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.
By Manjot Singh, Guido Mont\'ufar, Gitta Kutyniok
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.
By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara
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.
By Carlo Wenig, Raoul-Martin Memmesheimer, Christian Klos
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.
By Sanja Karilanova, Subhrakanti Dey, Ay\c{c}a \"Oz\c{c}elikkale
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.
By Dominik Dold, Philipp Christian Petersen
arXiv:2609.08070v1 Announce Type: cross
Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timin...
By Xiangnan Zhang, Jingxin Liu, Ranqi Lu, Jingyu Liu, Qunxi Dong, Fuze Tian, Lixian Zhu, Bin Hu, Bj\"orn W. Schuller
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).
By Himanshu Udupi, Xiaocong Yang, ChengXiang Zhai
arXiv:2606. 00073v1 Announce Type: cross Abstract: We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional connectivity.
By Aditi Aravind, Konstantinos Ladakis, Mario Alexios Savaglio, Stelios M. Smirnakis, Maria Papadopouli
arXiv:2607. 26648v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates.
By Zeyu Wang
Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates. We argue the energy dividend of sparsity is not a property of SNNs but of the task.
arXiv:2608.29869v1 Announce Type: new
Abstract: Spiking neural networks (SNNs) are often regarded as energy-efficient alternatives to artificial neural networks (ANNs), yet their advantage depends cr...
By Miriam Kranzlm\"uller, Pascal Esser, Gitta Kutyniok
SpikeMoE introduces a spike-based k‑WTA router that uses lateral inhibition and refractory periods to select the top‑K experts based on discrete spike counts, inspired by hippocampal CA1 competition. The framework combines spiking neural network dynamics with mixture‑of‑experts conditional computation and adds a two‑stage missing‑modality module for robust multimodal processing. Experiments on vision, language, and multimodal tasks show that SpikeMoE matches or surpasses ANN baselines while offering energy‑efficient performance.
By Xiaoli Liu, Yujie Liang, Jialin Li, Malu Zhang