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Found 6 papers, 1 papers with code
Embedded Silicon-Organic Integrated Neuromorphic System
The development of artificial intelligence (AI) and robotics are both based on the tenet of "science and technology are people-oriented", and both need to achieve efficient communication with the human brain.
The Brain-Inspired Decoder for Natural Visual Image Reconstruction
In this paper, we propose a deep learning neural network architecture with biological properties to reconstruct visual image from spike trains.
An Adaptive Contrastive Learning Model for Spike Sorting
But for BCIs used in neuroscience research, it is important to separate out the activity of individual neurons.
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation
To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task.
An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence
This review introduces the following sections, the biological background of spiking neurons and the theoretical basis, different neuronal models, the connectivity of neural circuits, the mainstream neural network learning mechanisms and network architectures, etc.
A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data
However, obtaining sufficient training data is difficult as well as limited.
