The paper investigates the connectomes of *Caenorhabditis elegans* by implementing them as echo state networks within a reservoir computing framework. Using connectomes derived at different ages and through three distinct measurement methods, the authors benchmark performance on neuro-inspired tasks, comparing biological wiring to randomized null models. Results indicate that biological wiring and bio-informed input/output configurations do not consistently outperform random models, and performance varies significantly with reservoir configuration and connectome derivation.
By Felix S. Reimers, Ola Huse Ramstad, Aliaksandr Hubin, Stefano Nichele
arXiv:2609.06093v1 Announce Type: new
Abstract: Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support functi...
By Zhuolin Yu, Xingyu Liu, Yuanhao Jia, Yunhang Xiao, Hairuo Xue, Feihan Sun, Guozhang Chen
The paper proposes a biologically inspired micro‑sleep technique called napping for recurrent spiking neural networks, combining proportional weight scaling with continuous stochastic membrane activity. Experiments on an unsupervised SNN trained with trace‑based STDP on Gabor‑preprocessed MNIST show that well‑tuned napping can match the classification accuracy of conventional weight normalization while offering different clustering characteristics. The study suggests that napping may be preferable when representational structure is more important than raw classification speed, despite its higher simulation cost.
By Andreas Massey, Stefano Nichele, Aliaksandr Hubin
arXiv:2607. 23285v1 Announce Type: cross Abstract: Photonic reservoir computing has attracted increasing attention as a fast and low-cost approach for time-series prediction.
By Sion Park, Kohei Watabe, Satoshi Sunada, Tomoki Yamagami, Atsushi Uchida
NeuroWeaver is an autonomous evolutionary agent that designs EEG analysis pipelines by framing pipeline engineering as a discrete constrained optimization problem solved with large language model–driven code generation. It uses a Domain‑Informed Subspace Initialization to keep the search within neuroscientifically plausible solutions and a Multi‑Objective Evolutionary Optimization to balance performance, novelty, and efficiency. On five diverse benchmarks, NeuroWeaver produces lightweight pipelines that outperform state‑of‑the‑art task‑specific methods and match or exceed large foundation models while using far fewer parameters.
By Guoan Wang, Shihao Yang, Feng Liu
arXiv:2606. 28380v1 Announce Type: cross Abstract: The intricate structures of biological neural networks largely emerge during development, guided by a comparatively compressed blueprint encoded in the genome.
By Mani Hamidi, Sina Khajehabdollahi, Charley M. Wu, Emmanouil Giannakakis
arXiv:2607. 15217v1 Announce Type: cross Abstract: We present NeuronSoup, a neural computation architecture that replaces synchronous layer-by-layer processing with asynchronous, delay-mediated signal propagation through a pool of shared neurons.
By Subodh Kalia
The paper introduces ELiSe, a model that leverages cortical network scaffolds and dendritic compartments to learn complex non‑Markovian spatio‑temporal patterns using only local, always‑on, phase‑free synaptic plasticity. It demonstrates the model’s ability to acquire and replay intricate sequences, exemplified by a birdsong learning mock‑up, and shows robustness to external disturbances and flexibility in parameter settings.
By Laura Kriener, Kristin V\"olk, Ben von H\"unerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.
By Mo Shakiba, Rana Rokni, Mohammad Mohammadi, Nima Dehghani
arXiv:2604. 23878v3 Announce Type: replace Abstract: ZenBrain is a seven-layer, neuroscience-derived memory architecture for LLM agents that unifies fifteen mechanisms - from Two-Factor synaptic consolidation to a Simulation-Selection sleep loop - under a single MemoryCoordinator: nine foundational algorithms plus six Predictive Memory Architecture components.
By Alexander Bering
arXiv:2604. 09229v2 Announce Type: replace-cross Abstract: von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species with complex social cognition, including humans, great apes, cetaceans, and elephants.
By Esila Keskin
The paper demonstrates that using a single activation function across all nodes in artificial neural networks imposes hard limits on evolutionary search, particularly for sparse evolved substrates. By evolving per-node activation functions from an 18-function palette, the authors show that oscillatory functions can solve parity problems at all tested scales, while monotonic functions fail beyond the simplest case. The study reveals that the choice of activation functions, beyond topology and weights, critically influences what evolutionary search can achieve, and that heterogeneous assignments discovered via indirect encoding are unlikely to be selected manually.
By Romain Claret, Michael O'Neill, Paul Cotofrei, Kilian Stoffel