Abstract
We model spatiotemporal patterns in locust olfaction with the dynamic neural filter, a recurrent network that produces spatiotemporal patterns in reaction to sets of constant inputs. We specify, within the model, inputs corresponding to different odors and different concentrations of the same odor. Then we proceed to analyze the resulting spatiotemporal patterns of the neurons of our model. Using SVD we investigate three kinds of data: global spatiotemporal data consisting of neuronal firing patterns over the period of odor presentation, spatial data, i.e. total spike counts during this period, and local spatiotemporal data which are neuronal spikes in single temporal bins.
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