Search Results
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
We find that performance degradation in the converted SNN stems from using "hard reset" spiking neuron that is driven to fixed reset potential once its membrane potential exceeds the firing threshold, leading to information loss during SNN inference.
BindsNET: A machine learning-oriented spiking neural networks library in Python
In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared towards machine learning and reinforcement learning.
Training Spiking Neural Networks Using Lessons From Deep Learning
This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks.
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
As a result, their performance lags behind modern deep learning, and we are yet to see the effectiveness of SNNs in language generation.
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron
Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient.
Surrogate Gradient Learning in Spiking Neural Networks
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing.
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).
Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation
The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli.
Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network
Based on this residual design, we develop Spikingformer, a pure transformer-based spiking neural network.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
Previous studies in image classification domain demonstrated that standard NNs (with ReLU nonlinearity) trained using supervised learning can be converted to SNNs with negligible deterioration in performance.
