Spiking Brain 1.0

Spiking Brain 1.0 treats computation as a stream of spikes rather than dense frames, so it wakes only when meaningful events occur. That event-driven design yields quick reaction times and a tiny power budget while preserving fine temporal structure—ideal for DVS cameras, keyword spotting, gesture recognition, and closed-loop control. The model supports both direct SNN training with surrogate gradients and ANN-to-SNN conversion for teams that want to migrate existing CNNs into a spiking form without rewriting pipelines. Inputs can be continuous sensor feeds or batched sequences; the network encodes them into temporal codes, integrates evidence across short windows, and emits either symbolic outputs—schema-true JSON for agents and apps—or motor commands for controllers on ROS or microcontroller stacks. Deployment is flexible: you can run it on standard GPUs for prototyping, then target neuromorphic hardware for production to cut latency and energy while keeping the same graphs and checkpoints. Because spikes are sparse, the system scales gracefully on edge devices, and quantization preserves accuracy without breaking timing. In practice it delivers fast, stable behavior for battery-constrained assistants, embedded copilots, and robots that need to sense, decide, and act in real time rather than after a full frame has been processed.