Gregor Lenz

The Tonic library standardizes event-based data loading and transformation, providing a PyTorch-compatible pipeline that accelerates SNN model training.

Nine neuromorphic tool maintainers navigate open-source funding, standardize edge deployments, and successfully balance commercial scale with biological realism.

Open Neuromorphic is introducing an organizational charter and holding its first Executive Committee elections to foster growth and collaboration. Learn more and get involved!

Digital in-memory computing and INT8 quantization directly accelerate efficient edge training and inference for small vision models on neuromorphic hardware.

The Expressive Leaky Memory (ELM) neuron leverages few memory states and nonlinear dendritic processing to solve long-horizon tasks efficiently.

Discover how the Fugu framework provides a hardware-agnostic intermediate representation for programming scalable, non-deep-learning neuromorphic algorithms.

A bio-inspired visual attention model leverages event cameras and SpiNNaker neuromorphic hardware to give robotic agents low-latency, depth-aware object focus.

Discover the fastest Spiking Neural Network (SNN) frameworks for deep learning-based optimization. Performance, flexibility, and more analyzed in-depth

The Microbrain architecture combines asynchronous processing and Forward Propagation Through Time (FPTT) to train 6.2-million-neuron SNNs.

See a complete workflow for training spiking neural networks in PyTorch with Sinabs and deploying them directly to the event-driven Speck neuromorphic chip.

The EONS framework applies evolutionary algorithms to co-design spiking neural network topologies and parameters for diverse neuromorphic hardware.

Learn how to overcome the discrepancies between synchronous GPU training and asynchronous deployment when building SNNs for SynSense's Speck chip.

Discover methods to efficiently encode and store event-based data from high-resolution event cameras, striking a balance between file size and fast retrieval for spiking neural network training.

The Neural Engineering Framework and Nengo's core Python objects accurately translate high-level algorithmic intentions into functional spiking neural network models.