P263 Enhancing Neuronal Modeling with a Modified Hodgkin-Huxley Approach for Ion Channel Dynamics
Batoul M. Saab*1, Jihad Fahs2, Arij Daou1
1Biomedical Engineering Program, American University of Beirut, Lebanon 2Department of Electrical and Computer Engineering, American University of Beirut
*Email: bms28@mail.aub.edu
Introduction The development of precise physical models is imperative for comprehending and manipulating system behavior. Neuronal firing models serve as a pivotal exemplar of intricate biological modeling, crucial for unraveling neural functionality across both normal cognitive processes and pathological disease states. Achieving accurate dynamical modeling of neuronal firing necessitates the meticulous fitting of model parameters through data assimilation, utilizing experimentally gathered recordings. This endeavor poses significant theoretical challenges due to two primary factors: (a) neuronal action potentials are the aggregate result of active nonlinear dynamics interconnecting various neuronal compartments, parameterized by a multitude of unknown variables, and (b) the stochastic nature of the noisy environmental stimuli influencing neuronal activity.
Methods In practice, the fitting of a substantial number of parameters is constrained by the scarcity of observable outputs (recording sites), the complexity of the underlying models, and the time-intensive and expensive nature of conducting experiments under controlled conditions [1]. While neurophysiologists are restricted to a limited range of feasible injection current waveforms, we propose herein to investigate the parameter estimation conundrum of model neurons using diverse quality metrics and processing techniques. Our approach involves optimizing a biophysically realistic model for these neurons [2] using intracellular data obtained via the whole-cell patch-clamp technique from basal-ganglia projecting cortical neurons in brain slices of zebra finches. Results We proceed with adopting a different approach than that adopted by Hodgkin and Huxley [3] in their seminal work whereby we model the activation functions directly using Hill functions rather than fitting both opening rate constants by exponential functions. Our approach provides additional flexibility and is biologically interpretable. Furthermore, using this modified model, we conduct exhaustive searches on a large subset of the model parameters and test different functional metrics to check which one(s) generate reliable and realistic fits to the biological data. Discussion The long-term benefits of this approach include the capability to examine large-scale dynamic phenomena in insightful manners, enhancing model accuracy and streamlining experimentation time. By refining parameter estimation methods and employing biologically interpretable mathematical representations, we aim to improve our understanding of neuronal firing dynamics and provide a robust framework for future computational neuroscience research.
Acknowledgements This work was supported by the University Research Board (URB) and the Medical Practice Plan (MPP) grants at the American University of Beirut. References Reference 1:https://doi.org/10.48550/arXiv.1609.00832 Reference 2:10.1152/jn.00162.2013 Reference 3:10.1113/jphysiol.1952.sp004764
