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Found 24 papers, 3 papers with code
Brain-inspired and Self-based Artificial Intelligence
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
Efficient Training Spiking Neural Networks with Parallel Spiking Unit
Efficient parallel computing has become a pivotal element in advancing artificial intelligence.
TIM: An Efficient Temporal Interaction Module for Spiking Transformer
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.
Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling
Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism.
Is Conventional SNN Really Efficient? A Perspective from Network Quantization
This paper introduces a unified perspective, illustrating that the time steps in SNNs and quantized bit-widths of activation values present analogous representations.
FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator
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.
Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
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.
Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency
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.
Dive into the Power of Neuronal Heterogeneity
The biological neural network is a vast and diverse structure with high neural heterogeneity.
Spiking Generative Adversarial Network with Attention Scoring Decoding
As it stands, such models are primarily limited to the domain of artificial neural networks.
Temporal Knowledge Sharing enable Spiking Neural Network Learning from Past and Future
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.
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks
The self-adaptation to membrane potential and input allows a timely adjustment of the threshold to fire spike faster and transmit more information.
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain
In order to improve the generalization ability of SNNs on event-based datasets, we use static images to assist SNN training on event data.
Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns
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.
FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks with Efficient DSP and Memory Optimization
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.
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
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.
An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections
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.
EventMix: An Efficient Augmentation Strategy for Event-Based Data
Data augmentation can improve the quantity and quality of the original data by processing more representations from the original data.
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling
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.
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain.
Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules
Our Spiking CapsNet fully combines the strengthens of SNN and CapsNet, and shows strong robustness to noise and affine transformation.
Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks
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.
BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons
Also, when ResNet structure-based ANNs are converted, the information of output neurons is incomplete due to the rapid transmission of the shortcut path.
BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons
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).
