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Title
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A neuromimetic realization of hippocampal CA1 for theta wave generation
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Author
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Abstract
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| Recent advances in neural engineering allowed the development of neuroprostheses which facilitate functionality in people with neurological problems. In this research, a real-time neuromorphic system is proposed to artificially reproduce the theta wave and firing patterns of different neuronal populations in the CA1, a sub-region of the hippocampus. The hippocampal theta oscillations (4–12 Hz) are an important electrophysiological rhythm that contributes in various cognitive functions, including navigation, memory, and novelty detection. The proposed CA1 neuromimetic circuit includes 100 linearized Pinsky-Rinzel neurons and 668 excitatory and inhibitory synapses on a field programmable gate array (FPGA). The implemented spiking neural network of the CA1 includes the main neuronal populations for the theta rhythm generation: excitatory pyramidal cells, PV+ basket cells, and Oriens Lacunosum–Moleculare (OLM) cells which are inhibitory interneurons. Moreover, the main inputs to the CA1 region from the entorhinal cortex via the perforant pathway, the CA3 via Schaffer collaterals, and the medial septum via fimbria–fornix are also implemented on the FPGA using a bursting leaky-integrate and fire (LIF) neuron model. The results of hardware realization show that the proposed CA1 neuromimetic circuit successfully reconstructs the theta oscillations and functionally illustrates the phase relations between firing responses of the different neuronal populations. It is also evaluated the impact of medial septum elimination on the firing patterns of the CA1 neuronal population and the theta wave’s characteristics. This neuromorphic system can be considered as a potential platform that opens opportunities for neuroprosthetic applications in future works. |
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Language
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English
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Source (journal)
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Neural networks. - New York, N.Y.
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Publication
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New York, N.Y.
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2021
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ISSN
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0893-6080
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DOI
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10.1016/J.NEUNET.2021.07.002
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Volume/pages
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142
(2021)
, p. 548-563
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ISI
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000691628800012
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Pubmed ID
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34340189
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Full text (Publisher's DOI)
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Full text (open access)
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