{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:10:01Z","timestamp":1745367001539,"version":"3.40.4"},"reference-count":43,"publisher":"MIT Press","issue":"5","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Neural network complexity allows for diverse neuronal population dynamics and realizes higherorder brain functions such as cognition and memory. Complexity is enhanced through chemical synapses with exponentially decaying conductance and greater variation in the neuronal connection strength due to synaptic plasticity. However, in the macroscopic neuronal population model, synaptic connections are often described by spike connections, and connection strengths within the population are assumed to be uniform. Thus, the effects of synaptic connections variation on network synchronization remain unclear. Based on recent advances in mean field theory for the quadratic integrate-and-fire neuronal network model, we introduce synaptic conductance and variation of connection strength into the excitatory and inhibitory neuronal population model and derive the macroscopic firing rate equations\u00a0for faithful modeling. We then introduce a heuristic switching rule of the dynamic system with respect to the mean membrane potentials to avoid divergences in the computation caused by variations in the neuronal connection strength. We show that the switching rule agrees with the numerical computation of the microscopic level model. In the derived model, variations in synaptic conductance and connection strength strongly alter the stability of the solutions to the equations, which is related to the mechanism of synchronous firing. When we apply physiologically plausible values from layer 4 of the mammalian primary visual cortex to the derived model, we observe event-related desynchronization at the alpha and beta frequencies and event-related synchronization at the gamma frequency over a wide range of balanced external currents. Our results show that the introduction of complex synaptic connections and physiologically valid numerical values into the low-dimensional mean field equations\u00a0reproduces dynamic changes such as eventrelated (de)synchronization, and provides a unique mathematical insight into the relationship between synaptic strength variation and oscillatory mechanism.<\/jats:p>","DOI":"10.1162\/neco_a_01749","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T18:38:46Z","timestamp":1742495926000},"page":"987-1009","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Distributed Synaptic Connection Strength Changes Dynamics in a Population Firing Rate Model in Response to Continuous External Stimuli"],"prefix":"10.1162","volume":"37","author":[{"given":"Masato","family":"Sugino","sequence":"first","affiliation":[{"name":"Department of Precision Engineering, University of Tokyo, Tokyo 113-8656, Japan sugino@neuron.t.u-tokyo.ac.jp"}]},{"given":"Mai","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Department of Human and Engineered Environmental Studies, University of Tokyo, Chiba 277-8563, Japan mai.tanaka@neuro.t.u-tokyo.ac.jp"}]},{"given":"Kenta","family":"Shimba","sequence":"additional","affiliation":[{"name":"Department of Human and Engineered Environmental Studies, University of Tokyo, Chiba 277-8563, Japan shimba@neuron.t.u-tokyo.ac.jp"}]},{"given":"Kiyoshi","family":"Kotani","sequence":"additional","affiliation":[{"name":"Department of Human and Engineered Environmental Studies, University of Tokyo, Chiba 277-8563, Japan kotani@neuron.t.u-tokyo.ac.jp"}]},{"given":"Yasuhiko","family":"Jimbo","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, University of Tokyo, Tokyo 113-8656, Japan jimbo@neuron.t.u-tokyo.ac.jp"}]}],"member":"281","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"issue":"1","key":"2025042219354368100_bib1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.97.012209","article-title":"Relationship between the mechanisms of gamma rhythm generation and the magnitude of the macroscopic phase response function in a population of excitatory and inhibitory modified quadratic integrate-and-fire neurons","volume":"97","author":"Akao","year":"2018","journal-title":"Physical Review E"},{"issue":"1","key":"2025042219354368100_bib2","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2560\/5\/1\/003","article-title":"A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior","volume":"5","author":"Bai","year":"2007","journal-title":"Journal of Neural Engineering"},{"issue":"1","key":"2025042219354368100_bib3","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevResearch.2.013042","article-title":"Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons","volume":"2","author":"Bi","year":"2020","journal-title":"Physical Review Research"},{"issue":"1","key":"2025042219354368100_bib4","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1152\/jn.01095.2002","article-title":"What determines the frequency of fast network oscillations with irregular neural discharges? 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