Laboratory of Computing Biological Networks

Some results from our experiments on OSR were shown in the slides. By holding the last state after terminating the periodic stimulus in an inverted manner (differ from the originally adapted brightness), OSR could be eliminated or shifted significantly in the latency. We’ll try to repeat the experiments a few more times, since such designed stimuli would tell us more about how ON and OFF pathways in the retina interact to produce OSR.

Mean Field Theory for a neural population is the lowest order macroscopic description for a neural network. By defining the firing rate probability density of the network, a dynamical equations can be written to describe the system. In usual case, there are two importent element in the equation : the time scale and the gain function. To understand the microscopic correspondence, I introduce the central idea of the derivation.

During the last two months, we started adding EGTA, a calcium chelator in the buffer and observed change in OSR. Surprisingly, period doubling occurred at some specific concentration, and didn’t abolish OSR. We think that EGTA affects the time scale in the whole retina activity.

Stochastic Resonance(SR) is a phenomena that a excitable system is capable of detecting weak signal best with some optimal noise strength. In usually case, the optimal noise strength is only one value (one maximum in the S/N versus noise strength plot). Since our environment is always changing, a crucial problem is that is the noise strength always in this optimal level ? Namely, for different environment, thus different noise strength, does our system automatically tune the excitability such that it is optimal ?

Following Tina's presentation last time, this article was published by the same group this year and they moved on to investigate spatiotemporal memory properties in the cultured neural network. I think some ideas and results in the article may be informative for the work on retina and the future works in cultured neurons in our group.