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Fast signaling and focal connectivity of PV+ interneurons ensure efficient pattern separation by lateral inhibition in a full-scale dentate gyrus network model

View ORCID ProfileSegundo Jose Guzman, View ORCID ProfileAlois Schlögl, View ORCID ProfileClaudia Espinoza, Xiaomin Zhang, View ORCID ProfileBen Suter, View ORCID ProfilePeter Jonas
doi: https://doi.org/10.1101/647800
Segundo Jose Guzman
1IST Austria (Institute of Science and Technology Austria), Am Campus 1, A-3400 Klosterneuburg, Austria
2Institute for Molecular Biotechnology (IMBA), Dr. Bohr-Gasse 3, A-1030 Wien, Austria
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Alois Schlögl
1IST Austria (Institute of Science and Technology Austria), Am Campus 1, A-3400 Klosterneuburg, Austria
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Claudia Espinoza
1IST Austria (Institute of Science and Technology Austria), Am Campus 1, A-3400 Klosterneuburg, Austria
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Xiaomin Zhang
1IST Austria (Institute of Science and Technology Austria), Am Campus 1, A-3400 Klosterneuburg, Austria
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Ben Suter
1IST Austria (Institute of Science and Technology Austria), Am Campus 1, A-3400 Klosterneuburg, Austria
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Peter Jonas
1IST Austria (Institute of Science and Technology Austria), Am Campus 1, A-3400 Klosterneuburg, Austria
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  • For correspondence: peter.jonas@ist.ac.at
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ABSTRACT

Pattern separation is a fundamental brain computation that converts small differences in synaptic input patterns into large differences in action potential (AP) output patterns. Pattern separation plays a key role in the dentate gyrus, enabling the efficient storage and recall of memories in downstream hippocampal CA3 networks. Several mechanisms for pattern separation have been proposed, including expansion of coding space, sparsification of neuronal activity, and simple thresholding mechanisms. Alternatively, a winner-takes-all mechanism, in which the most excited cells inhibit all less-excited cells by lateral inhibition, might be involved. Although such a mechanism is computationally powerful, it remains unclear whether it operates in biological networks. Here, we develop a full-scale network model of the dentate gyrus, comprised of granule cells (GCs) and parvalbumin+ (PV+) inhibitory interneurons, based on experimentally determined biophysical cellular properties and synaptic connectivity rules. Our results demonstrate that a biologically realistic principal neuron–interneuron (PN–IN) network model is a highly efficient pattern separator. Mechanistic dissection in the model revealed that a winner-takes-all mechanism by lateral inhibition plays a crucial role in pattern separation. Furthermore, both fast signaling properties of PV+ interneurons and focal GC–interneuron connectivity are essential for efficient pattern separation. Thus, PV+ interneurons are not only involved in basic microcircuit functions, but also contribute to higher-order computations in neuronal networks, such as pattern separation.

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Posted May 25, 2019.
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Fast signaling and focal connectivity of PV+ interneurons ensure efficient pattern separation by lateral inhibition in a full-scale dentate gyrus network model
Segundo Jose Guzman, Alois Schlögl, Claudia Espinoza, Xiaomin Zhang, Ben Suter, Peter Jonas
bioRxiv 647800; doi: https://doi.org/10.1101/647800
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Fast signaling and focal connectivity of PV+ interneurons ensure efficient pattern separation by lateral inhibition in a full-scale dentate gyrus network model
Segundo Jose Guzman, Alois Schlögl, Claudia Espinoza, Xiaomin Zhang, Ben Suter, Peter Jonas
bioRxiv 647800; doi: https://doi.org/10.1101/647800

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