Laboratory of Computing Biological Networks

The inverse problem for neuronal networks is to infer its topology from analyzing its dynamics. Recently, transfer entropy[1], an information theoretical measure of directed interactions has become more popular for solving the inverse problem[2]. Due to its model-free nature, it can easily be applied to data in a variety of fields such as neuroscience, physiology, climate research and financial markets.

As observed in my experiments, "achromatic" receptive fields may react similarly to blue or green light stimuli, which are the two observable colors in bullfrogs. However, under intermeshed blue and green light stimulus, we constantly (such experiment had been repeated for more than five times) see the activity of recorded ganglion cells synchronize to only one color. I suspect that the retina randomly "follow" a color, since the same retina could produce activity following blue or green light under a same stimulus, and uninfluenced by the starting color.

NCTS Interdisciplinary Workshop on Mathematical Biology & Complex Systems
Date: Friday December 19, 2014, Saturday December 20, 2014
Place: Lecture Room B, 4F, General 3rd Building, NTHU
Organizers:
   Mathematics Biology Focus Program & Complex Systems Focus Group
   National Center for Theoretical Sciences, Hsinchu, Taiwan

Linear-nonlinear(LN) model is a approximation method for linking the time trace of stimulus with the firing rate. However, this is only a approximation and we can actually have some intuitive understanding about when it will fail.