← 研踪 PaperTrailarXiv:2610.19732 · v2
Controlling the latency in temporal coded spiking models for neuromorphic datasets
首次提交 2026-10-14 15:53
版本更新 2026-11-09 23:09
雷达观测 2026-11-10 15:22
摘要
Spiking neural networks (SNNs) promise energy-efficient, event-driven computation on neuromorphic hardware, where response time, or latency, matters as much as energy for real-time applications. Time-to-first-spike (TTFS) coding with learnable synaptic delays is a natural way to exploit the timing of events. However, in state-of-the-art spiking neuron models, spike times emerge from nonlinear membrane dynamics and thresholds: no closed-form expression links network parameters to spike times, so latency can only be measured empirically, not specified or guaranteed at design time. Moreover, TTFS models are mostly evaluated on static datasets, which hides their latency on truly temporal data. Our hypothesis is that, if spike times are computed directly as closed-form functions of presynaptic spike times and synaptic delays, bypassing membrane potential and thresholding, latency becomes an explicit function of the learnable parameters that can be bounded, and therefore controlled, analytically. We build on this hypothesis with the Delay Neural Network (DeNN) in real time, which turns event streams into successive single-spike images, computes each spike time from standardised inter-spike intervals and synaptic delays, and links successive images through a long-term memory. We prove lower and upper bounds on the latency of every neuron of a layer, and show that bounding the synaptic parameters after training with a single hyperparameter places the latency anywhere between these bounds, without retraining. On a ten-class subset of the Google Speech Commands dataset, this post-training control exposes the accuracy--latency trade-off: a three-hidden-layer network reaches 94.25\% accuracy while deciding, on average, 477~ms after the end of the one-second input, and about 250~ms with confidence-based early decisions. Learning is also shown to concentrate the long-term memory on a few neurons. Latency thus becomes a design parameter of spiking models rather than a quantity measured after the fact, a step towards neuromorphic systems with real-time guarantees.
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v2 · 2026-11-09 23:09 · 最新观测版本
Controlling the latency in temporal coded spiking models for neuromorphic datasets
Spiking neural networks (SNNs) promise energy-efficient, event-driven computation on neuromorphic hardware, where response time, or latency, matters as much as energy for real-time applications. Time-to-first-spike (TTFS) coding with learnable synaptic delays is a natural way to exploit the timing of events. However, in state-of-the-art spiking neuron models, spike times emerge from nonlinear membrane dynamics and thresholds: no closed-form expression links network parameters to spike times, so latency can only be measured empirically, not specified or guaranteed at design time. Moreover, TTFS models are mostly evaluated on static datasets, which hides their latency on truly temporal data. Our hypothesis is that, if spike times are computed directly as closed-form functions of presynaptic spike times and synaptic delays, bypassing membrane potential and thresholding, latency becomes an explicit function of the learnable parameters that can be bounded, and therefore controlled, analytically. We build on this hypothesis with the Delay Neural Network (DeNN) in real time, which turns event streams into successive single-spike images, computes each spike time from standardised inter-spike intervals and synaptic delays, and links successive images through a long-term memory. We prove lower and upper bounds on the latency of every neuron of a layer, and show that bounding the synaptic parameters after training with a single hyperparameter places the latency anywhere between these bounds, without retraining. On a ten-class subset of the Google Speech Commands dataset, this post-training control exposes the accuracy--latency trade-off: a three-hidden-layer network reaches 94.25\% accuracy while deciding, on average, 477~ms after the end of the one-second input, and about 250~ms with confidence-based early decisions. Learning is also shown to concentrate the long-term memory on a few neurons. Latency thus becomes a design parameter of spiking models rather than a quantity measured after the fact, a step towards neuromorphic systems with real-time guarantees.
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