Spiking neural networks combined with Lempel-Ziv complexity reach up to 98.25% accuracy on the Wisconsin breast cancer dataset, but the result lacks error bars, code, and a working threshold rule for the Levy-Baxter neuron.
Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey
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abstract
For a long time, biology and neuroscience fields have been a great source of inspiration for computer scientists, towards the development of Artificial Intelligence (AI) technologies. This survey aims at providing a comprehensive review of recent biologically-inspired approaches for AI. After introducing the main principles of computation and synaptic plasticity in biological neurons, we provide a thorough presentation of Spiking Neural Network (SNN) models, and we highlight the main challenges related to SNN training, where traditional backprop-based optimization is not directly applicable. Therefore, we discuss recent bio-inspired training methods, which pose themselves as alternatives to backprop, both for traditional and spiking networks. Bio-Inspired Deep Learning (BIDL) approaches towards advancing the computational capabilities and biological plausibility of current models.
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Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
Spiking neural networks combined with Lempel-Ziv complexity reach up to 98.25% accuracy on the Wisconsin breast cancer dataset, but the result lacks error bars, code, and a working threshold rule for the Levy-Baxter neuron.