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Title: From mind to machine: neural circuits, learning algorithms, and beyond
Authors: Yang, Runzhe
Advisors: SeungNarasimhan, SebastianKarthik
Contributors: Computer Science Department
Subjects: Artificial intelligence
Issue Date: 2023
Publisher: Princeton, NJ : Princeton University
Abstract: This thesis explores diverse topics within computational neuroscience and machine learning. The work begins by examining the organization of biological neuronal circuits reconstructed by electron microscopy. First, our study of neural connectivity patterns in the mouse primary visual cortex illustrates the necessity for refined understanding of the non-random features of cortical connections, challenging conventional perspectives. Second, in the larval zebrafish hindbrain, our novel discovery highlights overrepresented three-cycles of neuron, an observation unprecedented in electron microscopy-reconstructed neuronal wiring diagrams. Additionally, I present an exhaustive compilation of motif statistics and network characteristics for the complete adult Drosophila brain. These efforts collectively enrich our understanding of the intricate wiring diagram of neurons, offering new insights into the organizational principles of biological brains. In the second part of the thesis, I introduce three distinct machine learning algorithms. The first algorithm, a biologically plausible unsupervised learning algorithm, is implemented within artificial neural networks using Hebbian feedforward and anti-Hebbian lateral connections. The theoretical discourse explores the duality and convergence of the learning process, connecting with the generalized concept of the "correlation game" principle. The second algorithm presents a novel multi-objective reinforcement learning approach, adept at managing real-world scenarios where multiple potentially conflicting criteria must be optimized without predefined importance weighting. This innovation allows the trained neural network model to generate policies that align optimally with user-specified preferences across the entire space of preference. The third algorithm employs a cognitive science-inspired learning principle for dialog systems. The designed system engages in negotiation with others, skillfully inferring the intent of the other party and predicting how its responses may influence the opponent's mental state. Collectively, these contributions shed light on the complexities of neural circuit organization and offer new methodologies in machine learning. By examining intelligence from both biological and computational perspectives, the thesis presents insights and reference points for future research, contributing to our growing understanding of intelligence.
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Computer Science

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