Joel Zylberberg

Joel Zylberberg is an Adjunct Assistant Professor at the University of California, Los Angeles (UCLA), affiliated with the Department of Ophthalmology within the School of Medicine. His research bridges Computational Neuroscience, Neural Networks, and Machine Learning, focusing on how neural activity and biological mechanisms inform artificial intelligence and visual cortex dynamics.

Joel's work explores retinal computation, population coding, and neural adaptation, often analyzing mouse visual cortex and neurophysiological data. His recent publications highlight trends in dynamic retinal processes, stimulus-driven network topology, and brain-inspired machine learning, emphasizing the interplay between biophysics and computational modeling.

Collaborators include Greg Field (UCLA), Richard Born (Harvard), and Michael DeWeese (UC Berkeley), with affiliations spanning institutions like University of Washington and University of California, San Diego (UCSD). His work appears in journals such as Nature Neuroscience, Neuron, and PLOS Computational Biology.