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      Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning

      , 1

      Hippocampus

      John Wiley and Sons Inc.

      memory, imagining, planning, epilepsy, learning

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          ABSTRACT

          Sharp wave ripples (SPW‐Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW‐Rs occur during “off‐line” states of the brain, associated with consummatory behaviors and non‐REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW‐induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW‐Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW‐Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW‐Rs interferes with memory. Recently acquired and pre‐existing information are combined during SPW‐R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW‐Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW‐Rs leads to their pathological conversion, “p‐ripples,” which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer's Disease. Mechanisms for SPW‐R genesis and function are discussed in this review. © 2015 The Authors Hippocampus Published by Wiley Periodicals, Inc.

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          Most cited references 823

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          A default mode of brain function.

          A baseline or control state is fundamental to the understanding of most complex systems. Defining a baseline state in the human brain, arguably our most complex system, poses a particular challenge. Many suspect that left unconstrained, its activity will vary unpredictably. Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF. The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered by flowing blood and is remarkably uniform in the awake but resting state (e.g., lying quietly with eyes closed). Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors. We used quantitative metabolic and circulatory measurements from positron-emission tomography to obtain the OEF regionally throughout the brain. Areas of activation were conspicuous by their absence. All significant deviations from the mean hemisphere OEF were increases, signifying deactivations, and resided almost exclusively in the visual system. Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this baseline, during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI. These decreases suggest the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.
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            Neural networks and physical systems with emergent collective computational abilities.

             John Hopfield (1982)
            Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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              A synaptic model of memory: long-term potentiation in the hippocampus.

              Long-term potentiation of synaptic transmission in the hippocampus is the primary experimental model for investigating the synaptic basis of learning and memory in vertebrates. The best understood form of long-term potentiation is induced by the activation of the N-methyl-D-aspartate receptor complex. This subtype of glutamate receptor endows long-term potentiation with Hebbian characteristics, and allows electrical events at the postsynaptic membrane to be transduced into chemical signals which, in turn, are thought to activate both pre- and postsynaptic mechanisms to generate a persistent increase in synaptic strength.
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                Author and article information

                Journal
                Hippocampus
                Hippocampus
                10.1002/(ISSN)1098-1063
                HIPO
                Hippocampus
                John Wiley and Sons Inc. (Hoboken )
                1050-9631
                1098-1063
                26 September 2015
                October 2015
                : 25
                : 10 , Hippocampal Sharp Waves ( doiID: 10.1002/hipo.v25.10 )
                : 1073-1188
                Affiliations
                [ 1 ]The Neuroscience Institute, School of Medicine and Center for Neural Science, New York University New York New York
                Author notes
                [* ]Correspondence to: György Buzsáki, The Neuroscience Institute, New York University, School of Medicine East Rivers Science Park, 450 East 29th Street, 9th Floor New York, NY 10016, USA. E‐mail: gyorgy.Buzsáki@nyumc.org
                Article
                HIPO22488
                10.1002/hipo.22488
                4648295
                26135716
                © 2015 The Authors Hippocampus Published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                Page count
                Pages: 116
                Product
                Funding
                Funded by: National Institutes of Health
                Award ID: NS075015
                Award ID: MH54671
                Award ID: MH107396
                Award ID: 5U01NS090583
                Funded by: NSF
                Award ID: SBE 0542013
                Funded by: Human Frontiers Science Program and the G. Harold and Leila Y. Mathers Foundation
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                hipo22488
                October 2015
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.4 mode:remove_FC converted:12.09.2016

                Neurology

                learning, epilepsy, planning, imagining, memory

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