Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces
Karlsruhe Institute of Technology, Karlsruhe, Germany（卡尔斯鲁厄理工大学）；FZI Research Center for Information Technology, Karlsruhe, Germany（弗劳恩霍夫信息与通信技术研究中心）；Infineon Technologies, Dresden, Germany（英飞凌科技）
Alexandru Vasilache, Jann Krausse, Klaus Knobloch, Juergen Becker
备注：
This work has been accepted at the 2024 IEEE Biomedical Circuits and Systems Conference
Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia by restoring their ability to perform daily activities. However, current iBMIs suffer from scalability and mobility limitations due to bulky hardware and wiring. Wireless iBMIs offer a solution but are constrained by a limited data rate. To overcome this challenge, we are investigating hybrid spiking neural networks for embedded neural decoding in wireless iBMIs. The networks consist of a temporal convolution-based compression followed by recurrent processing and a final interpolation back to the original sequence length. As recurrent units, we explore gated recurrent units (GRUs), leaky integrate-and-fire (LIF) neurons, and a combination of both - spiking GRUs (sGRUs) and analyze their differences in terms of accuracy, footprint, and activation sparsity. To that end, we train decoders on the "Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology" dataset and evaluate it using the NeuroBench framework, targeting both tracks of the IEEE BioCAS Grand Challenge on Neural Decoding. Our approach achieves high accuracy in predicting velocities of primate reaching movements from multichannel primary motor cortex recordings while maintaining a low number of synaptic operations, surpassing the current baseline models in the NeuroBench framework. This work highlights the potential of hybrid neural networks to facilitate wireless iBMIs with high decoding precision and a substantial increase in the number of monitored neurons, paving the way toward more advanced neuroprosthetic technologies.
Qiushi Academy for Advanced Studies, Zhejiang University（浙江大学求是高等研究院）；College of Biomedical Engineering and Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；China Nanhu Academy of Electronics and Information Technology (CNAEIT)（中国电子科技南湖研究院）
Despite its better bio-plausibility, goal-driven spiking neural network (SNN) has not achieved applicable performance for classifying biological spike trains, and showed little bio-functional similarities compared to traditional artificial neural networks. In this study, we proposed the motorSRNN, a recurrent SNN topologically inspired by the neural motor circuit of primates. By employing the motorSRNN in decoding spike trains from the primary motor cortex of monkeys, we achieved a good balance between classification accuracy and energy consumption. The motorSRNN communicated with the input by…
A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data
University of Chinese Academy of Sciences（中国科学院大学）；Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院深港脑科学研究所脑认知与脑疾病研究所）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks. In this work, we present convolutional spike timing dependent plasticity based feature learning with biologically plausible leaky-integrate-and-fire neurons in Spiking Neural Networks (SNNs). We use shared weight kernels that are trained to encode representative features underlying the input patterns thereby improving the sparsity as well as the robustness of the learning model. We demonstrate…
Intelligent Systems Research Centre（智能系统研究中心）；University of Ulster（阿尔斯特大学）
The practical applications based on recurrent spiking neurons are limited due to their non-trivial learning algorithms. The temporal nature of spiking neurons is more favorable for hardware implementation where signals can be represented in binary form and communication can be done through the use of spikes. This work investigates the potential of recurrent spiking neurons implementations on reconfigurable platforms and their applicability in temporal based applications. A theoretical framework of reservoir computing is investigated for hardware/software implementation. In this framework, only…
Spiking Neural Network for Intra-cortical Brain Signal Decoding
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Hubei Key Laboratory of Brain-inspired Intelligent Systems, Huazhong University of Science and Technology（华中科技大学湖北省类脑智能系统重点实验室）；College of Life Science and Technology, Huazhong University of Science and Technology（华中科技大学生命科学与技术学院）
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
Qiushi Academy for Advanced Studies, Zhejiang University（浙江大学求是高等研究院）；College of Biomedical Engineering and Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；Friedrich Miescher Institute for Biomedical Research（弗里德里希·米歇尔生物医学研究所）；Faculty of Science, University of Basel（巴塞尔大学理学院）；China Nanhu Academy of Electronics and Information Technology (CNAEIT)（中国南湖电子信息技术研究院）
Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activity
Karlsruhe Institute of Technology, Karlsruhe, Germany（卡尔斯鲁厄理工大学）；Infineon Technologies, Dresden, Germany（英飞凌科技）；FZI Research Center for Information Technology, Karlsruhe, Germany（弗劳恩霍夫信息与通信技术研究中心）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Spiking Neural Networks (SNNs) contain more biologically realistic structures and biologically-inspired learning principles than those in standard Artificial Neural Networks (ANNs). SNNs are considered the third generation of ANNs, powerful on the robust computation with a low computational cost. The neurons in SNNs are non-differential, containing decayed historical states and generating event-based spikes after their states reaching the firing threshold. These dynamic characteristics of SNNs make it difficult to be directly trained with the standard backpropagation (BP), which is also consid…
Laboratory of Biological Modeling, NIDDK/NIH（美国国立糖尿病、消化系统疾病和肾病研究所（NIDDK）/美国国立卫生研究院（NIH）生物建模实验室）
Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular sp…
Neurophysiological time series, such as electromyographic signal and intracortical recordings, are typically composed of many individual spiking sources, the recovery of which can give fundamental insights into the biological system of interest or provide neural information for man-machine interfaces. For this reason, source separation algorithms have become an increasingly important tool in neuroscience and neuroengineering. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors, which degrades human-machine interfacing applicati…
University of California, Santa Barbara（加州大学圣巴巴拉分校）
Spiking neural networks (SNNs) are well suited for spatio-temporal learning and implementations on energy-efficient event-driven neuromorphic processors. However, existing SNN error backpropagation (BP) methods lack proper handling of spiking discontinuities and suffer from low performance compared with the BP methods for traditional artificial neural networks. In addition, a large number of time steps are typically required to achieve decent performance, leading to high latency and rendering spike-based computation unscalable to deep architectures. We present a novel Temporal Spike Sequence L…
Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
organization= Department of Computer Science \& Engineering, Khulna University of Engineering \& Technology (KUET) , city= Khulna , postcode= 9203 , country= Bangladesh；organization= Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= USA；organization= Department of Computer Science, American International University-Bangladesh (AIUB) , city= Dhaka , postcode= 1229 , country= Bangladesh
Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning
Faculty of Electronic and Information Engineering, Xi’an Jiaotong University（电子与信息工程学院，西安交通大学）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人工智能与机器人研究院，西安交通大学）；State Key Lab of Brain-Machine Intelligence, Zhejiang University（脑机智能国家重点实验室，浙江大学）；School of Computer Science, Dalian University of Technology（计算机科学学院，大连理工大学）；National Key Lab of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University（人机混合增强智能国家实验室，西安交通大学）
Neuromorphic hardware platforms implement biological neurons and synapses to execute spiking neural networks (SNNs) in an energy-efficient manner. We present SpiNeMap, a design methodology to map SNNs to crossbar-based neuromorphic hardware, minimizing spike latency and energy consumption. SpiNeMap operates in two steps: SpiNeCluster and SpiNePlacer. SpiNeCluster is a heuristic-based clustering technique to partition SNNs into clusters of synapses, where intracluster local synapses are mapped within crossbars of the hardware and inter-cluster global synapses are mapped to the shared interconne…
