arXiv AI
Causal pieces: analysing and improving spiking neural networks piece by piece
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arXiv Machine Learning
Sep 11
Polyhedral Geometry of Time-to-First-Spike Neural Networks
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.
arXiv Machine Learning
Jul 17
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
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.
arXiv Machine Learning
Jun 3
Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent
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 Machine Learning
Aug 12
Delays in Spiking Neural Networks: A State Space Model Approach
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.
arXiv AI
Sep 16
Equivalence of approximation by networks of single- and multi-spike neurons
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 Machine Learning
Sep 10
A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
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...