Minicolumn-Based Episodic Memory Model With Spiking Neurons, Dendrites and Delays

Biologically Inspired Spatial-Temporal Perceiving Strategies for Spiking Neural Network

A future unmanned system needs the ability to perceive, decide and control in an open dynamic environment. In order to fulfill this requirement, it needs to construct a method with a universal environmental perception ability. Moreover, this perceptual process needs to be interpretable and understandable, so that future interactions between unmanned systems and humans can be unimpeded. However, current mainstream DNN (deep learning neural network)-based AI (artificial intelligence) is a 'black box'. We cannot interpret or understand how the decision is made by these AIs. An SNN (spiking neural network), which is more similar to a biological brain than a DNN, has the potential to implement interpretable or understandable AI. In this work, we propose a neuron group-based structural learning method for an SNN to better capture the spatial and temporal information from the external environment, and propose a time-slicing scheme to better interpret the spatial and temporal information of responses generated by an SNN. Results show that our method indeed helps to enhance the environment perception ability of the SNN, and possesses a certain degree of robustness, enhancing the potential to build an interpretable or understandable AI in the future.

Biologically Plausible Sparse Temporal Word Representations

Word representations, usually derived from a large corpus and endowed with rich semantic information, have been widely applied to natural language tasks. Traditional deep language models, on the basis of dense word representations, requires large memory space and computing resource. The brain-inspired neuromorphic computing systems, with the advantages of better biological interpretability and less energy consumption, still have major difficulties in the representation of words in terms of neuronal activities, which has restricted their further application in more complicated downstream language tasks. Comprehensively exploring the diverse neuronal dynamics of both integration and resonance, we probe into three spiking neuron models to post-process the original dense word embeddings, and test the generated sparse temporal codes on several tasks concerning both word-level and sentence-level semantics. The experimental results show that our sparse binary word representations could perform on par with or even better than original word embeddings in capturing semantic information, while requiring less storage. Our methods provide a robust representation foundation of language in terms of neuronal activities, which could potentially be applied to future downstream natural language tasks under neuromorphic computing systems.

Training multi-layer spiking neural networks with plastic synaptic weights and delays

Spiking neural networks are usually considered as the third generation of neural networks, which hold the potential of ultra-low power consumption on corresponding hardware platforms and are very suitable for temporal information processing. However, how to efficiently train the spiking neural networks remains an open question, and most existing learning methods only consider the plasticity of synaptic weights. In this paper, we proposed a new supervised learning algorithm for multiple-layer spiking neural networks based on the typical SpikeProp method. In the proposed method, both the synaptic weights and delays are considered as adjustable parameters to improve both the biological plausibility and the learning performance. In addition, the proposed method inherits the advantages of SpikeProp, which can make full use of the temporal information of spikes. Various experiments are conducted to verify the performance of the proposed method, and the results demonstrate that the proposed method achieves a competitive learning performance compared with the existing related works. Finally, the differences between the proposed method and the existing mainstream multi-layer training algorithms are discussed.