**Large-Scale Neural Network Models for Neuroscience**

**Autumn 2018, Stanford University**

Time:Mon./Wed. 1:30p - 2:50p

Time:

**Location:**Gates (Computer Science) B12

**Staff:**Daniel Yamins (x@stanford.edu where x=yamins) & Chengxu Zhuang (x=chengxuz)

**Repo:**http://github.com/neuroailab/cs375

This class will serve as an introduction to designing, building, and training large-scale neural networks for modeling brain and behavioral data, including: deep convolutional neural network models of sensory systems (vision and audition); recurrent neural networks for dynamics, memory and attention; integration of variational and generative methods for cognitive modeling; and methods and metrics for comparing such models to real-world neural data. Attention will be given both to established methods as well as cutting-edge techniques. Students will investigate conceptual bases for deep neural network models, while learning to implement and train large-scale models in Tensorflow using GPUs.