No abstract

With the development of neuromorphic computing, more and more attention has been paid to a brain-inspired spiking neural network (SNN) because of its ultralow energy consumption and high-performance spatiotemporal information processing. Due to the discontinuity of the spiking neuronal activation function, it is still a difficult problem to train brain-inspired deep SNN directly, so SNN has not yet shown performance comparable to that of an artificial neural network. For this reason, the spike-based approximate backpropagation (SABP) algorithm and a general brain-inspired SNN framework are proposed in this paper. The combination of the two can be used for end-to-end direct training of brain-inspired deep SNN. Experiments show that compared with other spike-based methods of directly training SNN, the classification accuracy of this method is close to the best results on MNIST and CIFAR-10 datasets and achieves the best classification accuracy on sonar image target classification (SITC) of small sample datasets. Further analysis shows that compared with artificial neural networks, our brain-inspired SNN has great advantages in computational complexity and energy consumption in sonar target classification.

Recently, deep learning methods have made significant progress in solving hyperspectral images (HSIs) classification problems of high-dimensional features, band redundancy, and spectral mixture. However, the deep neural network is too complex, with a long training time and high energy consumption, making it difficult to deploy on edge computing devices. In order to solve the above problems, this paper proposes a brain-inspired computing framework based on the spiking leaky integrate-andfire neuron model for HSIs classification. Then we design an approximate derivative algorithm to solve the non-differentiable spike activity of the spiking neuron. The framework uses direct coding to generate spatiotemporal spikes for input HSI and achieves efficient extraction of spatial-spectral features through spiking standard convolution and spiking depthwise separable convolution. Extensive experiments are performed on four benchmark hyperspectral data sets and two public unmanned aerial vehicle-borne hyperspectral data sets. Experiments show that the proposed model has the advantages of high classification accuracy and fewer spiking time steps. The proposed model can save about 10 times computational energy consumption compared with the CNN of the same architecture. This research has great significance for overcoming the technical bottleneck of HSI classification based on brain-inspired computing, solving the critical problems of mobile computing in unmanned autonomous systems, and realizing the engineering application of unmanned aerial vehicles and software-defined satellites. The source code will be made available at https://github.com/Katherine-Cao/HSI SNN.

No abstract