Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier
Lobov, S.A.; Chernyshov, A.V.; Krilova, N.P.; Shamshin, M.O.; Kazantsev, V.B. Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier. Sensors 2020, 20, 500.
Lobov SA, Chernyshov AV, Krilova NP, Shamshin MO, Kazantsev VB. Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier. Sensors. 2020; 20(2):500.Chicago/Turabian Style
Lobov, Sergey A.; Chernyshov, Andrey V.; Krilova, Nadia P.; Shamshin, Maxim O.; Kazantsev, Victor B. 2020. "Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier." Sensors 20, no. 2: 500.
- Supplementary File 1:
ZIP-Document (ZIP, 23570 KB)
Externally hosted supplementary file 1
Description: Video S1: Unsupervised SNN learning The output neurons in the process of learning become selective to different EMG patterns generated by the muscles during a) wrist extension, b) wrist flexion, c) rest. It is impossible to predict which neuron will be responsible for which gesture. At the end of learning, we show that trained neuron has different couplings depending on what signals it responds on. The degree of grayscale of coupling is proportional to the value of weight.
Externally hosted supplementary file 2
Description: Video S2: Supervised SNN learning Supervised learning is stimulation of the target neuron simultaneously with the generation of the corresponding EMG pattern. We would like to achieve the following correspondences of output neurons: a) the left neuron – the movement of the palm to the left, i.e. wrist flexion, b) the middle neuron – rest, c) the right neuron – the movement of the palm to the right, i.e. wrist extension.