Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.37587

Every route counts, but some count more: a max-plus view of the C. elegans brain

Emma Reyner-Fuentes·Sebastian Rudolph·Carmen Peláez-Moreno·Francisco J. Valverde-Albacete
View original source ↗Read source document ↗Export metadata ↓

Abstract

Brains do not simply add up every signal they receive: they select which routes dominate. Yet connectomes are usually analyzed with tools that sum over all paths and place neural states in Euclidean spaces, blurring the feedback circuits that may sustain internal states. Here we treat neural signaling as routing instead of diffusion and describe it with max-plus algebra, in which signals accumulate along a path and the strongest route prevails. We propose a model in which the functional connectome of Caenorhabditis elegans emerges from the time-varying combination of electrical, synaptic, monoaminergic, neuropeptidergic and hormonal layers, and analyze five networks within this framework. Each collapses onto a handful of dominant feedback loops, which recover well-characterized circuits and point to new candidates for experimental study. Unlike linear spectra, these structures keep the biological meaning of the data, and their order-based description suggests that brain states live in lattices rather than Euclidean spaces.

Explore related subjects

q-bio.NCcs.NEmath.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Emma Reyner-Fuentes, Sebastian Rudolph, Carmen Peláez-Moreno, Francisco J. Valverde-Albacete. 2026-10-27. Every route counts, but some count more: a max-plus view of the C. elegans brain. https://arxiv.org/abs/2610.37587

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Inferring Inference

Patterns of microcircuitry in the cerebral cortex suggest that the brain has an array of repeated canonical computational units. However, neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define canonical distributed computations. Here we integrate normative and algorithmic theories of neural computation to present a mathematical framework for inferring canonical distributed computations from large-scale neural activity patterns. At the normative level, we hypothesize that the brain learns a structured internal model of its environment and its latent causes. At the algorithmic level, we propose that this inference process is a nonlinear message-passing algorithm on a graph-structured model of the world. Given a time series of neural activity during a perceptual inference task, our analysis framework finds (i) the neural representation of the relevant latent variables, (ii) interactions between these latent variables that define the brain's internal model of the world, and (iii) message functions that specify the inference algorithm. With enough data, key computational properties are identifiable due to constraints from the symmetries inherent in any canonical computation, up to a joint global transformation of all interactions and the message-passing functions. As a demonstration of this framework, we analyze artificial neural recordings generated by a simulated brain that implicitly implements an approximate inference algorithm. Given simulated inputs and neural activity we successfully recover the latent variables, their neural representation and dynamics, and canonical message functions that govern the dynamics. Analysis of these models reveals features of experimental design required to extract canonical computations from neural data.

q-bio.NC↗
arXiv2026

Prefrontal scaling of reward prediction error readout gates reinforcement-derived adaptive behavior in primates

Reinforcement learning (RL) driven by reward prediction errors (RPEs) enables adaptive behavior across species. While midbrain-striatal encoding of RPEs is well-documented, how prefrontal circuits translate these signals into species-specific flexibility remains unknown. Integrating cross-species RL modeling, neuroimaging, and transcriptomics, we identified shared molecular and neuroanatomical infrastructure for RPE computation in humans and macaques. Despite this conserved foundation, humans demonstrated superior performance on deterministic and probabilistic reversal tasks. Single-trial decoding revealed equally robust dynamic RPE-to-choice prediction in both species; yet humans disproportionately recruited dorsal anterior cingulate (dACC) and dorsolateral prefrontal cortex (dlPFC). Interspecies alignment localized similarly predictive policy signals in macaque dACC/dlPFC that failed to guide behavior. Crucially, a within-species comparison in an independent human cohort demonstrated that only near-optimal performers exhibited strong prefrontal action decoding, contrasting with suboptimal individuals. These findings suggest that behavioral flexibility arises from prefrontal readout breadth - a hierarchical scaling principle that challenges efficient coding theories.

q-bio.NC↗
arXiv2026

Working Memory with Polychronous Chains

Working memory --- the ability to store and recall precise temporal patterns of activity --- remains an open challenge for spiking neural networks (SNNs). A recurrent SNN of $N=1024$ neurons is proposed, in which each synapse is equipped with $D = 41$ heterogeneous delays, parameterised as a weight tensor $\mathbf{W} \in \mathbb{R}^{N \times N \times D}$ and trained end-to-end with surrogate-gradient backpropagation through time. Each stored motif is represented as a sequential chain of overlapping Spiking Motifs: contiguous context windows of length $D$ that uniquely predict the activity at the next time step. A closed-form Hebbian initialisation, derived by deconvolving the LIF membrane response and targeting a membrane potential value above the firing threshold $\vartheta$, already achieves high $F_1$ scores before any gradient step on a benchmark of $M=16$ motifs of duration $T=1000$ steps. With learning, the network reaches a mean $F_1$ score of $0.80$ over the motifs, and retrieval degrades gracefully when the trigger window is corrupted, with an $F_1$ score of $0.53$ at $25\%$ bit-flip noise. The network shows attractor-like retrieval dynamics consistent with hippocampal motif completion and with low-dimensional collective dynamics of population activity on manifolds. These results demonstrate that heterogeneous synaptic delays are an efficient and scalable substrate for working memory in SNNs, with direct implications for neuromorphic edge deployment.

q-bio.NC↗