UC San Diego

Human memory and learning represent the most complex and miraculous of human abilities and also the most difficult and major challenges of modern medicine. While treatments for diseases from heart to lung have rapidly advanced, basic mechanisms of brain function rooted in memory and learning remain completely unknown, reflected by the dearth of targeted treatments available for neuropsychiatric disorders.

This dissertation seeks to understand the neural processes of memory and learning at three interdependent levels of investigation --- systems of circuits, an individual circuit, and molecular modulation of circuits --- and how they can become disrupted in disease. To study these processes, I use artificial neural network models.

I first consider a system of circuits (Chapter 2), studying how regions of the brain coordinate to produce coherent and flexible behavior and how disruptions lead to specific clinical syndromes. I focus on the prefrontal cortex where distinct subregions have been characterized, and analyze how simple architectural alterations in circuit interdependencies can give rise to powerful memory and learning features characteristic of humans. I then study how disruptions affect this circuit system, revealing striking parallels to patients who have suffered specific prefrontal lesions.

To understand how an individual circuit learns and develops, I then focus on the internal process of strategy acquisition and evolution within a neural network (Chapter 3). To do this, I investigate a surprising experimental finding that humans and monkeys exhibit major behavioral discrepancies when solving a set of working memory tasks. I find that neural network models follow characteristic strategy progressions, exhibiting both monkey-like and human-like behavior at different stages. Furthermore, neural networks can mimic common problem solving heuristics while operating with unrelated underlying mechanisms. Counterintuitively, I find that some non-optimal heuristics can act as catalytic strategies that accelerate optimal learning.

These investigations raise the question of how highly structured networks of neurons, like brains, are able to store a multitude of versatile behaviors. In Chapter 4, I investigate how a critical class of regulatory molecules in the brain called neuromodulators alter network properties to enable multiplexing of neural circuits. I reveal how neuromodulators may underlie idiosyncratic circuit dose-response properties, suggesting an alternative explanation for the large variability of neuropsychiatric drug sensitivities observed in patients.

Through these three levels of investigation, I aim to contribute to our understanding of how systems, circuits, and molecules help store and manipulate forms of neural memory to generate the complex and dynamic behaviors characteristic of humans and animals. My findings suggest how these processes relate to disease manifestations and begin to suggest possible avenues toward the ultimate goal: treatments for patients.