evolutionary-snn-classifier

name	evolutionary-snn-classifier
description	Evolutionary feature selection for spiking neural network pattern classifiers using the biologically realistic JASTAP model. Combines evolutionary algorithms with SNN training for simultaneous architecture and feature optimization.

Evolutionary SNN Classifier with JASTAP

Research methodology from paper "Evolutionary feature selection for spiking neural network pattern classifiers" (2026-04-29).

Core Approach

Applies evolutionary feature selection to the JASTAP (biologically realistic spiking neural network model) for pattern classification tasks.

JASTAP Neural Network Model

• Biologically realistic alternative to standard multi-layer perceptrons
• Incorporates spiking neuron dynamics
• More faithful to biological neural computation than traditional ANNs

Evolutionary Procedure

The paper applies an evolutionary procedure for:

1. Simultaneous feature selection - identifying optimal input feature subsets
2. Architecture optimization - finding optimal network configurations
3. Parameter tuning - optimizing synaptic weights and neuron parameters

Key Benefits

• Reduces input dimensionality automatically
• Finds biologically plausible network architectures
• Avoids manual feature engineering
• Jointly optimizes features and network structure

When to Use

• Classification tasks with high-dimensional input
• Need for biologically plausible neural models
• Scenarios where feature selection is critical
• Applications requiring interpretable feature importance

Workflow

1. Define feature space and JASTAP network architecture
2. Initialize evolutionary population (feature subsets + network configs)
3. Evaluate fitness (classification accuracy + model complexity)
4. Apply selection, crossover, mutation
5. Iterate until convergence
6. Deploy best individual's feature subset + network

Related Skills

• spiking-neural-network-analysis
• bio-neuron-snn-learning
• multi-plasticity-snn-training

Paper Reference

• arXiv: 2604.26654
• Authors: Michal Valko, Nuno C. Marques, Marco Castelani
• Date: 2026-04-29
• Categories: cs.NE