Bio

Subashini Lakshmanan is pursuing master’s degree in bioengineering at Temple University. She is interested in studying and understanding the complexity and the randomness exhibited by the activity of the human brain’s neural population. Her works involve gaining a clear perspective and formulation of a mathematical model that would represent and predict the activity of the neuron firing as a complete system that would explain both the analytical function of the brain and the creative output (irrespective of both being subjective). Her undergraduate project was the designing and implementation of a hardware-based text to speech convertor using signal processing, natural language processing, and artificial neural networks (recorded sounds were not used; instead, they were synthesized using a Klatt synthesizer). After finishing her undergraduate studies, she worked on studying the echolocation behavior of the bat in relation with its brain activity using the existing literature in a startup company. She tried to understand the neural processing aspect of the echolocation behavior. She is currently working on formulating a navigation pattern for the communication between each bat in a crowded bat cave, which could be used in the robotics’ visual and communication aspect. Her current master thesis work is based on the peripheral neural system’s CPG model.

Her other interests involve painting, listening to music, and watching animation series. She has been conceptualizing a movie script for a very long time just for fun, which is based on third-century science concepts, continental drift, and the consequent human migration

Project: Abstract Model of the Interconnected Neural Tissue Using Cellular Automata

A continuous automaton would be programmed based on the rule that depicts the input, output, and the interconnection relation of the neural tissue. The weight factor of this CA would be changed using various distribution functions and the resulting pattern would be compared and studied. Its output would be simulated and analyzed against the properties of a normal EEG. The distribution function and the corresponding weighing function are changed to study the various EEG patterns that could be obtained as an output of the model. It is compared against the standard EEG model to recognize the change in its output pattern.

Favorite Outer Totalistic r=1, k=2 2D Cellular Automaton

Rule 94817