no code implementations • 29 May 2024 • Yiting Dong, Xiang He, Guobin Shen, Dongcheng Zhao, Yang Li, Yi Zeng
EventZoom employs a progressive temporal strategy that intelligently blends time and space to enhance the diversity and complexity of the data while maintaining its authenticity.
no code implementations • 23 May 2024 • Linghao Feng, Dongcheng Zhao, Sicheng Shen, Yiting Dong, Guobin Shen, Yi Zeng
This paper presents a novel approach leveraging Spiking Neural Networks (SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) with a temporal codebook inspired by hippocampal time cells.
no code implementations • 30 Apr 2024 • Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, Yi Zeng
Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal representations.
no code implementations • 29 Feb 2024 • Yi Zeng, Feifei Zhao, Yuxuan Zhao, Dongcheng Zhao, Enmeng Lu, Qian Zhang, Yuwei Wang, Hui Feng, Zhuoya Zhao, Jihang Wang, Qingqun Kong, Yinqian Sun, Yang Li, Guobin Shen, Bing Han, Yiting Dong, Wenxuan Pan, Xiang He, Aorigele Bao, Jin Wang
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
no code implementations • 1 Feb 2024 • Yang Li, Yinqian Sun, Xiang He, Yiting Dong, Dongcheng Zhao, Yi Zeng
Efficient parallel computing has become a pivotal element in advancing artificial intelligence.
2 code implementations • 22 Jan 2024 • Sicheng Shen, Dongcheng Zhao, Guobin Shen, Yi Zeng
Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in processing diverse datasets.
no code implementations • 12 Dec 2023 • Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Jindong Li, Kang Sun, Yi Zeng
Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism.
no code implementations • 17 Nov 2023 • Guobin Shen, Dongcheng Zhao, Tenglong Li, Jindong Li, Yi Zeng
This paper introduces a unified perspective, illustrating that the time steps in SNNs and quantized bit-widths of activation values present analogous representations.
no code implementations • 28 Sep 2023 • Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware.
no code implementations • 23 Aug 2023 • Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Feifei Zhao, Yi Zeng
This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt.
no code implementations • 23 May 2023 • Dongcheng Zhao, Guobin Shen, Yiting Dong, Yang Li, Yi Zeng
Notably, our algorithm has achieved state-of-the-art performance on neuromorphic datasets DVS-CIFAR10 and N-Caltech101, and can achieve superior performance in the test phase with timestep T=1.
no code implementations • 19 May 2023 • Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Yi Zeng
The biological neural network is a vast and diverse structure with high neural heterogeneity.
no code implementations • 17 May 2023 • Linghao Feng, Dongcheng Zhao, Yi Zeng
As it stands, such models are primarily limited to the domain of artificial neural networks.
no code implementations • 13 Apr 2023 • Yiting Dong, Dongcheng Zhao, Yi Zeng
However, SNNs typically grapple with challenges such as extended time steps, low temporal information utilization, and the requirement for consistent time step between testing and training.
no code implementations • 23 Mar 2023 • Xiang He, Yang Li, Dongcheng Zhao, Qingqun Kong, Yi Zeng
The self-adaptation to membrane potential and input allows a timely adjustment of the threshold to fire spike faster and transmit more information.
1 code implementation • 23 Mar 2023 • Xiang He, Dongcheng Zhao, Yang Li, Guobin Shen, Qingqun Kong, Yi Zeng
In order to improve the generalization ability of SNNs on event-based datasets, we use static images to assist SNN training on event data.
no code implementations • 29 Jan 2023 • Guobin Shen, Dongcheng Zhao, Yi Zeng
Inspired by spike patterns in biological neurons, this paper introduces the dynamic Burst pattern and designs the Leaky Integrate and Fire or Burst (LIFB) neuron that can make a trade-off between short-time performance and dynamic temporal performance from the perspective of network information capacity.
no code implementations • 5 Jan 2023 • Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
To improve memory efficiency, we design a memory system to enable efficient synaptic weights and membrane voltage memory access with reasonable on-chip RAM consumption.
no code implementations • 18 Jul 2022 • Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang, Yuwei Wang, Yang Li, Xin Liu, Chengcheng Du, Qingqun Kong, Zizhe Ruan, Weida Bi
These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.
no code implementations • 6 Jul 2022 • Yiting Dong, Dongcheng Zhao, Yang Li, Yi Zeng
By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks.
no code implementations • 24 May 2022 • Guobin Shen, Dongcheng Zhao, Yi Zeng
Data augmentation can improve the quantity and quality of the original data by processing more representations from the original data.
no code implementations • 24 May 2022 • Jihang Wang, Dongcheng Zhao, Guobin Shen, Qian Zhang, Yi Zeng
Privacy protection is a crucial issue in machine learning algorithms, and the current privacy protection is combined with traditional artificial neural networks based on real values.
1 code implementation • 25 Dec 2021 • Yang Li, Yiting Dong, Dongcheng Zhao, Yi Zeng
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain.
no code implementations • 15 Nov 2021 • Dongcheng Zhao, Yang Li, Yi Zeng, Jihang Wang, Qian Zhang
Our Spiking CapsNet fully combines the strengthens of SNN and CapsNet, and shows strong robustness to noise and affine transformation.
no code implementations • 17 Oct 2021 • Guobin Shen, Dongcheng Zhao, Yi Zeng
Secondly, we propose a biologically plausible temporal adjustment making the error propagate across the spikes in the temporal dimension, which overcomes the problem of the temporal dependency within a single spike period of the traditional spiking neurons.
no code implementations • 27 May 2021 • Yang Li, Yi Zeng, Dongcheng Zhao
Also, when ResNet structure-based ANNs are converted, the information of output neurons is incomplete due to the rapid transmission of the shortcut path.
no code implementations • 27 May 2021 • Dongcheng Zhao, Yi Zeng, Yang Li
With the combination of the two mechanisms, we propose a deep spiking neural network with adaptive self-feedback and balanced excitatory and inhibitory neurons (BackEISNN).