On October 8, the Nobel Prize in Physics 2024 was awarded to John J. Hopfield and Geoffrey E. Hinton "for foundational discoveries and inventions that enable machine learning with artificial neural networks".*
This year’s Nobel Laureates didn’t achieve their breakthroughs overnight. Below you can read, share, and download a selection of their research that’s published across Springer Nature’s journals and books. Read the story of this year’s Laureates in Physics.
| “Neural” computation of decisions in optimization problems. Published in: Biological Cybernetics | Modeling the olfactory bulb and its neural oscillatory processings. In Published: Biological Cybernetics | On theorists and data in computational neuroscience. Published in: Nature Neuroscience | |
Pattern recognition computation using action potential timing for stimulus representation. Published in: Nature |
| From molecular to modular cell biology. Published in: Nature |
| Collective Computation, Content-Adressable Memory, and Optimization Problems. In: Complexity in Information Theory | Biological Information Processing and Molecular Nanocomputing. In: From Neural Networks and Biomolecular Engineering to Bioelectronics | Brain, Neural Networks, and Computation. In: More Things in Heaven and Earth |
| Collective Computation With Continuous Variables. In: Disordered Systems and Biological Organization | Matching Neural Models to Experiment. In: Computation and Neural Systems | The Logic of Limax Learning. In: Model Neural Networks and Behavior |
| Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. Published in: Journal of Signal Processing Systems | Coaching variables for regression and classification. In: Statistics and Computing | Visualizing non-metric similarities in multiple maps. In: Machine Learning |
Computation by neural networks. Published in: Nature | Guest Editorial: Deep Learning. Published in: International Journal of Computer Vision | Learning representations by back-propagating errors. Published in: Nature |
| Backpropagation and the brain. Published in: Nature Reviews Neuroscience | Self-organizing neural network that discovers surfaces in random-dot stereograms. Published in: Nature | Deep learning. Published in: Nature |
NASA Neural Articulated Shape Approximation. In: Computer Vision – ECCV 2020 | A Practical Guide to Training Restricted Boltzmann Machines. In: Neural Networks: Tricks of the Trade | Transforming Auto-Encoders. In: Artificial Neural Networks and Machine Learning – ICANN 2011 |
Learning to Detect Roads in High-Resolution Aerial Images. In: Computer Vision – ECCV 2010 | Analysis-by-Synthesis by Learning to Invert Generative Black Boxes. In: Artificial Neural Networks - ICANN 2008 | A New Learning Algorithm for Mean Field Boltzmann Machines. In: Artificial Neural Networks — ICANN 2002 |
Keeping Neural Networks Simple. In: ICANN ’93 | Learning translation invariant recognition in a massively parallel networks. In: PARLE Parallel Architectures and Languages Europe | Boltzmann Machines. In: Encyclopedia of Machine Learning and Data Mining |
| Deep Belief Nets. In: Encyclopedia of Machine Learning and Data Mining |
