Eduardo Sandoval

  • PhD student, Helen Wills Neuroscience Program

Current Research

I am co-mentored by Robert T. Knight in the Department of Psychology and Michael DeWeese in Redwood and the Department of Physics. In the DeWeese lab, I have implemented STDP rules in a spiking neural network(SAILnet) to allow us to use time-varying data as input to the model. I am currently investigating the spatiotemporal receptive field properties of the neurons in this network.

In the Knight lab, I analyze human intra-cranial data while patients in the hospital complete various tasks, currently looking at LFPs in Auditory Cortex and Insula during a music listening task, and looking at human single neural recordings in Medial Prefrontal Cortex during an interval timing task. In this venture, I look at high gamma activity for neural correlates of behavioral variables and am looking through single neuron data through the lens of distributional reinforcement learning. I hope to implement my findings and modify this theory to see if these models perform better on certain RL tasks.

Bio I arrived at Berkeley through a long time of exploring different fields of research at my undergrad university Johns Hopkins University. I have previously done work in ultra-fast endocytosis at hippocampal synapses in mouse cultured neurons in the Watanabe lab at the Johns Hopkins School of Medicine. Here I developed a sense for what was important for a cell to operate, and more over what was so special about neurons in their compartmentalization of cellular processes and connectivity patterns. I have also used image processing and computer vision techniques to assist the Global Health and Engineering lab in the Department of Biomedical Engineering at Hopkins to process a dataset of mosquito images for downstream convolutional neural network classification. I learned how to code and develop a love of programming here. In combination with experiments done in the Watanabe lab, I have also used the computer vision techniques to analyze fluorescence data and modeled it in the Liu Lab at the Center for Cell Dynamics at Johns Hopkins. Here, I developed physical intuitation and developed mass action models of phase separated closed membrane vesicles, and models of protein phase separation using advection-diffusion systems of equations. Finally, I spent two years following my undergrad career in the Honey Lab in the Department of Psychology at Hopkins. Here I used Javascript, SQL, and python to develop behavioral experiments and analyze their results. In addition, I started toying with recurrent neural networks during this time. Here our focus was on the hierarchy of temporal integration and separation in the human cortex. It was here that I developed a fundamental interest in memory and cognition/perception and in a temporal aspect to information processing. Combining all my experiences, I strive to be a computational neuroscientist that thinks about multiple scales of organization at once, from the importance of information processing across a single synapse, up to population level ideas about information processing and how cognition can arise from that, and moreover how this occurs from physical phenomena whether that be via physical constraints of neural architectures, or symmetry in the sensory environment that can be exploited by the brain.