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      Hamiltonian energy in a modified Hindmarsh–Rose model

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          Abstract

          This paper investigates the Hamiltonian energy of a modified Hindmarsh–Rose (HR) model to observe its effect on short-term memory. A Hamiltonian energy function and its variable function are given in the reduced system with a single node according to Helmholtz’s theorem. We consider the role of the coupling strength and the links between neurons in the pattern formation to show that the coupling and cooperative neurons are necessary for generating the fire or a clear short-term memory when all the neurons are in sync. Then, we consider the effect of the degree and external stimulus from other neurons on the emergence and disappearance of short-term memory, which illustrates that generating short-term memory requires much energy, and the coupling strength could further reduce energy consumption. Finally, the dynamical mechanisms of the generation of short-term memory are concluded.

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          Most cited references41

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          An energy budget for signaling in the grey matter of the brain.

          Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of glutamate are predicted to consume much of the energy (47% and 34%, respectively), with the resting potential consuming a smaller amount (13%), and glutamate recycling using only 3%. Energy usage depends strongly on action potential rate--an increase in activity of 1 action potential/cortical neuron/s will raise oxygen consumption by 145 mL/100 g grey matter/h. The energy expended on signaling is a large fraction of the total energy used by the brain; this favors the use of energy efficient neural codes and wiring patterns. Our estimates of energy usage predict the use of distributed codes, with
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            Synaptic reverberation underlying mnemonic persistent activity.

            Stimulus-specific persistent neural activity is the neural process underlying active (working) memory. Since its discovery 30 years ago, mnemonic activity has been hypothesized to be sustained by synaptic reverberation in a recurrent circuit. Recently, experimental and modeling work has begun to test the reverberation hypothesis at the cellular level. Moreover, theory has been developed to describe memory storage of an analog stimulus (such as spatial location or eye position), in terms of continuous 'bump attractors' and 'line attractors'. This review summarizes new studies, and discusses insights and predictions from biophysically based models. The stability of a working memory network is recognized as a serious problem; stability can be achieved if reverberation is largely mediated by NMDA receptors at recurrent synapses.
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              ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework

              Highlights • WM is thought to depend on persistent maintenance of stationary activity states. • However, population-level analyses reveal that brain activity is highly dynamic. • Accumulating evidence implicates activity-silent neural states for WM. • Dynamic coding suggests that WM is encoded in patterns of functional connectivity.
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                Author and article information

                Contributors
                Journal
                Front Netw Physiol
                Front Netw Physiol
                Front. Netw. Physiol.
                Frontiers in Network Physiology
                Frontiers Media S.A.
                2674-0109
                26 March 2024
                2024
                : 4
                : 1362778
                Affiliations
                [1] 1 School of Science , Xuchang University , Xuchang, Henan, China
                [2] 2 School of Mathematics and Statistics , Northwestern Polytechnical University , Xi’an, Shaanxi, China
                [3] 3 School of Mathematics and Statistics , North China University of Water Resources and Electric Power , Zhengzhou, Henan, China
                Author notes

                Edited by: Eckehard Schöll, Technical University of Berlin, Germany

                Reviewed by: Ling Kang, Fudan University, China

                Anna Zakharova, Humboldt University of Berlin, Germany

                *Correspondence: Yong Xu, hsux3@ 123456nwpu.edu.cn ; Jianwei Shen, xcjwshen@ 123456gmail.com
                Article
                1362778
                10.3389/fnetp.2024.1362778
                11002134
                38595864
                a753a15a-6da8-4c1d-a8e2-f3400b196061
                Copyright © 2024 Zheng, Xu and Shen.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 December 2023
                : 04 March 2024
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (12002297 and 12272135), Basic Research Project of Universities in Henan Province (21zx009), Program for Science & Technology Innovation Talents in Universities of Henan Province (22HASTIT018), Funding of Henan Province for merit-based overseas students (2023), Outstanding Young Backbone Teacher of Xuchang University (2022), and Training Program for Young Key Teachers in Colleges and Universities of Henan Province (2023GGJS144). Natural Science Foundation of Henan (242300421396).
                Categories
                Network Physiology
                Original Research
                Custom metadata
                Networks of Dynamical Systems

                hr,pattern formation,network,matrix,turing instability,delay
                hr, pattern formation, network, matrix, turing instability, delay

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