A5010755100- Circadian rhythm and melatonin
- Neurobiology and Insect Physiology Research
- Photoreceptor and optogenetics research
- Fractal and DNA sequence analysis
- Neural dynamics and brain function
- Machine Learning in Bioinformatics
- Animal Behavior and Reproduction
- Insect and Arachnid Ecology and Behavior
- Time Series Analysis and Forecasting
- Animal Vocal Communication and Behavior
- Blind Source Separation Techniques
McGovern Institute for Brain Research
2018-2019
Massachusetts Institute of Technology
2019
University of Michigan–Ann Arbor
2014-2018
Michigan United
2014
A sensitivity of the circadian clock to light/dark cycles ensures that biological rhythms maintain optimal phase relationships with external day. In animals, neuron network (CCNN) driving sleep/activity receives light input from multiple photoreceptors, but how these photoreceptors modulate CCNN components is not well understood. Here we show Hofbauer-Buchner eyelets differentially two classes ventral lateral neurons (LNvs) within <i>Drosophila</i> CCNN. The antagonize Cryptochrome (CRY)-...
Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be salient feature of dynamics, but not succinctly captured by traditional dimensionality reduction techniques. Here, we software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional data, testing the significance these extracted patterns, and assessing...
Synchronized neuronal activity is vital for complex processes like behavior. Circadian pacemaker neurons offer an unusual opportunity to study synchrony as their molecular clocks oscillate in phase over extended timeframe (24 h). To identify where, when, and how synchronizing signals are perceived, we first studied the minimal clock neural circuit Drosophila larvae, manipulating either four master (LNvs) or two dorsal (DN1s). Unexpectedly, found that PDF Receptor (PdfR) required both LNvs...
Abstract Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be salient feature of dynamics, but not succinctly captured by traditional dimensionality reduction techniques. Here we software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional data, testing the significance these extracted patterns, and assessing...