Neuromorphic computing relies on spike-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation. This is usually accomplished 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 behaviour. We developed a tool, the WaLiN-GUI, that supports the investigation of neuron models and parameter combinations to identify suitable configurations for neuron-based encoding of sample-based data into spike trains. Due 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, thus offering the possibility of tuning biologically plausible responses to the input data. The GUI is provided open source and with documentation, being easy to extend with further neuron models and personalize with data analysis functions.

翻译：神经形态计算依赖基于脉冲的节能通信，这本质上要求将实值（感知）数据与二值稀疏脉冲表示进行相互转换。通常通过将实值数据作为电流输入至脉冲神经元模型，并调节神经元参数以匹配期望的（通常受生物学启发）行为来实现。我们开发了WaLiN-GUI工具，支持对神经元模型及参数组合的探究，以确定适用于将基于样本的数据编码为脉冲序列的神经元配置。由于默认实现了通用LIF模型（此外还有LIF和Izhikevich神经元模型），可直接研究多种脉冲行为，从而能够对输入数据调节出具有生物学合理性的响应。该GUI以开源形式提供并附有文档，易于扩展更多神经元模型及个性化数据分析功能。