Object Recognition

May 9, 2023 ... The article examines the role of the spontaneous activity of neurons, and therefore, of brain neural networks, in information processing. Dec 19, 2019 ... Abstract: Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. Aug 30, 2023 ... To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of 'Pong' to ... We then perform information le- sions of individual neurons to determine their contribution to each task. Following that, we investigate the ability of two. Apr 28, 2021 ... First, the Information Bottleneck Principle is not the same as bottleneck layers in networks like auto-encoders. These can serve as information ... The brain is a deep and complex recurrent neural network. The models of information processing that have dominated computational neuroscience, by contrast, are ... a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process. In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and ... Data-driven network performance prediction for B5G networks: a graph neural network approach. Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven ... ... neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.