Sir Henry Dale Fellow
I study principles of learning in the brain and mind. The interactions of billions of neurons ultimately give rise to our thoughts and actions. Remarkably, much of our behavior is learned starting in infancy and continuing throughout our lifespan. Understanding how learning at the level of behavior is linked to changes at the level of neurons and synapses is a key challenge for theory.
My lab aims to develop a mathematical toolkit suitable for analyzing and describing learning in complex multi-regional brain networks. Our current focus is on the theory of deep learning, a class of artificial neural network models that take inspiration from the brain.
Alongside this theoretical work, we develop close collaborations with experimentalists to test principles of learning in biological organisms.
A deep learning framework for neuroscience
SAXE A. and BOGACZ R., (2019), Nature Neuroscience
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Saxe AM. et al, (2019)
A mathematical theory of semantic development in deep neural networks.
Saxe AM. et al, (2019), Proc Natl Acad Sci U S A, 116, 11537 - 11546
Tensor Switching Networks
Tsai C-Y. et al, (2016), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 29
Acquisition of decision making criteria: reward rate ultimately beats accuracy.
Balci F. et al, (2011), Atten Percept Psychophys, 73, 640 - 657