DBNN: Neural Spike Classification Using a Deep Binarized Neural Network
Implantable brain-computer interfaces require on-node spike sorting to reduce telemetry bandwidth and power while maintaining reliable neural decoding. This paper presents a hardware-oriented deep binarized neural network (DBNN) spike-sorting system with two binarized hidden layers with 256 neurons and a fixed-point output layer to enable multiplier-free inference dominated by sign-controlled accumulation and bit-wise logic. The proposed classifier operates on compact 16-sample spike waveforms to reduce the implementation cost (16-256-256-3) and achieves a median classification accuracy of 98.7% on both synthetic and in-vivo datasets. An FPGA prototype on a Cyclone V device operates at 50...
| Authors | Binyi Ren, Luca M. Meyer, Majid Zamani |
|---|---|
| Primary category | eess.SP |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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