DoB-SNN: A New Neuron Assembly-inspired Spiking Neural Network for Pattern classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Spiking neural networks (SNNs) as the third generation of artificial neural networks are closer to their biological counterparts than their predecessors. SNNs have a higher computational capacity and lower power requirements than networks of sigmoidal neurons. In this paper, a new spiking neural network for pattern classification referred to as Degree of Belonging SNN (DoB-SNN) is introduced. DoB-SNN is inspired by a neuronal assembly where each neuron has a degree of belonging to every class of data being process. DoB-SNN clusters the neurons during the training process using DoBs to allocate a group of neurons to each class. A new training algorithm is presented to adjust DoBs along with the network's synaptic weights, based on Spike-Timing Dependent Plasticity (STDP) and neurons' activity for training samples. The performance of DoB-SNN is evaluated on five datasets from the UCI machine learning repository. Nested Cross-Validation is employed to determine the network's hyperparameters for each dataset and thoroughly assess generalisation capability. A detailed comparison on these datasets with three other supervised learning algorithms, including SpikeProp, SWAT, and SRESN is provided. The results show that no algorithm significantly outperforms DoB-SNN, Whereas DoB- SNN has significantly better performance than others for Liver disorders dataset (>6.10%, p<0.01). Accuracies obtained by DoB- SNN are significantly greater than SWAT for both Iris and Breast Cancer (>1.69%, p<0.001) and significantly better than SpikeProp for Iris (1.62%, p=0.04). In all comparisons, DoB-SNN used the smallest network, among others. DoB-SNN therefore offers significant potential as alternative SNN architecture and learning algorithm.
Original languageEnglish
Title of host publication2021 International Joint Conference on Neural Networks (IJCNN)
Number of pages6
ISBN (Electronic)978-1-6654-3900-8
ISBN (Print)978-1-6654-4597-9
Publication statusPublished (in print/issue) - 22 Sept 2021
Event2021 International Joint Conference on Neural Networks (IJCNN) - Shenzhen, China
Duration: 18 Jul 202122 Jul 2021


Conference2021 International Joint Conference on Neural Networks (IJCNN)
Internet address


  • spiking neural network
  • SNN
  • degree of belonging
  • DoB
  • spiking neurons
  • Integrate and Fire


Dive into the research topics of 'DoB-SNN: A New Neuron Assembly-inspired Spiking Neural Network for Pattern classification'. Together they form a unique fingerprint.

Cite this