Eduardo Sandoval

  • PhD student, Helen Wills Neuroscience Program

esandoval@berkeley.edu

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. My current project involves developing a causal model of sparse coding for auditory nerve fibers. My current research interests in the DeWeese all involve some flavor of time, whether it be prediction of time series, or dynamical systems of the mind or brain.

In the Knight lab, I analyze human intra-cranial data while patients in the hospital complete various tasks. I look at both intracranial LFP, microLFP, and single unit data. Currently interested in gamma, theta, and beta oscillations. My immediate projects are to compare neural correlates of behavior across iEEG(5 mm), microLFP(5 um), and single unit data within the anterior cingulate cortex, orbitofrontal cortex, and hippocampus during the Wisconsin Card Sorting Task and the Interval Timing Task.

Bio

My past research has involved:

  1. Studying ultra-fast endocytosis in excitatory in mouse hippocampal cultures in the Watanabe lab at Johns Hopkins School of Medicine.
  2. Development of image preprocessing and computer vision pipelines for downstream convolutional neural network classification of mosquitos on behalf of VecTech LLC in collaboration with the Global Health and Engineering lab in the Department of Biomedical Engineering at Johns Hopkins University.
  3. Use of computer vision to analyze FRAP (fluorescence recovery after photobleaching) data as well as Lagrangian mechanics to develop mass action models of phase separated closed membrane vesicles and protein phase separation. Done in the Liu Lab at the Center for Cell Dynamics at Johns Hopkins School of Medicine.
  4. Development of behavioral experiments investigating temporal integration and separation and exploration of recurrent neural networks in the Honey lab at the Department of Psychological and Brain Sciences at Johns Hopkins University.

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. In my free time, I like to dance salsa, cook, garden, and play with electronics.