Brain-inspired randomness makes artificial vision more reliable
Humans are usually remarkably good at recognizing objects, even under poor conditions. Artificial vision systems, by contrast, reach their limits more quickly. Researchers at the Hertie Institute for Clinical Brain Research have now investigated what artificial vision systems can learn from the brain. They found that combining two brain-inspired features can make image recognition considerably more reliable. The research findings are published in the journal Patterns.
Catastrophic forgetting, an innate issue with backpropagation learning algorithms, is a challenging problem in artificial and spiking neural network (ANN and SNN) research.
A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously carried out by artificial systems. By integrating cultured neuronal networks into a machine learning framework, the team showed that these biological systems…
Researchers at FORTH have developed a new type of artificial neural network (ANN) that incorporates features of biological dendrites. This innovative design allows for accurate and robust image recognition while using significantly fewer parameters, paving the way for more compact and energy-efficient AI systems.