Recent Advances and New Frontiers in Spiking Neural Networks

In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field.Comment: Accepted at IJCAI202

Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning

With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to DNNs with vanilla artificial neurons, the biologically plausible Spiking Neural Network (SNN) contains a diverse population of spiking neurons, making it naturally powerful on state representation with spatial and temporal information. Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding. For input coding, we apply population coding with dynamically receptive fields to directly encode each input state component. For neuronal coding, we propose different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN is trained in conjunction with deep critic networks using the Twin Delayed Deep Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental results show that our TD3-PDSAN model achieves better performance than state-of-the-art models on four OpenAI gym benchmark tasks. It is an important attempt to improve RL with SNN towards the effective computation satisfying biological plausibility.Comment: 27 pages, 11 figures, accepted by Journal of Neural Network

Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network

Learning from the interaction is the primary way biological agents know about the environment and themselves. Modern deep reinforcement learning (DRL) explores a computational approach to learning from interaction and has significantly progressed in solving various tasks. However, the powerful DRL is still far from biological agents in energy efficiency. Although the underlying mechanisms are not fully understood, we believe that the integration of spiking communication between neurons and biologically-plausible synaptic plasticity plays a prominent role. Following this biological intuition, we optimize a spiking policy network (SPN) by a genetic algorithm as an energy-efficient alternative to DRL. Our SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based spikes. Inspired by biological research that the brain forms memories by forming new synaptic connections and rewires these connections based on new experiences, we tune the synaptic connections instead of weights in SPN to solve given tasks. Experimental results on several robotic control tasks show that our method can achieve the performance level of mainstream DRL methods and exhibit significantly higher energy efficiency

Explaining cocktail party effect and McGurk effect with a spiking neural network improved by Motif-topology

Network architectures and learning principles have been critical in developing complex cognitive capabilities in artificial neural networks (ANNs). Spiking neural networks (SNNs) are a subset of ANNs that incorporate additional biological features such as dynamic spiking neurons, biologically specified architectures, and efficient and useful paradigms. Here we focus more on network architectures in SNNs, such as the meta operator called 3-node network motifs, which is borrowed from the biological network. We proposed a Motif-topology improved SNN (M-SNN), which is further verified efficient in explaining key cognitive phenomenon such as the cocktail party effect (a typical noise-robust speech-recognition task) and McGurk effect (a typical multi-sensory integration task). For M-SNN, the Motif topology is obtained by integrating the spatial and temporal motifs. These spatial and temporal motifs are first generated from the pre-training of spatial (e.g., MNIST) and temporal (e.g., TIDigits) datasets, respectively, and then applied to the previously introduced two cognitive effect tasks. The experimental results showed a lower computational cost and higher accuracy and a better explanation of some key phenomena of these two effects, such as new concept generation and anti-background noise. This mesoscale network motifs topology has much room for the future

Effect of Sc and Zr Additions on Recrystallization Behavior and Intergranular Corrosion Resistance of Al-Zn-Mg-Cu Alloys

The recrystallization and intergranular corrosion behaviors impacted by the additions of Sc and Zr in Al-Zn-Mg-Cu alloys are investigated. The stronger effect of coherent Al3(Sc1−xZrx) phases on pinning dislocation resulted in a lower degree of recrystallization in Al-Zn-Mg-Cu-Sc-Zr alloy, while the subgrain boundaries can escape from the pinning of Al3Zr phases and merge with each other, bringing about a higher degree of recrystallization in Al-Zn-Mg-Cu-Zr alloy. A low degree of recrystallization promotes the precipitation of grain boundary precipitates (GBPs) with a discontinuous distribution, contributing to the high corrosion resistance of Al-Zn-Mg-Cu-Sc-Zr alloy in the central layer. The primary Al3(Sc1−xZrx) phase promotes recrystallization due to particle-stimulated nucleation (PSN), and acts as the cathode to stimulate an accelerated electrochemical process between the primary Al3(Sc1−xZrx) particles and GBPs, resulting in a sharp decrease of the corrosion resistance in the surface layer of Al-Zn-Mg-Cu-Sc-Zr alloy

Atmospheric Peroxides in a Polluted Subtropical Environment: Seasonal Variation, Sources and Sinks, and Importance of Heterogeneous Processes

Hydrogen peroxide (H₂O₂) and organic peroxides play an important role in atmospheric chemistry, but knowledge of their abundances, sources, and sinks from heterogeneous processes remains incomplete. Here we report the measurement results obtained in four seasons during 2011–2012 at a suburban site and a background site in Hong Kong. Organic peroxides were found to be more abundant than H₂O₂, which is in contrast to most previous observations. Model calculations with a multiphase chemical mechanism suggest important contributions from heterogeneous processes (primarily transition metal ion [TMI]-HOx reactions) to the H₂O₂ budget, accounting for about one-third and more than half of total production rate and loss rate, respectively. In comparison, they contribute much less to organic peroxides. The fast removal of H₂O₂ by these heterogeneous reactions explains the observed high organic peroxide fractions. Sensitivity analysis reveals that the role of heterogeneous processes depends on the abundance of soluble metals in aerosol, serving as a net H₂O₂ source at low metal concentrations, but as a net sink with high metal loading. The findings of this study suggest the need to consider the chemical processes in the aerosol aqueous phase when examining the chemical budget of gas-phase H₂O₂