Beyond the Patterns 34 - Amy Kuceyeski - Biological and Artificial Neural Networks

It’s a great pleasure to welcome Prof. Amy Kuceyeski at our lab for an invited presentation! Abstract: The recent explosion of machine learning literature has centered largely around Artificial Neural Networks (ANNs). These networks, originally inspired by biological neural networks – specifically, how the human brain processes visual information (Rosenblatt et al., 1958) – have proved remarkably useful for classification or regression problems of many types. Meanwhile, in the field of neuroscience, researchers have incorporated ANNs into “encoding models” that predict neural responses to visual stimuli and, furthermore, have been shown to reflect structure and function of the visual processing pathway.  This observation has led to speculation that primate ventral visual stream may have evolved to be an optimal system for object recognition/detection in the same way that ANNs are identifying optimal computational architectures. Here, we introduce NeuroGen, a novel encoding/generative model architecture desig