Encoding and Decoding Spikes for Dynamic Stimuli.

@article{citeulike:2970641, abstract = {Naturally occurring sensory stimuli are dynamic. In this article, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.}, address = {Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4. rama@cs.toronto.edu.}, author = {Natarajan, Rama and Huys, Quentin J M J. and Dayan, Peter and Zemel, Richard S S. }, citeulike-article-id = {2970641}, doi = {http://dx.doi.org/10.1162/neco.2008.01-07-436}, issn = {0899-7667}, journal = {Neural computation}, keywords = {dynamic\_encoding, learning, network\_dynamics, neural\_coding, population\_code}, month = {April}, posted-at = {2008-07-07 18:30:34}, priority = {3}, title = {Encoding and Decoding Spikes for Dynamic Stimuli.}, url = {http://dx.doi.org/10.1162/neco.2008.01-07-436}, year = {2008} }

TY - JOUR ID - citeulike:2970641 L3 - citeulike-article-id:2970641 TI - Encoding and Decoding Spikes for Dynamic Stimuli. JF - Neural computation AD - Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4. rama@cs.toronto.edu. SN - 0899-7667 N2 - Naturally occurring sensory stimuli are dynamic. In this article, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule. KW - dynamic_encoding KW - learning KW - network_dynamics KW - neural_coding KW - population_code AU - Natarajan, Rama AU - Huys, Quentin J M AU - Dayan, Peter AU - Zemel, Richard S PY - 2008/04/03/ UR - http://dx.doi.org/10.1162/neco.2008.01-07-436 ER -