Evolutionary feature selection for spiking neural network pattern classifiers

Michal Valko; Nuno C. Marques; Marco Castelani

cs.NEApr 29, 2026

Evolutionary feature selection for spiking neural network pattern classifiers

Authors: Michal Valko, Nuno C. Marques, Marco Castelani

Organizations: Department of Applied Informatics, Comenius University, Mlynská Dolina, 842 48 Bratislava, Slovakia · CENTRIA,Departmento de Informatica, Faculdade de Ciencias e Tecnologia, University Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal · CENTRIA, Faculdade de Ciencias e Tecnologia, University Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal

Abstract

This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard multi-layer perceptrons is extended with JASTAP model. Preliminary results on IRIS standard data set give evidence that this extension allows the use of smaller neural networks that can handle noisier data without any degradation in classification accuracy.

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Jun 12, 2026 · cs.NEJ/K move · Enter open · S save

Co-Evolved Spiking Neural Network Ensembles via Marginal Contribution Fitness

Catherine Rodriquez, James Ghawaly

Louisiana State University Baton Rouge, Louisiana, USA

Evolutionary optimization of spiking neural networks (SNNs) becomes increasingly difficult as task complexity grows because they must search a combined topology--parameter space that grows super-exponentially with network size. We address this scaling challenge through a co-evolutionary ensemble framework in which a population of candidate SNNs is evolved with fitness defined by each network's marginal contribution to group performance. Grounded in cooperative game theory and difference evaluation functions from multiagent systems, this credit assignment rewards networks that consistently improve ensemble performance and penalizes redundancy, encouraging complementary specialization during evolution rather than relying on post-hoc combination of independently trained networks. We evaluate the approach on classification, regression, and control tasks under

μ

Caspian neuromorphic hardware constraints. Co-evolved ensembles achieve statistically significant improvements over both single-network evolution and post-hoc ensembles across all tasks, with the most pronounced gains in control, where standard evolution fails to discover effective policies and co-evolution enables a qualitative transition to near-optimal performance.

Spiking Neural Networks Co-Evolution Neuromorphic Hardware Neural Network Ensemble Physical Activities

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Abstract

Explore similar work

Co-Evolved Spiking Neural Network Ensembles via Marginal Contribution Fitness

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