Response Fingerprinting: a probabilistic method for evaluating the network response to stimuli

Spiking neural networks that have variable connection delays have the interesting property that they are sensitive to both spatial and temporal patterns of input. Each neuron in the network receives input from spatially-distributed input neurons whose precise firing times interact with connection delays to determine whether the neuron can exceed the firing threshold and produce an output. The network is therefore most responsive to spatio-temporal stimuli whose temporal components match the delays in the connected structure of the network. Izhikevich (2006a) has shown that certain strongly connected groups of neurons known as polychronous neural groups (or PNGs) exist in large numbers within the network structure. The activation of these neural groups is stimulus-specific and produces unique firing signatures that are detectable in the firing data. Previous methods for detecting PNG activation have relied on a template matching technique that assumes a deterministic response to each stimulus presentation (Izhikevich, 2006a; Martinez and Paugam-Moisy, 2009; Guise et al., 2013a). Here we present an alternative probabilistic view of the stimulus response and demonstrate the application of this new detection method.