Hi, I'm Jocelyn

Evolution has guided the brain to be able to quickly process a near constant influx of input stimuli, allowing for biological agents to effectively understand and interact with their environment in order to survive. Biological intelligence displays features of robustness, generalizability, and efficiency, fundamental to its ability to construct a coherent representation of the world in which it resides. These key features of biological intelligence are essential for advanced artificially intelligent systems to exhibit but have presented a challenge to replicate. The capabilities of machine intelligence are still far from those of the brain; however, fundamental principles of biological intelligence can serve as a muse for how these characteristics of robustness and adaptability may be achieved.

This thesis centers around the synergistic relationship between machine intelligence and neuroscience: how artificially intelligent systems can be improved by borrowing features from the brain and how machine learning methods can be applied to advance neuroscience knowledge. In pursuit of studying the first side of this bidirectional relationship, we describe neuroscience inspired machine learning systems that include features present in the brain, such as sparsity, competition, feedback, and spiking communication. These neuroscience inspired systems display an inherent robustness, generalizability, and efficiency; fundamental characteristics of biological intelligence that are desirable, but remain out of reach, for artificially intelligent systems. On the other side of this mutually beneficial relationship, we explore how machine learning methods can be employed to advance neuroscience knowledge. We show how neuromorphic chips and algorithms can be used to effectively model biological neural responses. The work proposed here is largely focused on how biologically plausible algorithms can be used, both in modeling and in signal processing efforts, to further our understanding of the brain at multiple levels. This work has the potential to improve our understanding of biological intelligence, providing further guidance towards building robust, generalizable, and truly intelligent machines.