A5034146158- Neural dynamics and brain function
- Neuroscience and Neuropharmacology Research
- Visual perception and processing mechanisms
- Advanced Neuroimaging Techniques and Applications
- Advanced Memory and Neural Computing
- Blind Source Separation Techniques
- Memory and Neural Mechanisms
- Photoreceptor and optogenetics research
- Random Matrices and Applications
- Quantum Computing Algorithms and Architecture
- Functional Brain Connectivity Studies
- Statistical Methods and Inference
- Statistical Mechanics and Entropy
- Vestibular and auditory disorders
- Neurobiology and Insect Physiology Research
- Sleep and Wakefulness Research
- Marine and coastal ecosystems
- Quantum optics and atomic interactions
- Microbial Community Ecology and Physiology
- Neural Networks and Applications
- Wastewater Treatment and Nitrogen Removal
Stanford University
2019-2024
University of Southern California
2018-2022
Abstract Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related behaviors. Well-known experimental results have revealed specialized cell types in MEC — e.g. grid, border, head-direction cells whose highly stereotypical response profiles are suggestive the role they might play supporting functionality. However, majority neurons do not exhibit firing patterns. How should these more “heterogeneous” be described, how contribute to behavior? In this work, we took...
Abstract The discovery of entorhinal grid cells has generated considerable interest in how and why hexagonal firing fields might mechanistically emerge a generic manner from neural circuits, what their computational significance be. Here we forge an intimate link between the problem path-integration existence grids, by demonstrating that such grids arise generically biologically plausible networks trained to path integrate. Moreover, develop unifying theory for are so ubiquitous...
In order to record the stream of autobiographical information that defines our unique personal history, brains must form durable memories from single brief exposures patterned stimuli impinge on them continuously throughout life. However, little is known about computational strategies or neural mechanisms underlie brain's ability perform this type "online" learning. Based increasing evidence dendrites act as both signaling and learning units in brain, we developed an analytical model relates...
Across many disciplines spanning from neuroscience and genomics to machine learning, atmospheric science, finance, the problems of denoising large data matrices recover hidden signals obscured by noise, estimating structure these signals, is fundamental importance. A key solving lies in understanding how singular value a signal deformed noise. This question has been thoroughly studied well-known spiked matrix model, which originate low-rank perturbed additive noise matrices, an asymptotic...
Abstract Recent work has claimed that the emergence of grid cells from trained path-integrator circuits is a more fragile phenomenon than previously reported. In this note we critically assess main analysis and simulation results underlying claim, within proper context published theoretical work. Our assessment reveals entirely consistent with prior theory: hexagonal robustly emerge precisely when theory predicts they should, don’t should not.
An important and understudied area in microbial ecology concerns the very large number of rare species within an environment. Study such based on non-specific sequencing environmental samples is practically difficult simply due to their rarity. A previous report demonstrated that pretreating with photoactive dye PMA before subsequent PCR can increase sensitivity species. Here we propose a mathematical model equilibrium chemical thermodynamics explains this effect suggests simple quantitative...
Abstract Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study problem faced by a hypothetical boundary cell, and to predict cortical circuitry could produce cell from population of conventional “simple cells”, we labeled 30,000 natural image patches used Bayes’ rule help determine simple should influence nearby depending on its relative offset receptive field position orientation. We identified three basic types...
Across many disciplines spanning from neuroscience and genomics to machine learning, atmospheric science, finance, the problems of denoising large data matrices recover hidden signals obscured by noise, estimating structure these signals, is fundamental importance. A key solving lies in understanding how singular value a signal deformed noise. This question has been thoroughly studied well-known spiked matrix model, which originate low-rank perturbed additive noise matrices, an asymptotic...
Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study problem faced by a hypothetical boundary cell, and to predict cortical circuitry could produce cell from population of conventional “simple cells,” we labeled 30,000 natural image patches used Bayes' rule help determine simple should influence nearby depending on its relative offset receptive field position orientation. We identified following three basic types...