17 research outputs found

    The dendritic engram

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    Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linear ways, provide the substrate for local protein synthesis and facilitate the orchestration of signaling pathways that regulate local synaptic plasticity. These capabilities allow them to act as a second layer of computation within the neuron and serve as the fundamental unit of plasticity. As such, dendrites are integral parts of the memory engram, namely the physical representation of memories in the brain and are increasingly studied during learning tasks. Here, we review experimental and computational studies that support a novel, dendritic view of the memory engram that is centered on non-linear dendritic branches as elementary memory units. We highlight the potential implications of dendritic engrams for the learning and memory field and discuss future research directions

    Unraveling the role of active dendrites of Dentate Gyrus Granule cells using a biologically relevant computational model

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    Hippocampus is known to be involved in numerous memory processes, including episodic and spatial memory. The hippocampal Dentate Gyrus (DG) is believed to be responsible for distinguishing between similar mnemonic events, a computation known as pattern separation. Granule cells, the principal neurons of the DG, are thought to perform pattern separation by minimizing the overlap between similar patterns. Granule cells are sparsely connected, fire scarcely due to strong attenuation of propagating synaptic currents, and integrate strong inhibitory signals, generating a "winner-takes-all" mechanism. Moreover, recent findings suggest that the dendrites of GCs are able to support local regenerative events, the so-called dendritic spikes, whose potential functional role remains unclear. In this thesis, we create a biologically constrained, computational model of the Dentate Gyrus network that consists of GCs and various interneurons (Basket cells, HIPP cells, CCK cells) simulated as integrate-and-fire models. We used the model to assess the relative importance of the various characteristics of GC, focusing primarily on how active dendrites may influence pattern separation. Preliminary results predict that active dendrites facilitate pattern separation via decreasing the activity of Granule cells and thus increasing network sparsity. Further investigation is necessary to explain this unexpected finding, via a detailed biophysical model and/or in vitro dendro-somatic recordings

    Comparison of the Accuracy of Two Transfer Caps in Positional Transmission of Palatal Temporary Anchorage Devices: An In Vitro Study

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    The aim of this study was to compare the positional information transfer accuracy of palatal temporary anchorage devices (TADs) of two different brands of transfer caps: PSM and Leone. Thirty plaster casts of maxillary dental arches were chosen for master models. A couple of Leone TADs were inserted in each master model. For each master model, two analysis models were created: using two transfer caps, Leone and PSM, the impressions were taken, the analogues were connected on the transfer caps, and the casts were poured. Using digital methods and equipment, such as a 3D scanner, a 3D analysis and a comparison of the accuracy of the two transfer caps in transferring the positional information of the TADs was then made. The data obtained were analyzed using the Mann–Whitney U-test at a significance level of α = 0.05. PSM transfer caps showed higher error frequency in almost all measurements. Only two measurements had a larger error in the analysis models made with Leone transfer caps. The Mann–Whitney U-test found a significant difference between the error levels of TADs found in the analysis models created with PSM transfer caps. Leone transfer caps showed greater reliability in TADs positional information transmission
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