DynaVision - Representational Dynamics in Vision

点击翻译按钮获取中文摘要

One of the central challenges of brain science is to understand the cortical mechanisms that underlie our ability to extract meaning from the rich visual information in the world around us. In recent years, the application of multivariate pattern recognition techniques, borrowed from machine learning, has led to unprecedented insights into how visual information is represented and transformed in the cortical network. To assess the fine-grained structures of cortical representations, most previous human studies rely on functional magnetic resonance imaging, which offers relatively high spatial, but poor temporal resolution. Much of our knowledge about visual representations, therefore, rests on temporal averages of cortical activity, neglecting encoding schemes that operate on shorter time scales.In parallel to experimental advances, computational neuroscience and computer vision have taken a complementary route, focusing instead on artificial vision systems. Inspired by neuroscience, recent deep neural network models (DNNs) enabled great strides at basic object recognition tasks, reaching unprecedented accuracies that approach human performance levels. Interestingly, the internal representations of the most successful feedforward models exhibit striking similarities to cortical representations. These comparisons are, however, again based on temporally averaged data. It therefore remains unclear in how far DNNs can account for dynamically changing cortical signals.Both, experimental and computational studies of visual representations have focussed on spatial activity patterns and feedforward computations, leaving the temporal dynamics of visual processing poorly understood. Here I propose a research program to advance our knowledge of the potentially rapid representational changes in the brain. I will develop novel multivariate analysis and statistical inference techniques, which can reveal the sequence of cortical encoding stages and their underlying computational mechanisms. Applying these methods to magnetoencephalography data, recorded at high temporal resolution, I will furthermore investigate whether the brain uses multiplexing by frequency to concurrently maintain and transfer different representations. Complementing these analyses, I will use state-of-the-art DNNs to build models of increasing dynamic complexity to determine whether the computational mechanisms and dynamics of human cortical visual processing are best accounted for by a feedforward or a recurrent deep neural network model.In summary, the proposed research project will provide important insights into the mechanisms underlying the dynamics of visual representation in the brain, based on novel computational models and data analysis techniques. This novel set of tools will furthermore enable researchers throughout systems neuroscience to probe the representational dynamics of biological brains and to test the agreement between computational models and experimental data.