Cicala, F. | bioRxiv Science

bioRxiv2026

A dual-clam species 63K SNP array for sustainable production and conservation of wild resources

Bivalves play an essential role in coastal ecosystems and their aquaculture represents an important economic sector in Europe playing a pivotal role within the EU Blue Growth Strategy. Among clam species, the Manila clam, Ruditapes philippinarum, and the grooved carpet shell, R. decussatus, are within the top five species in terms of production volume and economic value. In this study we designed and validated the first medium-density 63K single nucleotide polymorphism (SNP) array for these two commercially important species. By leveraging a new chromosome-level genome assembly for R. philippinarum and that of R. decussatus, we identified over 300 million SNPs through whole-genome resequencing and genotyping-by-sequencing strategies. After stringent filtering, we selected 49,392 high-quality SNPs for R. philippinarum and 14,193 for R. decussatus to construct a dual-species array. Array validation was carried out by genotyping 384 individuals across multiple wild populations and hatchery samples, demonstrating excellent performance, with 67.7% and 67.5% of SNPs classified as high-quality polymorphic markers for R. philippinarum and R. decussatus, respectively. Minor allele frequency, missing data rate, and inter-marker distance met stringent quality thresholds, confirming the array robustness for clam population genetics. Parentage analysis in R. philippinarum families highlighted significant power for pedigree reconstruction in breeding programs. This publicly available genomic resource provides a reliable, cost-effective genotyping platform to enable population genomics and advanced selective breeding, genome-wide association studies, and genetic monitoring, ultimately strengthening management of genetic diversity and sustainable farming for two key clam species.

genomics↗

bioRxiv2025

A Mean-Field Approach to Criticality in Spiking Neural Networks for Reservoir Computing

Reservoir computing is a neural network paradigm for processing temporal data by exploiting the dynamics of a fixed, high-dimensional system, enabling efficient computation with reduced complexity compared to fully trainable recurrent networks. This work presents an analytical framework for configuring in the critical regime a reservoir based on spiking neural networks with a highly general topology. Specifically, we derive and solve a mean-field equation that governs the evolution of the average membrane potential in leaky integrate-and-fire neurons, and provide an approximation for the critical point. This framework reduces the need for an extensive online fine-tuning, offering a streamlined path to near-optimal network performance from the outset. Through extensive simulations, we validate the theoretical predictions by analyzing the networks spiking dynamics and quantifying its computational capacity using the information-based Lempel-Ziv-Welch complexity near criticality. Finally, we explore self-organized quasi-criticality by implementing a local learning rule for synaptic weights, demonstrating that the networks dynamics remain close to the theoretical critical point. Beyond AI, our approach and findings also have significant implications for computational neuroscience, providing a principled framework for quantitatively understanding how biological networks leverage criticality for efficient information processing.

neuroscience↗