P260 Oscillatory activity patterns in a detailed model of the prefrontal cortex
Antonio C. Roque*1, Marcelo R. S. Rempel1
1Department of Physics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil
*Email: antonior@usp.br
Introduction
The prefrontal cortex (PFC) is a crucial brain region involved in executive functions, behavioral control, and affective modulation. PFC neurons exhibit distinct activity states, including asynchronous irregular firing during wakefulness and slow oscillations with UP/DOWN state transitions during deep sleep and anesthesia [1]. Previous computational models have investigated the mechanisms underlying these states, but many focus on general cortical networks or sensory cortices. This study aims to replicate and extend a detailed PFC network model to explore the conditions leading to oscillatory activity and UP/DOWN transitions.
Methods A previously published PFC model [2] was reimplemented using Brian2, preserving its original parameters to ensure replication accuracy. Simulations were conducted to compare the original model with three parameter-modified variants. Variant A increased recurrent excitation, inducing hyperactive network fluctuations. Variant B intensified synaptic excitation, resulting in epileptiform-like bursting. Variant C introduced adaptation currents and stochastic external inputs, leading to oscillatory UP/DOWN transitions. Network activity was analyzed through spike raster plots, local field potential (LFP) estimation, and membrane potential dynamics . Results The original model exhibited asynchronous irregular firing, consistent with physiological observations of cortical activity under moderate external drive. Variants A and B disrupted excitation-inhibition balance, promoting excessive synchrony. Variant C successfully generated low-frequency oscillations (~8 Hz) with UP/DOWN transitions, influenced by adaptive currents and external noise, mirroring previous findings in cortical dynamics. Discussion The results align with established models of cortical bistability and highlight the interplay between adaptation and external drive in shaping oscillatory states.
Acknowledgements This work was produced as part of the activities of FAPESP Research, Innovation and Dissemination Center for Neuromathematics (grant 2013/07699-0). ACR is partially supported by a CNPq fellowship (grant 303359/2022-6). References [1] Wang, X. J. (2010). Neurophysiological and computational principles of cortical rhythms in cognition.Physiological Reviews, 90(3), 1195-1268. https://doi.org/10.1152/physrev.00035.2008. [2] Hass, J., Hertäg, L., & Durstewitz, D. (2016). A detailed data-driven network model of prefrontal cortex reproduces key features ofin vivoactivity.PLoS Computational Biology. 12, e1004930. https://doi.org/10.1371/journal.pcbi.1004930.
