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Found 18 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.
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.
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks
In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task).
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.
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks
The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture.
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.
Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks
The proposed DPAP model considers multiple biologically realistic mechanisms (such as dendritic spine dynamic plasticity, activity-dependent neural spiking trace, and local synaptic plasticity), with the addition of an adaptive pruning strategy, so that the network structure can be dynamically optimized during learning without any pre-training and retraining.
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.
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.
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.
