arXiv (All)
open access
Evolutionary feature selection for spiking neural network pattern classifiers
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FidLIF: Parallel Training of High-Fidelity Spiking Neurons with Exact BPTT#1341806
- Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware#19786
- Classification of multivariate data with a spiking neural network on neuromorphic hardware#1299006
- Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies#984171
- Causal pieces: analysing and improving spiking neural networks piece by piece#1141412
Record · ID 1199811
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive