Spiking Neural Networks Incrementally Compute Continuous-valued Neural Networks through Attractor Dynamics

Understanding how spiking neural networks (SNN) efficiently process data streams is a key to (i) study how biological brains conserve energy for lifelong predictions and (ii) enable an efficient continuous AI inference. In this work, we propose a theory on how an SNN incrementally computes its corresponding continuous-valued neural network (ANN), namely by reusing prior activation and updating only the temporal difference between ANN and the SNN. Specifically, we prove that discrete SNN output continuously attracts toward the instantaneous ANN output implicitly determined by the SNN input. Based on this principle, we develop LIF-Stream, a efficient streaming SNN inference framework which controls the SNN to continuously adapt to the reference ANN output which computes the high-accuracy frame-wise prediction online. Evaluation on three large-scale streaming inference benchmarks with distinct modalities show that LIF-Stream achieves the best efficiency-accuracy trade-off compared to high-accuracy SNN techniques.