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      Imbalanced pattern completion vs. separation in cognitive disease: network simulations of synaptic pathologies predict a personalized therapeutics strategy

      research-article
      1 , 2 , , 1
      BMC Neuroscience
      BioMed Central

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          Abstract

          Background

          Diverse Mouse genetic models of neurodevelopmental, neuropsychiatric, and neurodegenerative causes of impaired cognition exhibit at least four convergent points of synaptic malfunction: 1) Strength of long-term potentiation (LTP), 2) Strength of long-term depression (LTD), 3) Relative inhibition levels (Inhibition), and 4) Excitatory connectivity levels (Connectivity).

          Results

          To test the hypothesis that pathological increases or decreases in these synaptic properties could underlie imbalances at the level of basic neural network function, we explored each type of malfunction in a simulation of autoassociative memory. These network simulations revealed that one impact of impairments or excesses in each of these synaptic properties is to shift the trade-off between pattern separation and pattern completion performance during memory storage and recall. Each type of synaptic pathology either pushed the network balance towards intolerable error in pattern separation or intolerable error in pattern completion. Imbalances caused by pathological impairments or excesses in LTP, LTD, inhibition, or connectivity, could all be exacerbated, or rescued, by the simultaneous modulation of any of the other three synaptic properties.

          Conclusions

          Because appropriate modulation of any of the synaptic properties could help re-balance network function, regardless of the origins of the imbalance, we propose a new strategy of personalized cognitive therapeutics guided by assay of pattern completion vs. pattern separation function. Simulated examples and testable predictions of this theorized approach to cognitive therapeutics are presented.

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          Most cited references85

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          Pattern separation in the human hippocampal CA3 and dentate gyrus.

          Pattern separation, the process of transforming similar representations or memories into highly dissimilar, nonoverlapping representations, is a key component of many functions ascribed to the hippocampus. Computational models have stressed the role of the hippocampus and, in particular, the dentate gyrus and its projections into the CA3 subregion in pattern separation. We used high-resolution (1.5-millimeter isotropic voxels) functional magnetic resonance imaging to measure brain activity during incidental memory encoding. Although activity consistent with a bias toward pattern completion was observed in CA1, the subiculum, and the entorhinal and parahippocampal cortices, activity consistent with a strong bias toward pattern separation was observed in, and limited to, the CA3/dentate gyrus. These results provide compelling evidence of a key role of the human CA3/dentate gyrus in pattern separation.
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            Computational analysis of the role of the hippocampus in memory.

            The authors draw together the results of a series of detailed computational studies and show how they are contributing to the development of a theory of hippocampal function. A new part of the theory introduced here is a quantitative analysis of how backprojections from the hippocampus to the neocortex could lead to the recall of recent memories. The theory is then compared with other theories of hippocampal function. First, what is computed by the hippocampus is considered. The hypothesis the authors advocate, on the basis of the effects of damage to the hippocampus and neuronal activity recorded in it, is that it is involved in the formation of new memories by acting as an intermediate-term buffer store for information about episodes, particularly for spatial, but probably also for some nonspatial, information. The authors analyze how the hippocampus could perform this function, by producing a computational theory of how it operates, based on neuroanatomical and neurophysiological information about the different neuronal systems contained within the hippocampus. Key hypotheses are that the CA3 pyramidal cells operate as a single autoassociation network to store new episodic information as it arrives via a number of specialized preprocessing stages from many association areas of the cerebral cortex, and that the dentate granule cell/mossy fiber system is important, particularly during learning, to help to produce a new pattern of firing in the CA3 cells for each episode. The computational analysis shows how many memories could be stored in the hippocampus and how quickly the CA3 autoassociation system would operate during recall. The analysis is then extended to show how the CA3 system could be used to recall a whole episodic memory when only a fragment of it is presented. It is shown how this recall could operate using modified synapses in backprojection pathways from the hippocampus to the cerebral neocortex, resulting in reinstatement of neuronal activity in association areas of the cerebral neocortex similar to that present during the original episode. The recalled information in the cerebral neocortex could then be used by the neocortex in the formation of long-term memories.
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              Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach.

              The authors present a computational neural-network model of how the hippocampus and medial temporal lobe cortex (MTLC) contribute to recognition memory. The hippocampal component contributes by recalling studied details. The MTLC component cannot support recall, but one can extract a scalar familiarity signal from MTLC that tracks how well a test item matches studied items. The authors present simulations that establish key differences in the operating characteristics of the hippocampal-recall and MTLC-familiarity signals and identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the 2 signals. They also use the model to address the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition. ((c) 2003 APA, all rights reserved)
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                Author and article information

                Journal
                BMC Neurosci
                BMC Neuroscience
                BioMed Central
                1471-2202
                2010
                13 August 2010
                : 11
                : 96
                Affiliations
                [1 ]Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
                [2 ]Genentech, Inc., South San Francisco, California 94080, USA
                Article
                1471-2202-11-96
                10.1186/1471-2202-11-96
                2931521
                20704756
                5de405b4-026e-4c0d-809e-3271d9cd7227
                Copyright ©2010 Hanson and Madison; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 January 2010
                : 13 August 2010
                Categories
                Research Article

                Neurosciences
                Neurosciences

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