The paper demonstrates that for a broad class of spiking neuron models, including the leaky integrate‑and‑fire with subtractive reset, any approximation bound proven for multi‑spike networks can be translated to an equivalent single‑spike network with only a linear change in neuron count, and vice versa. This establishes that single‑spike and multi‑spike neural networks possess identical approximation capabilities for general machine learning tasks. Consequently, existing approximation results for single‑spike networks automatically extend to the multi‑spike case.
arXiv:2610. 22484v1 Announce Type: cross Abstract: The input space of single-spike neural networks decomposes into causal pieces, and the number of causal pieces has been shown to provide a lower bound on the approximation error for some spiking neural networks.
arXiv:2606. 03935v1 Announce Type: cross Abstract: The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing.
arXiv:2504.14015v2 Announce Type: replace-cross Abstract: We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study express...
arXiv:2605. 08022v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networks (ANNs).
The paper presents a convex formulation for training spiking neural networks (SNNs), showing that the weight‑decay‑regularized training problem for a class of threshold RNNs—including leaky integrate‑and‑fire SNNs—has zero duality gap and matches a convex program’s optimal value. To handle the exponential size of the resulting convex programs, the authors introduce a randomized surrogate with a high‑probability approximation guarantee. Empirical results demonstrate that the convex variant outperforms surrogate‑gradient training on addition and first‑last‑XOR tasks, matches performance on sequential MNIST, and falls slightly behind a reservoir baseline on base‑3 and base‑5 addition.
arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.
The paper investigates the expressivity of time-to-first-spike spiking neural networks, showing that each neuron's firing time can be represented in a maxout-like form with many constrained affine pieces. It formalizes causal regions as polyhedral sets defined by fixed causal spike sequences and derives bounds on the number of such regions for both shallow and multilayer networks. Experiments confirm that spiking networks can produce richer input-space partitions than conventional feedforward ReLU networks.
SpikeQ introduces a spike‑guided post‑training quantization framework for spiking neural networks, selecting weight levels by simulating downstream neurons to minimize spike errors and designing a reset‑aware grid for pre‑fire membrane potential quantization that avoids spike flips. The method outperforms existing grids on seven image‑processing benchmarks, maintaining full‑precision accuracy with 2‑bit pre‑fire potentials and staying within 2.59 points of full precision at 4 bits while drastically reducing memory and energy usage.
arXiv:2610.15146v2 Announce Type: replace Abstract: Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiki...
arXiv:2605.30361v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer compelling energy efficiency on neuromorphic hardware, yet their training remains challenging because th...
arXiv:2609.22775v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) offer a promising path toward ultra-low-power artificial intelligence inference by emulating the event-driven computatio...
arXiv:2512. 01906v3 Announce Type: replace Abstract: Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing.