arXiv:2405. 02369v2 Announce Type: replace-cross Abstract: In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons.
By Feng-Lei Fan, Meng Wang, Hang-Cheng Dong, Jianwei Ma, Tieyong Zeng
arXiv:2501.18018v2 Announce Type: replace-cross
Abstract: The neurons of artificial neural networks were originally invented when much less was known about biological neurons than is known today. Our...
By Rorry Brenner, Laurent Itti
arXiv:2607.10735v3 Announce Type: replace-cross
Abstract: We build GNet, a scalable and flexible Gaussian process network with nonparametric activation functions. The key computational contribution i...
By Mengyang Gu
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:2607. 19973v1 Announce Type: new Abstract: AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech.
By Jaeho Seol
arXiv:2606. 21295v2 Announce Type: replace-cross Abstract: Existing sequence models, including RNNs, LSTMs, continuous-time networks, and Transformers, share a common structural principle: layer-wise dynamics, where all neurons in the same layer co-evolve through a shared parameterized operator, leaving individual neurons no freedom to evolve independently.
By Borui Cai, Yao Zhao