A review of learning in biologically plausible spiking neural networks

By Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Georgina Cosma, Liam P. Maguire, T. McGinnity. Neural Networks, 2019

384 citations recorded by the source corpus.

Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of SNN learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the SNN field are discussed.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/39c9044bb2b1785de16db65d6e6fa5b0710c480f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Zhaofei Yu
2. Wei Fang
3. Yanqing Chen
4. Timothée Masquelier

In Cognitive Neuroscience

1. Zhaofei Yu
2. Wei Fang
3. Yanqing Chen
4. Timothée Masquelier
5. Sergio Davies
6. Yonghong Tian
7. Michael Pfeiffer

In Electrical and Electronic Engineering

1. Zhaofei Yu
2. Wei Fang
3. Yanqing Chen
4. Timothée Masquelier
5. Sergio Davies
6. Yonghong Tian
7. Michael Pfeiffer

Citations in the corpus

• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence (2023, methodology): “Unsupervised learning algorithms inspired by the biological nervous system are applied in SNNs, including Hebbian learning ( 32 ), spike timing – dependent plasticity (STDP) ( 33 ), and their variants ( 34 – 38 ).”