WiN-GUI: A graphical tool for neuron-based encoding

Abstract

Neuromorphic computing relies on event-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation. This is usually accomplished by using the real-valued data as current input to a spiking neuron model and tuning the neuron's parameters to match a desired – often biologically inspired – behavior. To support the investigation of neuron models and parameter combinations to identify suitable configurations for neuron-based encoding of sample-based data into spike trains we developed the WiN-GUI. Thanks to the generalized LIF model implemented by default, next to the LIF and Izhikevich neuron models, many spiking behaviors can be investigated out of the box offering the possibility of tuning biologically plausible responses to the input data. The GUI is provided open source and with documentation and is easy to extend with further neuron models and personalize with data analysis functions.