Abstract
LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.
Highlights
Spiking neural networks (SNN) originated in brain science and has received extensive attention in the field of brain-like computing, due to its rich spatiotemporal dynamics, diverse coding schemes, and event-driven characteristics
This paper focuses on constructing LogicSNN, a unified SNN logical operation paradigm, including the definition of logical variables, the chosen of the spike neuron model, the cascading “building block” network structure, which is used to build logical operation modules and the spike-timing–dependent plasticity (STDP) rule to train the modules
A series of experiments of the logical operation module based on LogicSNN mentioned in Section 3 are carried out on the Brian 2 platform [19]
Summary
Spiking neural networks (SNN) originated in brain science and has received extensive attention in the field of brain-like computing, due to its rich spatiotemporal dynamics, diverse coding schemes, and event-driven characteristics. With continuous efforts in studying the structure and mechanism of biological neural networks [1,2], more and more research results have been applied to computational neuroscience and brain-like computing. The research and development of SNN is the process to understand, simulate and make better use of the brain. To achieve these goals, the capability of doing logical operations that the brain can do is basic and essential for SNN. Pattern-matching and coincidence-detection share the same characteristics with logical operation AND [3]
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