I'll be recruiting grad students and postdocs, so please reach out if interested!
More details here: sites.google.com/view/marcust...
@uclaneurotheme.bsky.social
@dgsomucla.bsky.social
I’m excited to share that I’ll be starting my computational neurosci & machine learning lab at UCLA this July! ☀️
We’ll be working on computational methods for high-throughput neural data analysis, optical interrogation of neural circuits, & mechanistic models of artificial+bio neural systems. ⤵️
Where, exactly, does learning happen in the brain?
Out today in @nature.com, we identify a synaptic locus of birdsong learning and show that the circuit can be tuned to make birds learn faster - but at a cost. Read on👇 #neuroskyence 🧪 #prattle 💬 #bioacoustics
Shareable link: rdcu.be/fiyrS
Very happy for this to finally be published! We developed new machine learning methods for scalable mapping of synaptic connectivity using holographic optogenetics and compressed sensing.
www.nature.com/articles/s41...
Our new paper in @natcomms.nature.com introduces improv, a flexible software platform that integrates models with experiments in real-time. Traditional experiments collect all data first, then analyze it later. With improv, models analyze data as it streams in and actively guide what to do next.
Marcus A. Triplett
Marcus A. Triplett
Marcus A. Triplett
Drew Schreiner
Anne Draelos
A pair of papers on using holographic optogenetics and compressed sensing for connectomics:
Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing
www.nature.com/articles/s41...
The authors develop a new computational system for high-throughput mapping of synaptic connectivity using two-photon holographic optogenetics and intracellular recordings.
Positions available
Multiple research positions are available in the Triplett lab at UCLA focused on statistical machine learning methods for neuroscience and mechanistic models of neural computation...
