Wavelet decomposition and phase encoding of temporal signals using spiking neurons

Spike encoding is the initial yet crucial step for any application domain of Artificial Spiking Neural Networks (ASNN). However, current encoding methods are not suitable to process complex temporal signal. Motivated by the modulation relationship found between afferent synaptic currents in biological neurons, this study proposes a spike phase encoding method for ASNN, which could perform wavelet decomposition on the input signal, and encode the wavelet spectrum into synchronized output spike trains. The spike delays in each synchronizing period represent the spectrum amplitudes. The encoding method was tested in two implementation examples: (a) encoding of human voice records for speech recognition propose; and (b) encoding of multichannel electroencephalography (EEG) records with the aim to detect interictal spikes in patients with epilepsy. Empirical evaluations confirm that encoded spike trains constitute a good representation of the continuous wavelet transform of the original signal, with the ability to capture interesting features from the input signal.