Spiking network optimized for word recognition in noise predicts auditory system hierarchy
Fig 6
Hierarchical transformation between consecutive network layers enhances word recognition performance and robustness of the optimal HSNN.
(a) The average word accuracy at 5 dB SNR systematically increases across network layers for the optimal HSNN (a, blue) whereas the high-resolution HSNN exhibits a systematic reduction in word recognition accuracy (a, red). For the high-resolution HSNN average firing rates (b, red), information rates (c, red), and information per spike (d, red) are relatively constant across layers indicating minimal transformations of the incoming acoustic information. In contrast, average firing rates (b, blue) and information rates (c, blue) both decrease between the first and last network layers of the optimal network, consistent with a sequential sparsification of the response and a reduction in the acoustic information encoded in the output spike trains. However, the information conveyed by single action potentials (d, blue) in the optimal HSNN sequentially increase between the first and last layer so that individual action potentials become progressively more informative across layers. Continuous curves show the mean whereas error contours designate the SD.