|M.Sc Student||Gil Rivnai|
|Subject||The Adaptation Process to Optical Signal in the Rat Retina|
|Department||Department of Electrical Engineering||Supervisors||Professor Porat Moshe|
|Professor Emeritus Perlman Ido|
|Full Thesis text - in Hebrew|
The retina is a neural tissue where visual perception starts. In addition to its main function of converting light to electrical signal (photo-transduction), the retina also processes the visual data, before transmitting it to the brain in order to optimize data transmission and processing. In this project we aimed to develop a multichannel recording system from retinal ganglion cells in order to evaluate adaptation phenomena and the characteristics of the signals transmitted to the brain. One property that we studied was the temporal summation of light-induced signals. According to Bloch’s law, the summation process will cause an increase of the response as long as the stimulus duration is shorter than the critical duration. For longer stimuli, the summation process will not have an additional effect and the response will not change. As opposed to these expectations we found a maximum response for 100msec pulse and a weaker response to shorter or longer pulses. This phenomenon could be due to neural mechanisms that cause amplification of the response. A similar phenomenon was observed in the past and was called the “Broca-Sulzer” effect. We did not find significant effects of pulse duration and of inter-stimulus period upon rate of response decay during light stimulation and rate of recovery from the preceding stimulus. The retinal response to a non-uniform chess board stimulus signal was examined in the second part of the project focusing the response analysis at different areas of the retina (off process, on process, edges). The same parameters of the response were examined to determine the effect of the spatial and time characteristic on response strength, response decay timing, and response recovery. The research conclusion from the second part is that biological retinal response characterization could be used for brain computer interface to assist artificial vision.