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ILS-based reservoir computing for handwritten digits recognition

Iranmehr, E ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/CFIS49607.2020.9238722
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. ILS-based reservoir is a bio-inspired computational model consisting of spiking neurons which has been designed to process spatiotemporal patterns appropriately. In ILS-based reservoir, the neurons are located in an ionic environment and the connections are provided by ionic density. By using ionic diffusion as a processing operation, this model is able to consider the effects of both the preceding and current stimuli properly. Since character recognition is an important task in various applications, this paper focuses on the classification of handwritten digits using ILS-based reservoir. For this purpose, a neuromorphic handwritten digit dataset called N-MNIST dataset is used as a benchmark. Like all reservoir networks, a readout layer is added to the ILS-based reservoir for classifying this dataset. Classification of the N-MNIST using the proposed network model has resulted in a maximum accuracy of 97.69 % which is comparable to state-of-the-art works using spiking neural networks. © 2020 IEEE
  6. Keywords:
  7. Ionic liquid space (ILS) ; Liquid state machine (LSM) ; N-MNIST dataset ; Spiking neurons ; Biomimetics ; Classification (of information) ; Intelligent systems ; Neural networks ; Computational model ; Handwritten digits recognition ; Maximum accuracies ; Processing operations ; Reservoir Computing ; Reservoir networks ; Spatiotemporal patterns ; Spiking neural networks ; Character recognition
  8. Source: 8th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2020, 2 September 2020 through 4 September 2020 ; 2020 , Pages 7-12
  9. URL: https://ieeexplore.ieee.org/abstract/document/9238722