Dr Agnieszka Grabska-Barwińska

 

dr Agnieszka Grabska - Barwińska 👏
 
dr Agnieszka Grabska - Barwińska is Research Scientist at Google DeepMind. She owns a Ph.D. in Computational Neuroscience (IGSN, Ruhr University Bochum). Additionally, she received training in Experimental Neuroscience as well as in Machine Learning and Bayesian approaches to the brain (University College in London). In her work she swapped the goal of understanding intelligence in vivo for in silico, and is still immersed in analysing Artificial Intelligence.
During her lecture "How to train deep networks in the brain? A fresh take on "deep learning" inspired by the cerebellum." we'll learn about the Dendritic Gated Network (DGN), a variant of the Gated Linear Network (Veness et al., 2017), which offers a biologically plausible alternative to backpropagation, and how is it more efficient than conventional artificial networks.
 
📌Topic: How to train deep networks in the brain? A fresh take on "deep learning" inspired by the cerebellum.
 
👩‍🏫Abstract:
The dominant view in neuroscience is that changes in synaptic weights underlie learning. It is unclear, however, how the brain is able to determine which synapses should change, and by how much. This uncertainty stands in sharp contrast to deep learning, where changes in weights are explicitly engineered to optimize performance. However, the main tool for doing that, backpropagation, is not biologically plausible, and networks trained with this rule tend to forget old tasks when learning new ones. Here we introduce the Dendritic Gated Network (DGN), a variant of the Gated Linear Network (Veness et al., 2017), which offers a biologically plausible alternative to backpropagation. DGNs combine dendritic “gating” (whereby interneurons target dendrites to shape neuronal response) with local learning rules to yield provably efficient performance. They are significantly more data efficient than conventional artificial networks and are highly resistant to forgetting, and we show that they perform well on a variety of tasks, in some cases better than backpropagation. The DGN bears similarities to the cerebellum, where there is evidence for shaping of Purkinje cell responses by interneurons. It also makes several experimental predictions, one of which we validate with in vivo cerebellar imaging of mice performing a motor task.