Fig. 1: Receptive-field-based encoding pipeline.
a Each signal was normalized and converted into 24 spike trains using quantization receptive fields. At each timestep, depending on the value of the signal, a spike was generated by one of 20 receptive fields equally spanning the 0–1 range of input values. Two additional spikes were generated both above and below the central spike making a total of five spikes per timestep (receptive fields in red) and 24 spike trains per signal. b Example of the normalized Mel 1 corresponding to eleven French vowels and encoded into a 24 spike trains. c Audio data decomposed into 24 Mel cepstral coefficients. d Multiunit neural data (middle) from embryonic mouse hindbrain spinal cord on the microelectrode array (MEA, left) was binned and smoothed to extract spike envelopes on each channel (right). e, f Initial encoding spike trains for audio and spike envelope of neural data, respectively. g, h Final encoding spike trains after short-term plasticity (STP) for audio and spike envelope of neural data, respectively. STP eliminated spikes corresponding to noise and only retained spikes indicative of a pattern. Some residual spikes corresponding to noise can be seen across some neural data channels as these channels were very noisy.
