The paper with title ``Toward Partition-Aware Design of Spiking Neural P Systems''

was authored by Job Lipat, together with Henry Adorna and Francis George Cabarle from our very own Department of Computer Science (DCS), with Miguel Ángel Martínez-del-Amor and David Orellana-Martín from the Department of Computer Science and Artificial Intelligence, University of Seville, Spain.

The paper by Job, a masters student at DCS, won the best student paper award at the International Conference on Membrane Computing 2026 (ICMC2026), a hybrid conference held on 1–3 September 2026 in Seville, Spain. The paper by Job is briefly described as follows:

Spiking neural P (SN P) systems are computing models inspired by the way biological neurons exchange signals. This paper studies how the structure of an SN P system affects the cost of running it across several processing components. We define a partitioned SN P model, adopt the Bulk Synchronous Parallel (BSP) model to account for distributed cost, and apply both to a published SN P multiplier by Zhang et al. For n-bit operands that are all ones, we prove that assigning the neurons evenly among a fixed number of components requires $\Omega(n^2)$ cumulative communication, showing that assigning the neurons differently cannot remove the cost created by the multiplier's structure. Guided by this result, we outline a new multiplier with $\Theta(n)$ neurons instead of $\Theta(n^2)$ and derive a preliminary $O(n\log n)$ communication estimate when its neurons are grouped according to how they communicate. A complete construction, correctness proof, and total cost analysis remain future work.

More information about ICMC2026 is at their website https://icmc2026.org/programme/

Congratulations to Job and team!

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