|Ph.D Student||Einat Kermany|
|Subject||Representation in Neural Networks|
|Department||Department of Medicine||Supervisor||Full Professor Marom Shimon|
|Full Thesis text - in Hebrew|
While the notion that object representation is embedded in sequences of action potentials is fairly well accepted amongst neuroscientists, there is less agreement concerning the actual representation schemes (i.e. neuronal activity features) that carry stimulus-relevant information at the assembly level. Here we offer a general approach to the issue, enabling a systematic and well controlled experimental analysis of constraints and tradeoffs, imposed by the physiology of neuronal populations, on plausible representation schemes. Using in vitro networks of rat cortical neurons as a model system, we compared the efficacy of different kinds of “neural codes” to represent both spatial and temporal input features. Two rate-based representation schemes and two time-based representation schemes were considered. Our results indicate that, by large, all representation schemes perform well in the various discrimination tasks tested, indicating the inherent redundancy in neural population activity; Nevertheless, differences in representation efficacy are identified when unique aspects of input features are considered. We discuss these differences in the context of neural population dynamics.