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Neuromorphic computing relies on spike-based, energy-efficient communication,inherently implying the need for conversion between real-valued (sensory) dataand binary, sparse spiking representation. This is usually accomplished usingthe real valued data as current input to a spiking neuron model, and tuning theneuron's parameters to match a desired, often biologically inspired behaviour.We developed a tool, the WaLiN-GUI, that supports the investigation of neuronmodels and parameter combinations to identify suitable configurations forneuron-based encoding of sample-based data into spike trains. Due to thegeneralized LIF model implemented by default, next to the LIF and Izhikevichneuron models, many spiking behaviors can be investigated out of the box, thusoffering the possibility of tuning biologically plausible responses to theinput data. The GUI is provided open source and with documentation, being easyto extend with further neuron models and personalize with data analysisfunctions.

WaLiN-GUI: a graphical and auditory tool for neuron-based encoding