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Gesture recognition utilizes deep learning network model to automatically extract deep features of data; however, traditional machine learning algorithms rely on manual feature extraction and poor model generalization ability. In this paper, a multimodal gesture recognition algorithm based on convolutional long-term memory network is proposed. First, a convolutional neural network (CNN) is employed to automatically extract the deeply hidden features of multimodal gesture data. Then, a time series model is constructed using a long short-term memory (LSTM) network to learn the long-term dependence of multimodal gesture features on the time series. On this basis, the classification of multimodal gestures is realized by the SoftMax classifier. Finally, the method is experimented and evaluated on two dynamic gesture datasets, VIVA and NVGesture. Experimental results indicate that the accuracy rates of the proposed method on the VIVA and NVGesture datasets are 92.55% and 87.38%, respectively, and its recognition accuracy and convergence performance are better than those of other comparison algorithms. [ABSTRACT FROM AUTHOR]

As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long-term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms. [ABSTRACT FROM AUTHOR]