Johnrob Y. Bantang

Education:

PhD – University of the Philippines, Diliman

MS – University of the Philippines, Diliman

BS – University of the Philippines, Diliman

Academic Group: Instrumentation Physics Laboratory

Fields of Interest: Complex systems: granular matter mixing, segregation, and heap stability; cellular automata model of dynamical systems, chaotic systems, complex networks and social dynamics; models of biological system dynamics.

Dr. Johnrob Y. Bantang is a Associate Professor of the National Institute of Physics (NIP) at the University of the Philippines. He is also currently the Adviser of Complexity Science Group sub-group of the Instrumentation Physics Laboratory. He obtained his BS, MS, and PhD degrees from the University of the Philippines Diliman in 2000, 2002, and 2006. He graduated with Latin Honor: cum Laude.

His Research Interests: Complex systems: granular matter mixing, segregation, and heap stability; cellular automata model of dynamical systems, chaotic systems, complex networks and social dynamics; ordinary differential equation model of virus-cell interaction; Membrane system simulation using Brane calculus formalism; Optics and imaging: focused light propagation and imaging through turbid media

Dr. Bantang is currently the Coordinator/Program Director, Computational Science Research Center, College of Science, 2018 – present. Technical Working Group/BAC of College of Science 2016 – present. NIP Executive Council Secretary/Scribe, 2016-2019. Former Program Coordinator of Instrumentation Physics Laboratory, 2018-2019. Former Officer-In-Charge as Deputy Director for Facilities and Resources, NIP, 2014-2017. Former Director, University Computer Center (UP Diliman), 2011-2014.

Young and Aged Neurinal Tissue Dynamics With a Simplified Neuronal Patch Cellular Automata Model

Johnrob Y. Bantang, Ph.D., Reinier Xander A. Ramos, and Jacqueline C. Dominguez

Instrumentation Physics Laboratory, National Institute of Physics,

University of the Philippines, Diliman, Quezon City, Philippines

Realistic single-cell neuronal dynamics are typically obtained by solving models that involve solving a set of differential equations similar to the Hodgkin-Huxley (HH) system. However, realistic simulations of neuronal tissue dynamics —especially at the organ level, the brain— can become intractable due to an explosion in the number of equations to be solved simultaneously. Consequently, such efforts of modeling tissue- or organ-level systems require a lot of computational time and the need for large computational resources. Here, we propose to utilize a cellular automata (CA) model as an efficient way of modeling a large number of neurons reducing both the computational time and memory requirement. First, a first-order approximation of the response function of each HH neuron is obtained and used as the response-curve automaton rule. We then considered a system where an external input is in a few cells. We utilize a Moore neighborhood (both totalistic and outer-totalistic rules) for the CA system used. The resulting steady-state dynamics of a two-dimensional (2D) neuronal patch of size 1, 024 × 1, 024 cells can be classified into three classes: (1) Class 0–inactive, (2) Class 1–spiking, and (3) Class 2–oscillatory. We also present results for different quasi-3D configurations starting from the 2D lattice and show that this classification is robust. The numerical modeling approach can find applications in the analysis of neuronal dynamics in mesoscopic scales in the brain (patch or regional). The method is applied to compare the dynamical properties of the young and aged population of neurons. The resulting dynamics of the aged population shows higher average steady-state activity 〈a(t → ∞)〉 than the younger population. The average steady-state activity 〈a(t → ∞)〉 is significantly simplified when the aged population is subjected to external input. The result conforms to the empirical data with aged neurons exhibiting higher firing rates as well as the presence of firing activity for aged neurons stimulated with lower external current.

Figure 1. A simplified neuronal response used for the CA model of neurons. The output aout is related to the probability of the neuron to fire given the input ain. The plot shows such functions for threshold values a0 = 0.2, a1 = 0.6, a2 = 0.8. The dashed line corresponds to aout = ain useful in the cobweb analysis.