3 papers
cs.LG2026
Flexible inference for animal learning rules using neural networks
Yuhan Helena Liu, Victor Geadah, Jonathan Pillow
Understanding how animals learn is a central challenge in neuroscience, with growing relevance to the development of animal- or human-aligned artificial intelligence. However, exis…
cs.NE2025
Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example
Yuhan Helena Liu, Guangyu Robert Yang, Christopher J. Cueva
Understanding how the brain learns may be informed by studying biologically plausible learning rules. These rules, often approximating gradient descent learning to respect biologic…
cs.NE2025
The Influence of Initial Connectivity on Biologically Plausible Learning
Weixuan Liu, Xinyue Zhang, Yuhan Helena Liu
Understanding how the brain learns can be advanced by investigating biologically plausible learning rules -- those that obey known biological constraints, such as locality, to serv…