From the 1 of 7 linked papers with an AI index.
7 papers
Teaching signal synchronization in deep neural networks with prospective neurons
Nicolas Zucchet, Qianqian Feng, Axel Laborieux +3
The paper proposes adding an adaptive current to neurons so they can predict future inputs, allowing teaching signals to stay synchronized with slowly integrating, hierarchically o…
Understanding Self-Supervised Learning via Latent Distribution Matching
Fabian A Mikulasch, Friedemann Zenke
Self-supervised learning (SSL) excels at finding general-purpose latent representations from complex data, yet lacks a unifying theoretical framework that explains the diverse exis…
Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction
Michael Hauri, Friedemann Zenke
Model-based reinforcement learning (MBRL) agents operating in high-dimensional observation spaces, such as Dreamer, rely on learning abstract representations for effective planning…
Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks
Julia Gygax, Friedemann Zenke
Training spiking neural networks to approximate universal functions is essential for studying information processing in the brain and for neuromorphic computing. Yet the binary nat…
Theories of synaptic memory consolidation and intelligent plasticity for continual learning
Friedemann Zenke, Axel Laborieux
Humans and animals learn throughout life. Such continual learning is crucial for intelligence. In this chapter, we examine the pivotal role plasticity mechanisms with complex inter…
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
Tengjun Liu, Julia Gygax, Julian Rossbroich +3
Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection…