Neurotechnology. Image recognition and modeling the response of neurons in the inferotemporal cortex
Date:
In this talk I show how convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs) can be used to study latent representations, coded by neurons in high visual areas. To explain neural preferences, I train a deep network to simulate the response of the neurons in the Inferior temporal (IT) cortex of macaque monkey. The model has high performance and explain > 0.65% of the variance in the neural data. I visualize latent representations of artificial neurons using a generative adversarial network. The approach allows to find an input signal that maximizes activation of an individual unit without limitations introduced by a dataset.