Spiking Neural Networks

Spiking Neural Networks

Neuromorphic perception stacks that process biosignals, tactile data, and event streams through sparse spikes for ultra-low-power intelligence.

• Biologically grounded neuron models tuned with clinical priors
• Tooling that converts ANN checkpoints into deployable SNN graphs
• Edge deployments on Loihi, FPGA accelerators, and custom mixed-signal chips

Research Area Overview

Spiking Neural Networks (SNNs) communicate through 1-bit spikes, mirroring the event-driven nature of biological neurons. This spike-based computation compresses models, reduces energy consumption, and stays close to neuroscience insights, making SNNs ideal for edge devices and neuromorphic chips.

Focus Topics

• Bio-plausible neuron and synapse models rooted in computational neuroscience
• Conversion and training techniques that maintain accuracy while leveraging sparse spike events
• Interfaces between SNNs and neuromorphic sensors (event cameras, tactile skins, bio-signal front-ends)
• ECG and other biosignal analytics via SNNs, with network topologies refined from neuroscience priors for low-compute, explainable decisions