Profile

_MG_6732.JPG

Shayan Monabbati
 

Dept. Of Electrical Engineering & Computer Science

 
 


BIO

Shayan Monabbati is a 4th year student in the Department of Mechanical Engineering at York University. Shayan is spending the summer deriving mathematical models for different mammalian neuronal firing rates in Dr. Eckford’s laboratory. Specifically, Shayan will be conducting Monte Carlo simulations for matching generated binary random sequences with target sequences (generated by the spike train models) using MATLAB and Python. By the end of the summer, Shayan is hoping to have furthered the research in Optogenetics, a new class of technology recently discovered to better understand neural coding and signaling. Achieving fine control in both high-temporal resolution and cellular precision has massive implications for neurological analysis, biomedical engineering, and the development of Artificial Neural Networks (ANNs).

ABSTRACT

Signal Processing for Optogenetics
For the past decade, the emphasis in the artificial neural network community has been shifted towards spiking neural networks. Many studies consider pulse-coupled neural networks with spike timing as an essential component in signal processing by the brain. Optogenetics is a rapidly growing field of neuroscience where mammalian neurons are genetically modified to express light-sensitive, trans-membrane receptors that enable external control over when the neurons fire or “spike”.
In any study of network dynamics, it is important to answer the question: what is an appropriate model to describe the spiking dynamics of each neuron? This study aims to develop a closed-loop model that is computationally efficient, yet biophysically representative of cortical neurons. We measure how effectively we could use an optogenetic framework to externally stimulate a spike train using a light source to match some desired or “target” spike train by comparing the timing distortion between the sequences.
We have developed a deterministic mathematical model that simulates the different spiking behaviors of cortical neurons as a function of both the input DC current and time elapsed across the membrane. Having precise temporal control and the appropriate metrics allowed us to generate spikes at any given time in order to match the target sequence and minimize the distortion. The ability to reproduce the target sequence has significant potential to improve the understanding of how the brain processes information, to develop treatments for neurological diseases, and to inspire artificial neural networks.