Spiking network optimized for word recognition in noise predicts auditory system hierarchy
Fig 1
Auditory hierarchical spiking neural network (HSNN) model.
The model consists of a (a) cochlear model stage that transforms the sound waveform into a spectrogram (time vs. frequency), (b) a central hierarchical spiking neural network containing frequency organized spiking neurons and a (c) Bayesian classifier that is used to read the spatio-temporal spike train outputs of the HSNN. Each dot in the output represents a single spike at a particular time-frequency bin. (d-f) Zoomed in view of the HSNN illustrates the pattern of convergent and divergent connections between network layers for a single leaky integrate-and-fire (LIF) neuron. (d-e) Input spike trains from the preceding network layer are integrated with excitatory (red) and inhibitory (blue) connectivity weights that are spatially localized and model by Gaussian functions (f). The divergence and convergence between consecutive layers is controlled by the connectivity width (SD of the Gaussian model, σl). Each incoming spike generates excitatory and inhibitory post-synaptic potentials (EPSP and IPSP, red and blue kernels in e). The integration time constant (τl) of the EPSP and IPSP kernels can be adjusted to control the temporal integration between consecutive network layers while the spike threshold level (Nl) is independently adjusted to control the output firing rates and the overall neuron layer sensitivity. (g, h) Example cochlear model outputs and the corresponding multi-neuron spike train outputs of the HSNN under the influence of speech babble noise (at 20 dB SNR). (g) HSNN response pattern for one sample of the words zero, six, and eight illustrate output pattern variability that can be used to differentiate words. (h) Example response variability for the word zero from multiple talkers in the presence of speech babble noise (20 dB SNR).