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Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey

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arxiv 2307.16235 v1 pith:EBRF4KST submitted 2023-07-30 cs.NE cs.AIcs.CVcs.LG

Gabriele Lagani , Fabrizio Falchi , Claudio Gennaro , Giuseppe Amato This is my paper · ORCID
classification cs.NEcs.AIcs.CVcs.LG
keywords bio-inspiredspikingapproachesbiologicaldeeplearningmainmodels
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection

    cs.NE 2025-06 reject novelty 4.0 of 10

    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.

  2. CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge

    cs.CV 2025-05 conditional novelty 4.0 of 10

    A compact spatio-temporal network mixing convolutions and linear-complexity temporal attention reaches strong accuracy on UCF101, HMDB51, and Kinetics400 with a 7M-parameter model and float16 training.

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