Translating novel insights from computational neuroscience into neuromorphic computing, we show how tightly balanced Spiking Neural Networks outperform existing networks in efficiency, robustness while maintaining competitive accuracy.
NoisyDECOLLE is a Python framework for assessing the robustness of SNNs trained with local learning rules inspired by three-factor learning and synaptic plasticity.
Designing and implementing the neuroAIx FPGA cluster, we show how a combination of novel communication topology, local synchronization algorithm and design paradigms like maximizing neuron-per-node density lead to a neuromorphic system that enables high-speed, deterministic and efficient neuroscience simulations.
The neuroAIx framework is a collection of C++-based simulation tools and hardware platforms to evaluate novel hardware concepts for neuroscience experiment accelerators.