/j.jtbi.2022.111200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 05/26/2022] [Accepted: 06/08/2022] [Indexed: 11/26/2022]
Abstract
Gamma oscillations are a prominent feature of various neural systems, including the CA3 subfield of the hippocampus. In CA3, in vitro carbachol application induces ∼40 Hz gamma oscillations in the network of glutamatergic excitatory pyramidal neurons (PNs) and local GABAergic inhibitory neurons (INs). Activation of NMDA receptors within CA3 leads to an increase in the frequency of carbachol-induced oscillations to ∼60 Hz, a broadening of the distribution of individual oscillation cycle frequencies, and a decrease in the time lag between PN and IN spike bursts. In this work, we develop a biophysical integrate-and-fire model of the CA3 subfield, we show that the dynamics of our model are in concordance with physiological observations, and we provide computational support for the hypothesis that the 'E-I' mechanism is responsible for the emergence of ∼40 Hz gamma oscillations in the absence of NMDA activation. We then incorporate NMDA receptors into our CA3 model, and we show that our model exhibits the increase in gamma oscillation frequency, broadening of the cycle frequency distribution, and decrease in the time lag between PN and IN spike bursts observed experimentally. Remarkably, we find an inverse relationship in our model between the net NMDA current delivered to PNs and INs in an oscillation cycle and cycle frequency. Furthermore, we find a disparate effect of NMDA receptors on PNs versus INs - we show that NMDA receptors on INs tend to increase oscillation frequency, while NMDA receptors on PNs tend to slightly decrease or not affect oscillation frequency. We find that these observations can be explained if NMDA activity above a threshold level causes a shift in the mechanism underlying gamma oscillations; in the absence of NMDA receptors, the 'E-I' mechanism is primarily responsible for the generation of gamma oscillations (at 40 Hz), while when NMDA receptors are active, the mechanism of gamma oscillations shifts to the 'I-I' mechanism, and we argue that within the 'I-I' regime (which displays a higher baseline oscillation frequency of ∼60 Hz), slight changes in the level of NMDA activity are inversely related to cycle frequency.
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Affiliation(s)
- Linda Ma
- Department of Mathematics, William & Mary, United States.
| | - Mainak Patel
- Department of Mathematics, William & Mary, United States.
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4
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Palabas T, Longtin A, Ghosh D, Uzuntarla M. Controlling the spontaneous firing behavior of a neuron with astrocyte. CHAOS (WOODBURY, N.Y.) 2022; 32:051101. [PMID: 35649970 DOI: 10.1063/5.0093234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 04/18/2022] [Indexed: 06/15/2023]
Abstract
Mounting evidence in recent years suggests that astrocytes, a sub-type of glial cells, not only serve metabolic and structural support for neurons and synapses but also play critical roles in the regulation of proper functioning of the nervous system. In this work, we investigate the effect of astrocytes on the spontaneous firing activity of a neuron through a combined model that includes a neuron-astrocyte pair. First, we show that an astrocyte may provide a kind of multistability in neuron dynamics by inducing different firing modes such as random and bursty spiking. Then, we identify the underlying mechanism of this behavior and search for the astrocytic factors that may have regulatory roles in different firing regimes. More specifically, we explore how an astrocyte can participate in the occurrence and control of spontaneous irregular spiking activity of a neuron in random spiking mode. Additionally, we systematically investigate the bursty firing regime dynamics of the neuron under the variation of biophysical facts related to the intracellular environment of the astrocyte. It is found that an astrocyte coupled to a neuron can provide a control mechanism for both spontaneous firing irregularity and burst firing statistics, i.e., burst regularity and size.
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Affiliation(s)
- Tugba Palabas
- Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100 Zonguldak, Turkey
| | - Andre Longtin
- Department of Physics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India
| | - Muhammet Uzuntarla
- Department of Bioengineering, Gebze Technical University, 41400 Kocaeli, Turkey
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5
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Roohi N, Valizadeh A. Role of Interaction Delays in the Synchronization of Inhibitory Networks. Neural Comput 2022; 34:1425-1447. [PMID: 35534004 DOI: 10.1162/neco_a_01500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 01/25/2022] [Indexed: 11/04/2022]
Abstract
Neural oscillations provide a means for efficient and flexible communication among different brain areas. Understanding the mechanisms of the generation of brain oscillations is crucial to determine principles of communication and information transfer in the brain circuits. It is well known that the inhibitory neurons play a major role in the generation of oscillations in the gamma range, in pure inhibitory networks, or in the networks composed of excitatory and inhibitory neurons. In this study, we explore the impact of different parameters and, in particular, the delay in the transmission of the signals between the neurons, on the dynamics of inhibitory networks. We show that increasing delay in a reasonable range increases the synchrony and stabilizes the oscillations. Unstable gamma oscillations characterized by a highly variable amplitude of oscillations can be observed in an intermediate range of delays. We show that in this range of delays, other experimentally observed phenomena such as sparse firing, variable amplitude and period, and the correlation between the instantaneous amplitude and period could be observed. The results broaden our understanding of the mechanism of the generation of the gamma oscillations in the inhibitory networks, known as the ING (interneuron-gamma) mechanism.
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Affiliation(s)
- Nariman Roohi
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences, Niavaran, Tehran, Iran
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6
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Barbosa J, Babushkin V, Temudo A, Sreenivasan KK, Compte A. Across-Area Synchronization Supports Feature Integration in a Biophysical Network Model of Working Memory. Front Neural Circuits 2021; 15:716965. [PMID: 34616279 PMCID: PMC8489684 DOI: 10.3389/fncir.2021.716965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 08/11/2021] [Indexed: 11/13/2022] Open
Abstract
Working memory function is severely limited. One key limitation that constrains the ability to maintain multiple items in working memory simultaneously is so-called swap errors. These errors occur when an inaccurate response is in fact accurate relative to a non-target stimulus, reflecting the failure to maintain the appropriate association or "binding" between the features that define one object (e.g., color and location). The mechanisms underlying feature binding in working memory remain unknown. Here, we tested the hypothesis that features are bound in memory through synchrony across feature-specific neural assemblies. We built a biophysical neural network model composed of two one-dimensional attractor networks - one for color and one for location - simulating feature storage in different cortical areas. Within each area, gamma oscillations were induced during bump attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. As a result, different memorized items were held at different phases of the network's oscillation. These two areas were then reciprocally connected via weak cortico-cortical excitation, accomplishing binding between color and location through the synchronization of pairs of bumps across the two areas. Encoding and decoding of color-location associations was accomplished through rate coding, overcoming a long-standing limitation of binding through synchrony. In some simulations, swap errors arose: "color bumps" abruptly changed their phase relationship with "location bumps." This model, which leverages the explanatory power of similar attractor models, specifies a plausible mechanism for feature binding and makes specific predictions about swap errors that are testable at behavioral and neurophysiological levels.
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Affiliation(s)
- Joao Barbosa
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Supérieure – PSL Research University, Paris, France
| | - Vahan Babushkin
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Ainsley Temudo
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Kartik K. Sreenivasan
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Albert Compte
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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Baysal V, Erkan E, Yilmaz E. Impacts of autapse on chaotic resonance in single neurons and small-world neuronal networks. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200237. [PMID: 33840215 DOI: 10.1098/rsta.2020.0237] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/08/2020] [Indexed: 05/22/2023]
Abstract
Chaotic resonance (CR) is a new phenomenon induced by an intermediate level of chaotic signal intensity in neuronal systems. In the current study, we investigated the effects of autapse on the CR phenomenon in single neurons and small-world (SW) neuronal networks. In single neurons, we assume that the neuron has only one autapse modelled as electrical, excitatory chemical and inhibitory chemical synapse, respectively. Then, we analysed the effects of each one on the CR, separately. Obtained results revealed that, regardless of its type, autapse significantly increases the chaotic resonance of the appropriate autaptic parameter's values. It is also observed that, at the optimal chaotic current intensity, the multiple CR emerges depending on autaptic time delay for all the autapse types when the autaptic delay time or its integer multiples match the half period or period of the weak signal. In SW networks, we investigated the effects of chaotic activity on the prorogation of pacemaker activity, where pacemaker neurons have different kinds of autapse as considered in single neuron cases. Obtained results revealed that excitatory and electrical autapses prominently increase the prorogation of pacemaker activity, whereas inhibitory autapse reduces or does not change it. Also, the best propagation was obtained when the autapse was excitatory. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 2)'.
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Affiliation(s)
- Veli Baysal
- Department of Computer Engineering, Bartın University, 74110 Bartın, Turkey
| | - Erdem Erkan
- Department of Computer Engineering, Bartın University, 74110 Bartın, Turkey
| | - Ergin Yilmaz
- Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100 Zonguldak, Turkey
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8
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Novikov N, Zakharov D, Moiseeva V, Gutkin B. Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs. Front Neural Circuits 2021; 15:647944. [PMID: 33967703 PMCID: PMC8096914 DOI: 10.3389/fncir.2021.647944] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/19/2021] [Indexed: 01/22/2023] Open
Abstract
According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the "common-noise" groups compared to the "independent-noise" groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.
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Affiliation(s)
- Nikita Novikov
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia
| | - Denis Zakharov
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia
| | - Victoria Moiseeva
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia
| | - Boris Gutkin
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia.,Group for Neural Theory, LNC2 INSERM U960, Départment d'Études Cognitives, École Normale Supérieure, PSL Research Université, Paris, France
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9
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Hooshmandi M, Truong VT, Fields E, Thomas RE, Wong C, Sharma V, Gantois I, Soriano Roque P, Chalkiadaki K, Wu N, Chakraborty A, Tahmasebi S, Prager-Khoutorsky M, Sonenberg N, Suvrathan A, Watt AJ, Gkogkas CG, Khoutorsky A. 4E-BP2-dependent translation in cerebellar Purkinje cells controls spatial memory but not autism-like behaviors. Cell Rep 2021; 35:109036. [PMID: 33910008 DOI: 10.1016/j.celrep.2021.109036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/15/2021] [Accepted: 04/06/2021] [Indexed: 11/19/2022] Open
Abstract
Recent studies have demonstrated that selective activation of mammalian target of rapamycin complex 1 (mTORC1) in the cerebellum by deletion of the mTORC1 upstream repressors TSC1 or phosphatase and tensin homolog (PTEN) in Purkinje cells (PCs) causes autism-like features and cognitive deficits. However, the molecular mechanisms by which overactivated mTORC1 in the cerebellum engenders these behaviors remain unknown. The eukaryotic translation initiation factor 4E-binding protein 2 (4E-BP2) is a central translational repressor downstream of mTORC1. Here, we show that mice with selective ablation of 4E-BP2 in PCs display a reduced number of PCs, increased regularity of PC action potential firing, and deficits in motor learning. Surprisingly, although spatial memory is impaired in these mice, they exhibit normal social interaction and show no deficits in repetitive behavior. Our data suggest that, downstream of mTORC1/4E-BP2, there are distinct cerebellar mechanisms independently controlling social behavior and memory formation.
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Affiliation(s)
- Mehdi Hooshmandi
- Department of Anesthesia and Faculty of Dentistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Vinh Tai Truong
- Department of Biochemistry and Goodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada
| | - Eviatar Fields
- Department of Biology, McGill University, Montreal, QC H3A 1A3, Canada; Integrated Program in Neuroscience, McGill University, Montreal, QC H3A 2B4, Canada
| | - Riya Elizabeth Thomas
- Integrated Program in Neuroscience, McGill University, Montreal, QC H3A 2B4, Canada; Centre for Research in Neuroscience, Research Institute of the McGill University Health Centre, Montreal QC, H3G1A4, Canada; Department of Neurology and Neurosurgery, Department of Pediatrics, McGill University, Montreal QC, H3G1A4, Canada
| | - Calvin Wong
- Department of Anesthesia and Faculty of Dentistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Vijendra Sharma
- Department of Biochemistry and Goodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada
| | - Ilse Gantois
- Department of Biochemistry and Goodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada
| | - Patricia Soriano Roque
- Department of Anesthesia and Faculty of Dentistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Kleanthi Chalkiadaki
- Division of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, University Campus, 45110 Ioannina, Greece
| | - Neil Wu
- Department of Anesthesia and Faculty of Dentistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Anindyo Chakraborty
- Department of Anesthesia and Faculty of Dentistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Soroush Tahmasebi
- Department of Pharmacology and Regenerative Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
| | | | - Nahum Sonenberg
- Department of Biochemistry and Goodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada
| | - Aparna Suvrathan
- Centre for Research in Neuroscience, Research Institute of the McGill University Health Centre, Montreal QC, H3G1A4, Canada; Department of Neurology and Neurosurgery, Department of Pediatrics, McGill University, Montreal QC, H3G1A4, Canada
| | - Alanna J Watt
- Department of Biology, McGill University, Montreal, QC H3A 1A3, Canada
| | - Christos G Gkogkas
- Division of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, University Campus, 45110 Ioannina, Greece.
| | - Arkady Khoutorsky
- Department of Anesthesia and Faculty of Dentistry, McGill University, Montreal, QC H3G 1Y6, Canada; Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC H3A 0G1, Canada.
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10
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Rochart R, Liu Q, Fonteh AN, Harrington MG, Arakaki X. Compromised Behavior and Gamma Power During Working Memory in Cognitively Healthy Individuals With Abnormal CSF Amyloid/Tau. Front Aging Neurosci 2020; 12:574214. [PMID: 33192465 PMCID: PMC7591805 DOI: 10.3389/fnagi.2020.574214] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/22/2020] [Indexed: 11/24/2022] Open
Abstract
Research shows that gamma activity changes in Alzheimer’s disease (AD), revealing synaptic pathology and potential therapeutic applications. We aim to explore whether cognitive challenge combined with quantitative EEG (qEEG) can unmask abnormal gamma frequency power in healthy individuals at high risk of developing AD. We analyzed low (30–50 Hz) and high gamma (50–80 Hz) power over six brain regions at EEG sensor level (frontal/central/parietal/left temporal/right temporal/occipital) in a dataset collected from an aging cohort during N-back working memory (WM) testing at two different load conditions (N = 0 or 2). Cognitively healthy (CH) study participants (≥60 years old) of both sexes were divided into two subgroups: normal amyloid/tau ratios (CH-NAT, n = 10) or pathological amyloid/tau (CH-PAT, n = 14) in cerebrospinal fluid (CSF). During low load (0-back) challenge, low gamma is higher in CH-PATs than CH-NATs over frontal and central regions (p = 0.014∼0.032, effect size (Cohen’s d) = 0.95∼1.11). However, during high load (2-back) challenge, low gamma is lower in CH-PATs compared to CH-NATs over the left temporal region (p = 0.045, Cohen’s d = −0.96), and high gamma is lower over the parietal region (p = 0.035, Cohen’s d = −1.02). Overall, our studies show a medium to large negative effect size across the scalp (Cohen’s d = −0.51∼−1.02). In addition, low gamma during 2-back is positively correlated with 0-back accuracy over all regions except the occipital region only in CH-NATs (r = 0.69∼0.77, p = 0.0098∼0.027); high gamma during 2-back correlated positively with 0-back accuracy over all regions in CH-NATs (r = 0.68∼0.78, p = 0.007∼0.030); high gamma during 2-back negatively correlated with 0-back response time over parietal, right temporal, and occipital regions in CH-NATs (r = −0.70∼−0.66, p = 0.025∼0.037). We interpret these preliminary results to show: (1) gamma power is compromised in AD-biomarker positive individuals, who are otherwise cognitively healthy (CH-PATs); (2) gamma is associated with WM performance in normal aging (CH-NATs) (most significantly in the frontoparietal region). Our pilot findings encourage further investigations in combining cognitive challenges and qEEG in developing neurophysiology-based markers for identifying individuals in the prodromal stage, to help improving our understanding of AD pathophysiology and the contributions of low- and high-frequency gamma oscillations in cognitive functions.
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Affiliation(s)
- Roger Rochart
- Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, United States
| | - Quanying Liu
- Department of Biomedical Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China
| | - Alfred N Fonteh
- Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, United States
| | - Michael G Harrington
- Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, United States
| | - Xianghong Arakaki
- Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, United States
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11
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Sherfey J, Ardid S, Miller EK, Hasselmo ME, Kopell NJ. Prefrontal oscillations modulate the propagation of neuronal activity required for working memory. Neurobiol Learn Mem 2020; 173:107228. [PMID: 32561459 DOI: 10.1016/j.nlm.2020.107228] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 02/01/2020] [Accepted: 04/01/2020] [Indexed: 01/11/2023]
Abstract
Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma-frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.
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Affiliation(s)
- Jason Sherfey
- Center for Systems Neuroscience, Department of Psychological and Brain Sciences, Boston University, MA 02215, United States; The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States.
| | - Salva Ardid
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT 06510, United States
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Department of Psychological and Brain Sciences, Boston University, MA 02215, United States
| | - Nancy J Kopell
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States.
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12
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Xu X, Hanganu-Opatz IL, Bieler M. Cross-Talk of Low-Level Sensory and High-Level Cognitive Processing: Development, Mechanisms, and Relevance for Cross-Modal Abilities of the Brain. Front Neurorobot 2020; 14:7. [PMID: 32116637 PMCID: PMC7034303 DOI: 10.3389/fnbot.2020.00007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 01/27/2020] [Indexed: 12/18/2022] Open
Abstract
The emergence of cross-modal learning capabilities requires the interaction of neural areas accounting for sensory and cognitive processing. Convergence of multiple sensory inputs is observed in low-level sensory cortices including primary somatosensory (S1), visual (V1), and auditory cortex (A1), as well as in high-level areas such as prefrontal cortex (PFC). Evidence shows that local neural activity and functional connectivity between sensory cortices participate in cross-modal processing. However, little is known about the functional interplay between neural areas underlying sensory and cognitive processing required for cross-modal learning capabilities across life. Here we review our current knowledge on the interdependence of low- and high-level cortices for the emergence of cross-modal processing in rodents. First, we summarize the mechanisms underlying the integration of multiple senses and how cross-modal processing in primary sensory cortices might be modified by top-down modulation of the PFC. Second, we examine the critical factors and developmental mechanisms that account for the interaction between neuronal networks involved in sensory and cognitive processing. Finally, we discuss the applicability and relevance of cross-modal processing for brain-inspired intelligent robotics. An in-depth understanding of the factors and mechanisms controlling cross-modal processing might inspire the refinement of robotic systems by better mimicking neural computations.
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Affiliation(s)
- Xiaxia Xu
- Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ileana L Hanganu-Opatz
- Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Malte Bieler
- Laboratory for Neural Computation, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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13
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Zhang T, Pan X, Xu X, Wang R. A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level. Cogn Neurodyn 2019; 13:579-599. [PMID: 31741694 PMCID: PMC6825110 DOI: 10.1007/s11571-019-09540-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 05/08/2019] [Accepted: 05/12/2019] [Indexed: 11/24/2022] Open
Abstract
Visual attention is a selective process of visual information and improves perceptual performance by modulating activities of neurons in the visual system. It has been reported that attention increased firing rates of neurons, reduced their response variability and improved reliability of coding relevant stimuli. Recent neurophysiological studies demonstrated that attention also enhanced the synaptic efficacy between neurons mediated through NMDA and AMPA receptors. Majority of computational models of attention usually are based on firing rates, which cannot explain attentional modulations observed at the synaptic level. To understand mechanisms of attentional modulations at the synaptic level, we proposed a neural network consisting of three layers, corresponding to three different brain regions. Each layer has excitatory and inhibitory neurons. Each neuron was modeled by the Hodgkin-Huxley model. The connections between neurons were through excitatory AMPA and NMDA receptors, as well as inhibitory GABAA receptors. Since the binding process of neurotransmitters with receptors is stochastic in the synapse, it is hypothesized that attention could reduce the variation of the stochastic binding process and increase the fraction of bound receptors in the model. We investigated how attention modulated neurons' responses at the synaptic level on the basis of this hypothesis. Simulated results demonstrated that attention increased firing rates of neurons and reduced their response variability. The attention-induced effects were stronger in higher regions compared to those in lower regions, and stronger for inhibitory neurons than for excitatory neurons. In addition, AMPA receptor antagonist (CNQX) impaired attention-induced modulations on neurons' responses, while NMDA receptor antagonist (APV) did not. These results suggest that attention may modulate neuronal activity at the synaptic level.
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Affiliation(s)
- Tao Zhang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Meilong Road 130, Shanghai, People’s Republic of China
| | - Xiaochuan Pan
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Meilong Road 130, Shanghai, People’s Republic of China
| | - Xuying Xu
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Meilong Road 130, Shanghai, People’s Republic of China
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Meilong Road 130, Shanghai, People’s Republic of China
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14
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Biased competition in the absence of input bias revealed through corticostriatal computation. Proc Natl Acad Sci U S A 2019; 116:8564-8569. [PMID: 30962383 DOI: 10.1073/pnas.1812535116] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Classical accounts of biased competition require an input bias to resolve the competition between neuronal ensembles driving downstream processing. However, flexible and reliable selection of behaviorally relevant ensembles can occur with unbiased stimulation: striatal D1 and D2 spiny projection neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We here present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded biased competition in the presence of balanced inputs. First, tonic input strength determines which one of the two SPN phenotypes exhibits a higher mean firing rate. Second, low-strength oscillatory inputs induce higher firing rate in D2 SPNs but higher coherence between D1 SPNs. Third, high-strength inputs oscillating at distinct frequencies can preferentially activate D1 or D2 SPN populations. Of these mechanisms, only the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortex.
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15
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Sherfey JS, Ardid S, Hass J, Hasselmo ME, Kopell NJ. Flexible resonance in prefrontal networks with strong feedback inhibition. PLoS Comput Biol 2018; 14:e1006357. [PMID: 30091975 PMCID: PMC6103521 DOI: 10.1371/journal.pcbi.1006357] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 08/21/2018] [Accepted: 07/11/2018] [Indexed: 11/24/2022] Open
Abstract
Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.
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Affiliation(s)
- Jason S. Sherfey
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
- Department of Psychological and Brain Sciences, Center for Systems Neuroscience, Boston University, Massachusetts, United States of America
| | - Salva Ardid
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
| | - Joachim Hass
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
- Faculty of Applied Psychology, SRH University for Applied Sciences Heidelberg, Heidelberg, Germany
| | - Michael E. Hasselmo
- Department of Psychological and Brain Sciences, Center for Systems Neuroscience, Boston University, Massachusetts, United States of America
| | - Nancy J. Kopell
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
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16
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Working Memory and Decision-Making in a Frontoparietal Circuit Model. J Neurosci 2017; 37:12167-12186. [PMID: 29114071 DOI: 10.1523/jneurosci.0343-17.2017] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 08/24/2017] [Accepted: 09/19/2017] [Indexed: 12/25/2022] Open
Abstract
Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and distinct roles of these areas and the nature of their coordination in cognitive function remain poorly understood. Biophysically based computational models of cortical circuits have provided insights into the mechanisms supporting these functions, yet they have primarily focused on the local microcircuit level, raising questions about the principles for distributed cognitive computation in multiregional networks. To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits. The circuit architecture includes hierarchical differences in local recurrent structure and implements reciprocal long-range projections. This parsimonious model captures a range of behavioral and neuronal features of frontoparietal circuits across multiple WM and DM paradigms. In the context of WM, both areas exhibit persistent activity, but, in response to intervening distractors, PPC transiently encodes distractors while PFC filters distractors and supports WM robustness. With regard to DM, the PPC module generates graded representations of accumulated evidence supporting target selection, while the PFC module generates more categorical responses related to action or choice. These findings suggest computational principles for distributed, hierarchical processing in cortex during cognitive function and provide a framework for extension to multiregional models.SIGNIFICANCE STATEMENT Working memory and decision-making are fundamental "building blocks" of cognition, and deficits in these functions are associated with neuropsychiatric disorders such as schizophrenia. These cognitive functions engage distributed networks with prefrontal cortex (PFC) and posterior parietal cortex (PPC) at the core. It is not clear, however, what the contributions of PPC and PFC are in light of the computations that subserve working memory and decision-making. We constructed a biophysical model of a reciprocally connected frontoparietal circuit that revealed shared and distinct functions for the PFC and PPC across working memory and decision-making tasks. Our parsimonious model connects circuit-level properties to cognitive functions and suggests novel design principles beyond those of local circuits for cognitive processing in multiregional brain networks.
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17
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Paneri S, Gregoriou GG. Top-Down Control of Visual Attention by the Prefrontal Cortex. Functional Specialization and Long-Range Interactions. Front Neurosci 2017; 11:545. [PMID: 29033784 PMCID: PMC5626849 DOI: 10.3389/fnins.2017.00545] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 09/19/2017] [Indexed: 11/13/2022] Open
Abstract
The ability to select information that is relevant to current behavioral goals is the hallmark of voluntary attention and an essential part of our cognition. Attention tasks are a prime example to study at the neuronal level, how task related information can be selectively processed in the brain while irrelevant information is filtered out. Whereas, numerous studies have focused on elucidating the mechanisms of visual attention at the single neuron and population level in the visual cortices, considerably less work has been devoted to deciphering the distinct contribution of higher-order brain areas, which are known to be critical for the employment of attention. Among these areas, the prefrontal cortex (PFC) has long been considered a source of top-down signals that bias selection in early visual areas in favor of the attended features. Here, we review recent experimental data that support the role of PFC in attention. We examine the existing evidence for functional specialization within PFC and we discuss how long-range interactions between PFC subregions and posterior visual areas may be implemented in the brain and contribute to the attentional modulation of different measures of neural activity in visual cortices.
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Affiliation(s)
- Sofia Paneri
- Faculty of Medicine, University of Crete, Heraklion, Greece.,Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Georgia G Gregoriou
- Faculty of Medicine, University of Crete, Heraklion, Greece.,Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas, Heraklion, Greece
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18
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Romeo A, Supèr H. Bump competition and lattice solutions in two-dimensional neural fields. Neural Netw 2017; 94:141-158. [PMID: 28779599 DOI: 10.1016/j.neunet.2017.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 05/19/2017] [Accepted: 07/02/2017] [Indexed: 10/19/2022]
Abstract
Some forms of competition among activity bumps in a two-dimensional neural field are studied. First, threshold dynamics is included and rivalry evolutions are considered. The relations between parameters and dominance durations can match experimental observations about ageing. Next, the threshold dynamics is omitted from the model and we focus on the properties of the steady-state. From noisy inputs, hexagonal grids are formed by a symmetry-breaking process. Particular issues about solution existence and stability conditions are considered. We speculate that they affect the possibility of producing basis grids which may be combined to form feature maps.
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Affiliation(s)
- August Romeo
- Department of Cognition, Development and Educational Psychology, Faculty of Psychology, University of Barcelona, Spain
| | - Hans Supèr
- Department of Cognition, Development and Educational Psychology, Faculty of Psychology, University of Barcelona, Spain; Institut de Neurociències, University of Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Spain.
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19
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Verguts T. Binding by Random Bursts: A Computational Model of Cognitive Control. J Cogn Neurosci 2017; 29:1103-1118. [DOI: 10.1162/jocn_a_01117] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Abstract
A neural synchrony model of cognitive control is proposed. It construes cognitive control as a higher-level action to synchronize lower-level brain areas. Here, a controller prefrontal area (medial frontal cortex) can synchronize two cortical processing areas. The synchrony is achieved by a random theta frequency-locked neural burst sent to both areas. The choice of areas that receive this burst is determined by lateral frontal cortex. As a result of this synchrony, communication between the two areas becomes more efficient. The model is tested on the classical Stroop cognitive control task, and its operation is explored in several simulations. Both reactive and proactive controls are implemented via theta power modulation. Increasing theta power improves behavioral performance; furthermore, via theta–gamma phase–amplitude coupling, theta also increases gamma frequency power and synchrony in posterior processing areas. Thus, the model solves a central computational problem for cognitive control (how to allow rapid communication between arbitrary brain areas), while making rich contact with behavioral and neurophysiological data.
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20
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Rule ME, Vargas-Irwin CE, Donoghue JP, Truccolo W. Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex. J Neurophysiol 2017; 117:1524-1543. [PMID: 28100654 DOI: 10.1152/jn.00651.2016] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 01/09/2017] [Accepted: 01/17/2017] [Indexed: 01/06/2023] Open
Abstract
Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation.NEW & NOTEWORTHY We show that whereas motor cortex spiking rates and beta (~20 Hz) spiking rhythmicity remain sustained during steady-state movement preparation periods, β-local field potential (β-LFP) oscillations emerge, in contrast, as transient events. Furthermore, the β-LFP phase at which neurons spike drifts: phase coupling is typically weak or absent. This dissociation points to two sources of variation in the level of motor cortex beta: one that impacts single-neuron spiking and another related to the generation of measured mesoscopic β-LFPs.
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Affiliation(s)
- Michael E Rule
- Department of Neuroscience, Brown University, Providence, Rhode Island
| | | | - John P Donoghue
- Department of Neuroscience, Brown University, Providence, Rhode Island.,Institute for Brain Science, Brown University, Providence, Rhode Island; and.,Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, Rhode Island
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, Rhode Island; .,Institute for Brain Science, Brown University, Providence, Rhode Island; and.,Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, Rhode Island
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21
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Romeo A, Supèr H. Global oscillation regime change by gated inhibition. Neural Netw 2016; 82:76-83. [PMID: 27479874 DOI: 10.1016/j.neunet.2016.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 06/06/2016] [Accepted: 06/26/2016] [Indexed: 10/21/2022]
Abstract
The role of sensory inputs in the modelling of synchrony regimes is exhibited by means of networks of spiking cells where the relative strength of the inhibitory interaction is controlled by the activation of a linear unit working as a gating variable. Adaptation to stimulus size is determined by the value of a changing length scale, modelled by the time-varying radius of a circular receptive field. In this set-up, 'consolidation' time intervals relevant to attentional effects are shown to depend on the dynamics governing the evolution of the introduced length scale.
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Affiliation(s)
- August Romeo
- Department of Cognition, Development and Educational Psychology, Faculty of Psychology, University of Barcelona, Spain
| | - Hans Supèr
- Department of Cognition, Development and Educational Psychology, Faculty of Psychology, University of Barcelona, Spain; Institute of Neurosciences, Faculty of Psychology, University of Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Spain.
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22
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Regulation of Irregular Neuronal Firing by Autaptic Transmission. Sci Rep 2016; 6:26096. [PMID: 27185280 PMCID: PMC4869121 DOI: 10.1038/srep26096] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 04/27/2016] [Indexed: 11/08/2022] Open
Abstract
The importance of self-feedback autaptic transmission in modulating spike-time irregularity is still poorly understood. By using a biophysical model that incorporates autaptic coupling, we here show that self-innervation of neurons participates in the modulation of irregular neuronal firing, primarily by regulating the occurrence frequency of burst firing. In particular, we find that both excitatory and electrical autapses increase the occurrence of burst firing, thus reducing neuronal firing regularity. In contrast, inhibitory autapses suppress burst firing and therefore tend to improve the regularity of neuronal firing. Importantly, we show that these findings are independent of the firing properties of individual neurons, and as such can be observed for neurons operating in different modes. Our results provide an insightful mechanistic understanding of how different types of autapses shape irregular firing at the single-neuron level, and they highlight the functional importance of autaptic self-innervation in taming and modulating neurodynamics.
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23
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Blaes S, Burwick T. Attentional Bias Through Oscillatory Coherence Between Excitatory Activity and Inhibitory Minima. Neural Comput 2015; 27:1405-37. [PMID: 25973545 DOI: 10.1162/neco_a_00742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
An implementation of attentional bias is presented for a network model that couples excitatory and inhibitory oscillatory units in a manner that is inspired by the mechanisms that generate cortical gamma oscillations. Attentional biases are implemented as oscillatory coherences between excitatory units that encode the spatial location or features of the target and the pool of inhibitory units. This form of attentional bias is motivated by neurophysiological findings that relate selective attention to spike field coherence. Including also pattern recognition mechanisms, we demonstrate how this implementation of attentional bias leads to selection of an attentional target while suppressing distracters for cases of spatial and feature-based attention. With respect to neurophysiological observations, we argue that the recently found positive correlation between high firing rates and strong gamma locking with attention (Vinck, Womelsdorf, Buffalo, Desimone, & Fries, 2013) may point to an essential mechanism of the brain's attentional selection and suppression processes.
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Affiliation(s)
- Sebastian Blaes
- Frankfurt Institute for Advanced Studies, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Thomas Burwick
- Frankfurt Institute for Advanced Studies, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
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24
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Bylinskii Z, DeGennaro EM, Rajalingham R, Ruda H, Zhang J, Tsotsos JK. Towards the quantitative evaluation of visual attention models. Vision Res 2015; 116:258-68. [PMID: 25951756 DOI: 10.1016/j.visres.2015.04.007] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 03/15/2015] [Accepted: 04/02/2015] [Indexed: 11/17/2022]
Abstract
Scores of visual attention models have been developed over the past several decades of research. Differences in implementation, assumptions, and evaluations have made comparison of these models very difficult. Taxonomies have been constructed in an attempt at the organization and classification of models, but are not sufficient at quantifying which classes of models are most capable of explaining available data. At the same time, a multitude of physiological and behavioral findings have been published, measuring various aspects of human and non-human primate visual attention. All of these elements highlight the need to integrate the computational models with the data by (1) operationalizing the definitions of visual attention tasks and (2) designing benchmark datasets to measure success on specific tasks, under these definitions. In this paper, we provide some examples of operationalizing and benchmarking different visual attention tasks, along with the relevant design considerations.
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Affiliation(s)
- Z Bylinskii
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02141, USA; Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge 02141, USA.
| | - E M DeGennaro
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge 02141, USA
| | - R Rajalingham
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02141, USA
| | - H Ruda
- Computational Vision Laboratory, Department of Communication Sciences and Disorders, Northeastern University, Boston 02115, USA
| | - J Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Visual Attention Lab, Brigham and Women's Hospital, Cambridge, MA 02139, USA
| | - J K Tsotsos
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge 02141, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02141, USA; Electrical Engineering and Computer Science, Centre for Vision Research, York University, Toronto M3J 1P3, Canada
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25
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Gregoriou GG, Paneri S, Sapountzis P. Oscillatory synchrony as a mechanism of attentional processing. Brain Res 2015; 1626:165-82. [PMID: 25712615 DOI: 10.1016/j.brainres.2015.02.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2014] [Revised: 01/24/2015] [Accepted: 02/01/2015] [Indexed: 01/11/2023]
Abstract
The question of how the brain selects which stimuli in our visual field will be given priority to enter into perception, to guide our actions and to form our memories has been a matter of intense research in studies of visual attention. Work in humans and animal models has revealed an extended network of areas involved in the control and maintenance of attention. For many years, imaging studies in humans constituted the main source of a systems level approach, while electrophysiological recordings in non-human primates provided insight into the cellular mechanisms of visual attention. Recent technological advances and the development of sophisticated analytical tools have allowed us to bridge the gap between the two approaches and assess how neuronal ensembles across a distributed network of areas interact in visual attention tasks. A growing body of evidence suggests that oscillatory synchrony plays a crucial role in the selective communication of neuronal populations that encode the attended stimuli. Here, we discuss data from theoretical and electrophysiological studies, with more emphasis on findings from humans and non-human primates that point to the relevance of oscillatory activity and synchrony for attentional processing and behavior. These findings suggest that oscillatory synchrony in specific frequencies reflects the biophysical properties of specific cell types and local circuits and allows the brain to dynamically switch between different spatio-temporal patterns of activity to achieve flexible integration and selective routing of information along selected neuronal populations according to behavioral demands. This article is part of a Special Issue entitled SI: Prediction and Attention.
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Affiliation(s)
- Georgia G Gregoriou
- University of Crete, Faculty of Medicine, 71003 Heraklion, Crete, Greece; Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Crete, Greece.
| | - Sofia Paneri
- University of Crete, Faculty of Medicine, 71003 Heraklion, Crete, Greece; Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Crete, Greece.
| | - Panagiotis Sapountzis
- Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Crete, Greece.
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Peters SU, Gordon RL, Key AP. Induced gamma oscillations differentiate familiar and novel voices in children with MECP2 duplication and Rett syndromes. J Child Neurol 2015; 30:145-52. [PMID: 24776956 PMCID: PMC4406405 DOI: 10.1177/0883073814530503] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Normal levels of the methyl CpG-binding protein 2 (MeCP2) are critical to neurologic functioning, and slight alterations result in intellectual disability and autistic features. It was hypothesized that children with MECP2 duplication (overexpression of MeCP2) and Rett syndrome (underexpression of MeCP2) would exhibit distinct electroencephalographic (EEG) indices of auditory stimulus discrimination. In this study, gamma-band oscillatory responses to familiar and novel voices were examined and related to social functioning in 17 children (3-11 years old) with MECP2 duplication (n = 12) and Rett syndrome (n = 5). Relative to the stranger's voice, gamma activity in response to the mother's voice was increased in MECP2 duplication but decreased in Rett syndrome. In MECP2 duplication, greater mother versus stranger differences in gamma activity were associated with higher social functioning. For the first time, brain responses in a passive voice discrimination paradigm show that overexpression and underexpression of MeCP2 have differential effects on cortical information processing.
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Affiliation(s)
- Sarika U Peters
- Department of Pediatrics, Vanderbilt University, Nashville, TN, USA Vanderbilt Kennedy Center for Research on Human Development, Nashville, TN, USA
| | - Reyna L Gordon
- Vanderbilt Kennedy Center for Research on Human Development, Nashville, TN, USA
| | - Alexandra P Key
- Vanderbilt Kennedy Center for Research on Human Development, Nashville, TN, USA Department of Speech and Hearing Sciences, Vanderbilt University, Nashville, TN, USA
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Structured synaptic inhibition has a critical role in multiple-choice motion-discrimination tasks. J Neurosci 2015; 34:13444-57. [PMID: 25274822 DOI: 10.1523/jneurosci.0001-14.2014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Neural network models have been constructed to explore the underlying neural mechanisms for decision-making in multiple-choice motion-discrimination tasks. Despite great progress made, several key experimental observations have not been interpreted. In contrast to homogeneous connectivity between pyramidal cells and interneurons in previous models, here their connectivity is totally structured in a continuous recurrent network model. Specifically, we assume two types of inhibitory connectivity: opposite-feature and similar-feature inhibition, representing that the connectivity strength has a maximum between neural pairs with opposite and identical preferred directions, respectively. With a common parameter set, the model accounted for a wide variety of physiological and behavioral data from monkey experiments, including those that previous models failed to reproduce. We found that the opposite-feature inhibition endows the decision-making circuit with an elimination strategy, which effectively reduces the number of choice alternatives for inspection to speed up the decision process at the cost of decision accuracy. Conversely, the similar-feature inhibition markedly enhances the ability of the network to make a choice among multiple options and improves the accuracy of decisions, while slowing down the decision process. A simplified mean-field model was also presented to analytically characterize the effect of structured inhibition on fine discrimination. We made a testable prediction: only the combination of cross-feature and similar-feature inhibition enables the circuit to make a categorical choice among 12 alternatives. Together, the current work highlights the importance of structured synaptic inhibition in multiple-choice decision-making processes and sheds light on the neural mechanisms for visual motion perception.
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Silvanto J. Why is "blindsight" blind? A new perspective on primary visual cortex, recurrent activity and visual awareness. Conscious Cogn 2014; 32:15-32. [PMID: 25263935 DOI: 10.1016/j.concog.2014.08.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 07/30/2014] [Accepted: 08/04/2014] [Indexed: 01/19/2023]
Abstract
The neuropsychological phenomenon of blindsight has been taken to suggest that the primary visual cortex (V1) plays a unique role in visual awareness, and that extrastriate activation needs to be fed back to V1 in order for the content of that activation to be consciously perceived. The aim of this review is to evaluate this theoretical framework and to revisit its key tenets. Firstly, is blindsight truly a dissociation of awareness and visual detection? Secondly, is there sufficient evidence to rule out the possibility that the loss of awareness resulting from a V1 lesion simply reflects reduced extrastriate responsiveness, rather than a unique role of V1 in conscious experience? Evaluation of these arguments and the empirical evidence leads to the conclusion that the loss of phenomenal awareness in blindsight may not be due to feedback activity in V1 being the hallmark awareness. On the basis of existing literature, an alternative explanation of blindsight is proposed. In this view, visual awareness is a "global" cognitive function as its hallmark is the availability of information to a large number of perceptual and cognitive systems; this requires inter-areal long-range synchronous oscillatory activity. For these oscillations to arise, a specific temporal profile of neuronal activity is required, which is established through recurrent feedback activity involving V1 and the extrastriate cortex. When V1 is lesioned, the loss of recurrent activity prevents inter-areal networks on the basis of oscillatory activity. However, as limited amount of input can reach extrastriate cortex and some extrastriate neuronal selectivity is preserved, computations involving comparison of neural firing rates within a cortical area remain possible. This enables "local" read-out from specific brain regions, allowing for the detection and discrimination of basic visual attributes. Thus blindsight is blind due to lack of "global" long-range synchrony, and it functions via "local" neural readout from extrastriate areas.
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Affiliation(s)
- Juha Silvanto
- University of Westminster, Faculty of Science and Technology, Department of Psychology, 309 Regent Street, W1B 2HW London, UK; Brain Research Unit, O.V. Lounasmaa Laboratory, School of Science, Aalto University, PO BOX 15100, 00076 Aalto, Finland.
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29
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Engelhard B, Vaadia E. Spatial computation with gamma oscillations. Front Syst Neurosci 2014; 8:165. [PMID: 25249950 PMCID: PMC4158807 DOI: 10.3389/fnsys.2014.00165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Accepted: 08/25/2014] [Indexed: 11/16/2022] Open
Abstract
Gamma oscillations in cortex have been extensively studied with relation to behavior in both humans and animal models; however, their computational role in the processing of behaviorally relevant signals is still not clear. One oft-overlooked characteristic of gamma oscillations is their spatial distribution over the cortical space and the computational consequences of such an organization. Here, we advance the proposal that the spatial organization of gamma oscillations is of major importance for their function. The interaction of specific spatial distributions of oscillations with the functional topography of cortex enables select amplification of neuronal signals, which supports perceptual and cognitive processing.
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Affiliation(s)
- Ben Engelhard
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Hebrew University Hadassah Medical School Jerusalem, Israel ; Edmond and Lily Safra Center for Brain Sciences, The Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem Jerusalem, Israel
| | - Eilon Vaadia
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Hebrew University Hadassah Medical School Jerusalem, Israel ; Edmond and Lily Safra Center for Brain Sciences, The Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem Jerusalem, Israel
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30
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Abstract
Neuroscience research spans multiple spatiotemporal scales, from subsecond dynamics of individual neurons to the slow coordination of billions of neurons during resting state and sleep. Here it is shown that a single functional principle-temporal fluctuations in oscillation peak frequency ("frequency sliding")-can be used as a common analysis approach to bridge multiple scales within neuroscience. Frequency sliding is demonstrated in simulated neural networks and in human EEG data during a visual task. Simulations of biophysically detailed neuron models show that frequency sliding modulates spike threshold and timing variability, as well as coincidence detection. Finally, human resting-state EEG data demonstrate that frequency sliding occurs endogenously and can be used to identify large-scale networks. Frequency sliding appears to be a general principle that regulates brain function on multiple spatial and temporal scales, from modulating spike timing in individual neurons to coordinating large-scale brain networks during cognition and resting state.
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31
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Gonzalez-Burgos G, Miyamae T, Pafundo DE, Yoshino H, Rotaru DC, Hoftman G, Datta D, Zhang Y, Hammond M, Sampson AR, Fish KN, Ermentrout GB, Lewis DA. Functional Maturation of GABA Synapses During Postnatal Development of the Monkey Dorsolateral Prefrontal Cortex. Cereb Cortex 2014; 25:4076-93. [PMID: 24904071 DOI: 10.1093/cercor/bhu122] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Development of inhibition onto pyramidal cells may be crucial for the emergence of cortical network activity, including gamma oscillations. In primate dorsolateral prefrontal cortex (DLPFC), inhibitory synaptogenesis starts in utero and inhibitory synapse density reaches adult levels before birth. However, in DLPFC, the expression levels of γ-aminobutyric acid (GABA) synapse-related gene products changes markedly during development until young adult age, suggesting a highly protracted maturation of GABA synapse function. Therefore, we examined the development of GABA synapses by recording GABAAR-mediated inhibitory postsynaptic currents (GABAAR-IPSCs) from pyramidal cells in the DLPFC of neonatal, prepubertal, peripubertal, and adult macaque monkeys. We found that the decay of GABAAR-IPSCs, possibly including those from parvalbumin-positive GABA neurons, shortened by prepubertal age, while their amplitude increased until the peripubertal period. Interestingly, both GABAAR-mediated quantal response size, estimated by miniature GABAAR-IPSCs, and the density of GABAAR synaptic appositions, measured with immunofluorescence microscopy, were stable with age. Simulations in a computational model network with constant GABA synapse density showed that the developmental changes in GABAAR-IPSC properties had a significant impact on oscillatory activity and predicted that, whereas DLPFC circuits can generate gamma frequency oscillations by prepubertal age, mature levels of gamma band power are attained at late stages of development.
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Affiliation(s)
- Guillermo Gonzalez-Burgos
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Takeaki Miyamae
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Diego E Pafundo
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA Current address: Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, USA
| | - Hiroki Yoshino
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA Current address: Department of Psychiatry, Nara Medical University, Japan
| | - Diana C Rotaru
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA Current address: Department of Integrative Neurophysiology, Vrije Universiteit, Amsterdam, Netherlands
| | - Gil Hoftman
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Dibyadeep Datta
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Yun Zhang
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mahjub Hammond
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Allan R Sampson
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kenneth N Fish
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - G Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
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32
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Wagatsuma N, Potjans TC, Diesmann M, Sakai K, Fukai T. Spatial and feature-based attention in a layered cortical microcircuit model. PLoS One 2013; 8:e80788. [PMID: 24324628 PMCID: PMC3855641 DOI: 10.1371/journal.pone.0080788] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Accepted: 10/07/2013] [Indexed: 11/18/2022] Open
Abstract
Directing attention to the spatial location or the distinguishing feature of a visual object modulates neuronal responses in the visual cortex and the stimulus discriminability of subjects. However, the spatial and feature-based modes of attention differently influence visual processing by changing the tuning properties of neurons. Intriguingly, neurons' tuning curves are modulated similarly across different visual areas under both these modes of attention. Here, we explored the mechanism underlying the effects of these two modes of visual attention on the orientation selectivity of visual cortical neurons. To do this, we developed a layered microcircuit model. This model describes multiple orientation-specific microcircuits sharing their receptive fields and consisting of layers 2/3, 4, 5, and 6. These microcircuits represent a functional grouping of cortical neurons and mutually interact via lateral inhibition and excitatory connections between groups with similar selectivity. The individual microcircuits receive bottom-up visual stimuli and top-down attention in different layers. A crucial assumption of the model is that feature-based attention activates orientation-specific microcircuits for the relevant feature selectively, whereas spatial attention activates all microcircuits homogeneously, irrespective of their orientation selectivity. Consequently, our model simultaneously accounts for the multiplicative scaling of neuronal responses in spatial attention and the additive modulations of orientation tuning curves in feature-based attention, which have been observed widely in various visual cortical areas. Simulations of the model predict contrasting differences between excitatory and inhibitory neurons in the two modes of attentional modulations. Furthermore, the model replicates the modulation of the psychophysical discriminability of visual stimuli in the presence of external noise. Our layered model with a biologically suggested laminar structure describes the basic circuit mechanism underlying the attention-mode specific modulations of neuronal responses and visual perception.
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Affiliation(s)
- Nobuhiko Wagatsuma
- Zanvyl Krieger Mind/Brain Institute, and Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland, United States of America
- Brain Science Institute, RIKEN, Wako, Saitama, Japan
- * E-mail:
| | - Tobias C. Potjans
- Institute of Neuroscience and Medicine, Computational and Systems Neuroscience (INM-6), Research Center Juelich, Juelich, Germany
- Brain and Neural Systems Team, RIKEN Computational Science Research Program, Wako, Saitama, Japan
- Faculty of Biology III, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Markus Diesmann
- Brain Science Institute, RIKEN, Wako, Saitama, Japan
- Brain and Neural Systems Team, RIKEN Computational Science Research Program, Wako, Saitama, Japan
| | - Ko Sakai
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Tomoki Fukai
- Brain Science Institute, RIKEN, Wako, Saitama, Japan
- Brain and Neural Systems Team, RIKEN Computational Science Research Program, Wako, Saitama, Japan
- CREST, JST, Kawaguchi, Saitama, Japan
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33
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Aton SJ. Set and setting: how behavioral state regulates sensory function and plasticity. Neurobiol Learn Mem 2013; 106:1-10. [PMID: 23792020 PMCID: PMC4021401 DOI: 10.1016/j.nlm.2013.06.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Revised: 05/31/2013] [Accepted: 06/10/2013] [Indexed: 10/26/2022]
Abstract
Recently developed neuroimaging and electrophysiological techniques are allowing us to answer fundamental questions about how behavioral states regulate our perception of the external environment. Studies using these techniques have yielded surprising insights into how sensory processing is affected at the earliest stages by attention and motivation, and how new sensory information received during wakefulness (e.g., during learning) continues to affect sensory brain circuits (leading to plastic changes) during subsequent sleep. This review aims to describe how brain states affect sensory response properties among neurons in primary and secondary sensory cortices, and how this relates to psychophysical detection thresholds and performance on sensory discrimination tasks. This is not intended to serve as a comprehensive overview of all brain states, or all sensory systems, but instead as an illustrative description of how three specific state variables (attention, motivation, and vigilance [i.e., sleep vs. wakefulness]) affect sensory systems in which they have been best studied.
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Affiliation(s)
- Sara J Aton
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, USA.
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34
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Dipoppa M, Gutkin BS. Correlations in background activity control persistent state stability and allow execution of working memory tasks. Front Comput Neurosci 2013; 7:139. [PMID: 24155714 PMCID: PMC3801087 DOI: 10.3389/fncom.2013.00139] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 09/25/2013] [Indexed: 11/17/2022] Open
Abstract
Working memory (WM) requires selective information gating, active information maintenance, and rapid active updating. Hence performing a WM task needs rapid and controlled transitions between neural persistent activity and the resting state. We propose that changes in correlations in neural activity provides a mechanism for the required WM operations. As a proof of principle, we implement sustained activity and WM in recurrently coupled spiking networks with neurons receiving excitatory random background activity where background correlations are induced by a common noise source. We first characterize how the level of background correlations controls the stability of the persistent state. With sufficiently high correlations, the sustained state becomes practically unstable, so it cannot be initiated by a transient stimulus. We exploit this in WM models implementing the delay match to sample task by modulating flexibly in time the correlation level at different phases of the task. The modulation sets the network in different working regimes: more prompt to gate in a signal or clear the memory. We examine how the correlations affect the ability of the network to perform the task when distractors are present. We show that in a winner-take-all version of the model, where two populations cross-inhibit, correlations make the distractor blocking robust. In a version of the mode where no cross inhibition is present, we show that appropriate modulation of correlation levels is sufficient to also block the distractor access while leaving the relevant memory trace in tact. The findings presented in this manuscript can form the basis for a new paradigm about how correlations are flexibly controlled by the cortical circuits to execute WM operations.
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Affiliation(s)
- Mario Dipoppa
- Departement d'Etudes Cognitives, Ecole Normale Superieure, Group for Neural Theory, Laboratoire des Neurosciences Cognitives INSERM U960 Paris, France ; Ecole Doctorale Cerveau Cognition Comportement, Université Pierre et Marie Curie Paris, France
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35
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Cohen MX, Gulbinaite R. Five methodological challenges in cognitive electrophysiology. Neuroimage 2013; 85 Pt 2:702-10. [PMID: 23954489 DOI: 10.1016/j.neuroimage.2013.08.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2013] [Revised: 08/03/2013] [Accepted: 08/06/2013] [Indexed: 11/19/2022] Open
Abstract
Here we discuss five methodological challenges facing the current cognitive electrophysiology literature that address the roles of brain oscillations in cognition. The challenges focus on (1) unambiguous and consistent terminology, (2) neurophysiologically meaningful interpretations of results, (3) evaluation and comparison of different spatial filters often used in M/EEG research, (4) the role of multiscale interactions in brain and cognitive function, and (5) development of biophysically plausible cognitive models. We also suggest research directions that will help address these challenges. We hope that this paper will help foster discussions and debates about important themes in the study of how the brain's rhythmic patterns of spatiotemporal electrophysiological activity support cognition.
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Affiliation(s)
- Michael X Cohen
- Department of Psychology, University of Amsterdam, The Netherlands.
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36
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Cano-Colino M, Almeida R, Gomez-Cabrero D, Artigas F, Compte A. Serotonin regulates performance nonmonotonically in a spatial working memory network. ACTA ACUST UNITED AC 2013; 24:2449-63. [PMID: 23629582 DOI: 10.1093/cercor/bht096] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The prefrontal cortex (PFC) contains a dense network of serotonergic [serotonin, 5-hydroxytryptamine (5-HT)] axons, and endogenous 5-HT markedly modulates PFC neuronal function via several postsynaptic receptors. The therapeutic action of atypical antipsychotic drugs, acting mainly via 5-HT receptors, also suggests a role for serotonergic neurotransmission in cognitive functions. However, psychopharmacological studies have failed to find a consistent relationship between serotonergic transmission and cognitive functions of the PFC, including spatial working memory (SWM). Here, we built a computational network model to investigate 5-HT modulation of SWM in the PFC. We found that 5-HT modulates network's SWM performance nonmonotonically via 5-HT1A and 5-HT2A receptors, following an inverted U-shape. This relationship may contribute to blur the effects of serotonergic agents in previous SWM group-based behavioral studies. Our simulations also showed that errors occurring at low and high 5-HT concentrations are due to different network dynamics instabilities, suggesting that these 2 conditions can be distinguished experimentally based on their distinct dependency on experimental variables. We inferred specific predictions regarding the expected behavioral effects of serotonergic agents in 2 classic working-memory tasks. Our results underscore the relevance of identifying different error types in SWM tasks in order to reveal the association between neuromodulatory systems and SWM.
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Affiliation(s)
- Maria Cano-Colino
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Rita Almeida
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, Department of Neuroscience
| | - David Gomez-Cabrero
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Francesc Artigas
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, Institut d'Investigacions Biomèdiques de Barcelona (IIBB)-CSIC, Barcelona, Spain and Centro de Investigación Biomédica en Salud Mental (CIBERSAM), Barcelona, Spain
| | - Albert Compte
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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Forbes CE, Cameron KA, Grafman J, Barbey A, Solomon J, Ritter W, Ruchkin DS. Identifying temporal and causal contributions of neural processes underlying the Implicit Association Test (IAT). Front Hum Neurosci 2012; 6:320. [PMID: 23226123 PMCID: PMC3510688 DOI: 10.3389/fnhum.2012.00320] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2012] [Accepted: 11/08/2012] [Indexed: 11/29/2022] Open
Abstract
The Implicit Association Test (IAT) is a popular behavioral measure that assesses the associative strength between outgroup members and stereotypical and counterstereotypical traits. Less is known, however, about the degree to which the IAT reflects automatic processing. Two studies examined automatic processing contributions to a gender-IAT using a data driven, social neuroscience approach. Performance on congruent (e.g., categorizing male names with synonyms of strength) and incongruent (e.g., categorizing female names with synonyms of strength) IAT blocks were separately analyzed using EEG (event-related potentials, or ERPs, and coherence; Study 1) and lesion (Study 2) methodologies. Compared to incongruent blocks, performance on congruent IAT blocks was associated with more positive ERPs that manifested in frontal and occipital regions at automatic processing speeds, occipital regions at more controlled processing speeds and was compromised by volume loss in the anterior temporal lobe (ATL), insula and medial PFC. Performance on incongruent blocks was associated with volume loss in supplementary motor areas, cingulate gyrus and a region in medial PFC similar to that found for congruent blocks. Greater coherence was found between frontal and occipital regions to the extent individuals exhibited more bias. This suggests there are separable neural contributions to congruent and incongruent blocks of the IAT but there is also a surprising amount of overlap. Given the temporal and regional neural distinctions, these results provide converging evidence that stereotypic associative strength assessed by the IAT indexes automatic processing to a degree.
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Affiliation(s)
- Chad E Forbes
- Social Neuroscience Laboratory, Department of Psychology, University of Delaware Newark, DE, USA
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38
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Cognit activation: a mechanism enabling temporal integration in working memory. Trends Cogn Sci 2012; 16:207-18. [PMID: 22440831 DOI: 10.1016/j.tics.2012.03.005] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2011] [Revised: 03/06/2012] [Accepted: 03/06/2012] [Indexed: 11/22/2022]
Abstract
Working memory is critical to the integration of information across time in goal-directed behavior, reasoning and language, yet its neural substrate is unknown. Based on recent research, we propose a mechanism by which the brain can retain working memory for prospective use, thereby bridging time in the perception/action cycle. The essence of the mechanism is the activation of 'cognits', which consist of distributed, overlapping and interactive cortical networks that in the aggregate encode the long-term memory of the subject. Working memory depends on the excitatory reentry between perceptual and executive cognits of posterior and frontal cortices, respectively. Given the pervasive role of working memory in the structuring of purposeful cognitive sequences, its mechanism looms essential to the foundation of behavior, reasoning and language.
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39
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Abstract
Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Here we review the cellular and synaptic mechanisms underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibition. Second, gamma oscillations are short-lived and typically emerge from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. Third, gamma rhythm typically concurs with irregular firing of single neurons, and the network frequency of gamma oscillations varies extensively depending on the underlying mechanism. To document gamma oscillations, efforts should be made to distinguish them from mere increases of gamma-band power and/or increased spiking activity. Fourth, the magnitude of gamma oscillation is modulated by slower rhythms. Such cross-frequency coupling may serve to couple active patches of cortical circuits. Because of their ubiquitous nature and strong correlation with the "operational modes" of local circuits, gamma oscillations continue to provide important clues about neuronal population dynamics in health and disease.
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Affiliation(s)
- György Buzsáki
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey 07102, USA.
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40
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Yilmaz O. Oscillatory synchronization model of attention to moving objects. Neural Netw 2012; 29-30:20-36. [PMID: 22369920 DOI: 10.1016/j.neunet.2012.01.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2010] [Revised: 12/26/2011] [Accepted: 01/27/2012] [Indexed: 10/14/2022]
Abstract
The world is a dynamic environment hence it is important for the visual system to be able to deploy attention on moving objects and attentively track them. Psychophysical experiments indicate that processes of both attentional enhancement and inhibition are spatially focused on the moving objects; however the mechanisms of these processes are unknown. The studies indicate that the attentional selection of target objects is sustained via a feedforward-feedback loop in the visual cortical hierarchy and only the target objects are represented in attention-related areas. We suggest that feedback from the attention-related areas to early visual areas modulates the activity of neurons; establishes synchronization with respect to a common oscillatory signal for target items via excitatory feedback, and also establishes de-synchronization for distractor items via inhibitory feedback. A two layer computational neural network model with integrate-and-fire neurons is proposed and simulated for simple attentive tracking tasks. Consistent with previous modeling studies, we show that via temporal tagging of neural activity, distractors can be attentively suppressed from propagating to higher levels. However, simulations also suggest attentional enhancement of activity for distractors in the first layer which represents neural substrate dedicated for low level feature processing. Inspired by this enhancement mechanism, we developed a feature based object tracking algorithm with surround processing. Surround processing improved tracking performance by 57% in PETS 2001 dataset, via eliminating target features that are likely to suffer from faulty correspondence assignments.
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Affiliation(s)
- Ozgur Yilmaz
- National Research Center for Magnetic Resonance (UMRAM), Bilkent Cyberpark Ankara, Turkey.
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Battaglia D, Hansel D. Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex. PLoS Comput Biol 2011; 7:e1002176. [PMID: 21998568 PMCID: PMC3188510 DOI: 10.1371/journal.pcbi.1002176] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2010] [Accepted: 07/15/2011] [Indexed: 12/02/2022] Open
Abstract
Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity. However, analysis of Local Field Potentials (LFPs) across different experiments reveals considerable diversity in the degree of oscillatory behavior of this induced activity. Contrast-dependent power enhancements can indeed occur over a broad band in the gamma frequency range and spectral peaks may not arise at all. Furthermore, even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. We show that the strength of the inter-layer coupling crucially affects this spatiotemporal structure. We predict that layer VI inactivation should induce global changes in the spectral properties of induced LFPs, reflecting their slower temporal decorrelation in the absence of inter-layer feedback. Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation. Visual stimulation elicits neuronal responses in visual cortex. When the contrast of the used stimuli increases, the power of this induced activity is boosted over a broad frequency range (30–100 Hz), called the “gamma band.” It would be tempting to hypothesize that this phenomenon is due to the emergence of oscillations in which many neurons fire collectively in a rhythmic way. However, previous models trying to explain contrast-related power enhancements using synchronous oscillations failed to reproduce the observed spectra because they originated unrealistically sharp spectral peaks. The aim of our study is to reconcile synchronous oscillations with broad-band power spectra. We argue here that, thanks to the interaction between neuronal populations at different depths in the cortical tissue, the induced oscillatory responses are synchronous, but, at the same time, chaotic. The chaotic nature of the dynamics makes it possible to have broad-band power spectra together with synchrony. Our modeling study allows us formulating qualitative experimental predictions that provide a potential test for our theory. We predict that if the interactions between cortical layers are suppressed, for instance by inactivating neurons in deep layers, the induced responses might become more regular and narrow isolated peaks might develop in their power spectra.
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Affiliation(s)
- Demian Battaglia
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
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Wagatsuma N, Potjans TC, Diesmann M, Fukai T. Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model. Front Comput Neurosci 2011; 5:31. [PMID: 21779240 PMCID: PMC3134838 DOI: 10.3389/fncom.2011.00031] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2011] [Accepted: 06/20/2011] [Indexed: 11/13/2022] Open
Abstract
A vast amount of information about the external world continuously flows into the brain, whereas its capacity to process such information is limited. Attention enables the brain to allocate its resources of information processing to selected sensory inputs for reducing its computational load, and effects of attention have been extensively studied in visual information processing. However, how the microcircuit of the visual cortex processes attentional information from higher areas remains largely unknown. Here, we explore the complex interactions between visual inputs and an attentional signal in a computational model of the visual cortical microcircuit. Our model not only successfully accounts for previous experimental observations of attentional effects on visual neuronal responses, but also predicts contrasting differences in the attentional effects of top-down signals between cortical layers: attention to a preferred stimulus of a column enhances neuronal responses of layers 2/3 and 5, the output stations of cortical microcircuits, whereas attention suppresses neuronal responses of layer 4, the input station of cortical microcircuits. We demonstrate that the specific modulation pattern of layer-4 activity, which emerges from inter-laminar synaptic connections, is crucial for a rapid shift of attention to a currently unattended stimulus. Our results suggest that top-down signals act differently on different layers of the cortical microcircuit.
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Benchenane K, Tiesinga PH, Battaglia FP. Oscillations in the prefrontal cortex: a gateway to memory and attention. Curr Opin Neurobiol 2011; 21:475-85. [DOI: 10.1016/j.conb.2011.01.004] [Citation(s) in RCA: 247] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2011] [Accepted: 01/18/2011] [Indexed: 11/16/2022]
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Gomez-Cabrero D, Compte A, Tegner J. Workflow for generating competing hypothesis from models with parameter uncertainty. Interface Focus 2011; 1:438-49. [PMID: 22670212 DOI: 10.1098/rsfs.2011.0015] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2011] [Accepted: 03/07/2011] [Indexed: 01/07/2023] Open
Abstract
Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters.
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Affiliation(s)
- David Gomez-Cabrero
- Department of Medicine, Karolinska Institutet , Unit of Computational Medicine, Centre for Molecular Medicine , Solna, Stockholm , Sweden
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Deco G, Buehlmann A, Masquelier T, Hugues E. The role of rhythmic neural synchronization in rest and task conditions. Front Hum Neurosci 2011; 5:4. [PMID: 21326617 PMCID: PMC3035810 DOI: 10.3389/fnhum.2011.00004] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2010] [Accepted: 11/07/2011] [Indexed: 11/23/2022] Open
Abstract
Rhythmic neural synchronization is found throughout the brain during many different tasks and even at rest. Beyond their underlying mechanisms, the question of their role is still controversial. Modeling can bring insight on this difficult question. We review here our recent modeling results concerning this issue in different situations. During rest, we show how local rhythmic synchrony can induce a spatiotemporally organized spontaneous activity at the brain level. Then, we show how rhythmic synchrony decreases reaction time in attention and enhances the strength and speed of information transfer between different groups of neurons. Finally, we show that when rhythmic synchrony creates firing phases, the learning with spike timing-dependent plasticity of repeatedly presented input patterns is greatly enhanced.
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Affiliation(s)
- Gustavo Deco
- Unit for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain
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46
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Tiesinga PH, Sejnowski TJ. Mechanisms for Phase Shifting in Cortical Networks and their Role in Communication through Coherence. Front Hum Neurosci 2010; 4:196. [PMID: 21103013 PMCID: PMC2987601 DOI: 10.3389/fnhum.2010.00196] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2010] [Accepted: 09/29/2010] [Indexed: 11/13/2022] Open
Abstract
In the primate visual cortex, the phase of spikes relative to oscillations in the local field potential (LFP) in the gamma frequency range (30-80 Hz) can be shifted by stimulus features such as orientation and thus the phase may carry information about stimulus identity. According to the principle of communication through coherence (CTC), the relative LFP phase between the LFPs in the sending and receiving circuits affects the effectiveness of the transmission. CTC predicts that phase shifting can be used for stimulus selection. We review and investigate phase shifting in models of periodically driven single neurons and compare it with phase shifting in models of cortical networks. In a single neuron, as the driving current is increased, the spike phase varies systematically while the firing rate remains constant. In a network model of reciprocally connected excitatory (E) and inhibitory (I) cells phase shifting occurs in response to both injection of constant depolarizing currents and to brief pulses to I cells. These simple models provide an account for phase-shifting observed experimentally and suggest a mechanism for implementing CTC. We discuss how this hypothesis can be tested experimentally using optogenetic techniques.
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Affiliation(s)
- Paul H. Tiesinga
- Donders Institute for Brain, Cognition and Behavior, Radboud University NijmegenNijmegen, Netherlands
- Physics and Astronomy Department, University of North CarolinaChapel Hill, NC, USA
| | - Terrence J. Sejnowski
- Howard Hughes Medical Institute, Salk Institute for Biological StudiesLa Jolla, CA, USA
- Division of Biological Studies, University of California at San DiegoLa Jolla, CA, USA
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Corlett PR, Taylor JR, Wang XJ, Fletcher PC, Krystal JH. Toward a neurobiology of delusions. Prog Neurobiol 2010; 92:345-69. [PMID: 20558235 PMCID: PMC3676875 DOI: 10.1016/j.pneurobio.2010.06.007] [Citation(s) in RCA: 246] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2010] [Revised: 05/06/2010] [Accepted: 06/08/2010] [Indexed: 12/21/2022]
Abstract
Delusions are the false and often incorrigible beliefs that can cause severe suffering in mental illness. We cannot yet explain them in terms of underlying neurobiological abnormalities. However, by drawing on recent advances in the biological, computational and psychological processes of reinforcement learning, memory, and perception it may be feasible to account for delusions in terms of cognition and brain function. The account focuses on a particular parameter, prediction error--the mismatch between expectation and experience--that provides a computational mechanism common to cortical hierarchies, fronto-striatal circuits and the amygdala as well as parietal cortices. We suggest that delusions result from aberrations in how brain circuits specify hierarchical predictions, and how they compute and respond to prediction errors. Defects in these fundamental brain mechanisms can vitiate perception, memory, bodily agency and social learning such that individuals with delusions experience an internal and external world that healthy individuals would find difficult to comprehend. The present model attempts to provide a framework through which we can build a mechanistic and translational understanding of these puzzling symptoms.
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Affiliation(s)
- P R Corlett
- Department of Psychiatry, Yale University School of Medicine, Connecticut Mental Health Centre, Abraham Ribicoff Research Facility, 34 Park Street, New Haven, CT 06519, USA.
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48
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Wang XJ. Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev 2010; 90:1195-268. [PMID: 20664082 DOI: 10.1152/physrev.00035.2008] [Citation(s) in RCA: 1154] [Impact Index Per Article: 82.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.
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Affiliation(s)
- Xiao-Jing Wang
- Department of Neurobiology and Kavli Institute of Neuroscience, Yale University School of Medicine, New Haven, Connecticut 06520, USA.
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