UC San Diego

The brain is the most complex part of human body. It is the source of intelligence and human behavior. Understanding how the brain works is essential for treatment of neurological disorders and development of more powerful artificial intelligence algorithms. To investigate its function, novel neurotechnologies are needed to record large-scale neural activity at high spatiotemporal resolution. Besides that, computational models could also be built to investigate possible mechanisms for neural activities at different levels. This dissertation presents research work on novel neurotechnologies and computational frameworks based on machine learning and biophysical modeling for investigation of brain functions. Chapter 1 presents a flexible insertable transparent microelectrode array (Neuro-FITM) for simultaneous electrical recordings from hippocampus and wide-field calcium imaging of dorsal cortex. We successfully recorded simultaneous activity of large-scale cortex and hippocampus. Using two-stage tensor component analysis and decoding analysis with support vector machines, we demonstrated distinct associations between hippocampus and cortex during hippocampal sharp-wave ripples. Chapter 2 presents a compact closed-loop optogenetics system based on transparent graphene microelectrode array. We extensively investigated light-induced artifacts for graphene electrodes and gold electrodes. We also validated the system for different frequencies of interest for neural recordings. The developed system can be used for various applications involving optogenetic stimulation and electrophysiological recordings. Chapter 3 presents the decoding of cortex-wide brain activity using surface electrical recordings over the cortex. We performed simultaneous local electrical recording and wide-field calcium imaging in awake mice. Using recurrent neural network, we demonstrated successful decoding of pixel-level cortical activity using locally recorded surface potentials. These results show that locally recorded surface potentials indeed contain rich information of large-scale neural activities. Chapter 4 presents a biophysical model for coupled network of hippocampal CA1 and prefrontal cortex to study mechanisms of memory trace transfer and reactivation. We found that stored memory traces in cortex can be retrieved through two different mechanisms, namely cell-specific inputs and non-specific spontaneous activities. Our study presents mechanistic explanations for memory transfer and retrieval. Chapter 5 is the conclusion of this dissertation. The future directions of neurotechnologies and computational models for neuroscience research are discussed