Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning

Visual Processing and Latent Representations in Biological and Artificial Neural Networks

The human visual system performs the impressive task of converting light arriving at the retina into a useful representation that allows us to make sense of the visual environment. We can navigate easily in the three-dimensional world and recognize objects and their properties, even if they appear from different angles and under different lighting conditions. Artificial systems can also perform well on a variety of complex visual tasks. While they may not be as robust and versatile as their biological counterpart, they have surprising capabilities that are rapidly improving. Studying the two types of systems can help us understand what computations enable the transformation of low-level sensory data into an abstract representation. To this end, this dissertation follows three different pathways. First, we analyze aspects of human perception. The focus is on the perception in the peripheral visual field and the relation to texture perception. Our work builds on a texture model that is based on the features of a deep neural network. We start by expanding the model to the temporal domain to capture dynamic textures such as flames or water. Next, we use psychophysical methods to investigate quantitatively whether humans can distinguish natural textures from samples that were generated by a texture model. Finally, we study images that cover the entire visual field and test whether matching the local summary statistics can produce metameric images independent of the image content. Second, we compare the visual perception of humans and machines. We conduct three case studies that focus on the capabilities of artificial neural networks and the potential occurrence of biological phenomena in machine vision. We find that comparative studies are not always straightforward and propose a checklist on how to improve the robustness of the conclusions that we draw from such studies. Third, we address a fundamental discrepancy between human and machine vision. One major strength of biological vision is its robustness to changes in the appearance of image content. For example, for unusual scenarios, such as a cow on a beach, the recognition performance of humans remains high. This ability is lacking in many artificial systems. We discuss on a conceptual level how to robustly disentangle attributes that are correlated during training, and test this on a number of datasets

Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop