Properties of a temporal population code.
The temporal patterning of neuronal activity may play a substantial role in the representation of sensory stimuli. One particular hypothesis suggests that visual stimuli are represented by the temporal evolution of the instantaneous firing rate averaged over a whole population of neurons. Using an implementation in a cortical type network with lateral interactions, we could previously show that this scheme can be successfully applied to a pattern recognition task. Here, we use a large set of artificially generated stimuli to investigate the coding properties of the network in detail. The temporal population code generated by the network is intrinsically invariant to stimulus translations. We show that the encoding is invariant to small deformations of the stimuli and robust with respect to static and dynamic variations in synaptic strength of the lateral connections in the network. Furthermore, we present several measures which indicate that the encoding maps the stimuli into a high-dimensional space. These results show that a temporal population code is a promising approach for the encoding of relevant stimulus properties while simultaneously discarding the irrelevant information.
Wyss
Reto
R
Institute of Neuroinformatics, University/ETH Zürich, Switzerland. rwyss@ini.unizh.ch
Verschure
Paul F M J
PF
König
Peter
P
D000818:Animals; D005544:Forecasting; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation; D012160:Retina / Q000502:physiology; D013028:Space Perception / Q000502:physiology; D013223:Statistics as Topic / Q000379:methods; D013569:Synapses / Q000502:physiology; D009435:Synaptic Transmission; D013997:Time Factors; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology