spiking neural networks

The Heidelberg Spiking Datasets comprise two spike-based classification datasets: The Spiking Heidelberg Digits (SHD) dataset and the Spiking Speech Command (SSC) dataset. The latter is derived from Pete Warden's Speech Commands dataset (https://arxiv.org/abs/1804.03209), whereas the former is based on a spoken digit dataset recorded in-house and included in this repository. Both datasets were generated by applying a detailed inner ear model to audio recordings. We distribute the input spikes and target labels in HDF5 format.


Co-cultures are a traditional method for studying the cellular properties of cell to cell interactions among different cell types. How network properties in these multicellular synthetic systems vary from monocultures are of particular interest. Understanding the changes in the functional output of these in vitro spiking neural networks can provide new insights into in vivo systems and how to develop biological system models that better reflect physiological conditions - something of paramount importance to the progress of synthetic biology.