Marcus A. Triplett

@marcustriplett.bsky.social

Computational neuroscience & machine learning Assistant prof @ UCLA

How can AI change theoretical neuroscience? Technologies separate us from the mechanics of computation. Are we liberated to focus on the big picture or abandoning the substrate of thought? Born from an ongoing experiment in my lab about balancing these technologies for research and training

The Transmitter @thetransmitter.bsky.social · 2mo ago

Agentic coding makes it possible to specify a neuroscience model in hours instead of months, writes @briandepasquale.bsky.social. The field risks becoming prolific but shallow—generating models faster than we can generate insights. #neuroskyence www.thetransmitter.org/the-big-pict...

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

A synaptic locus of song learning - Nature

Combining a computational framework and optogenetic and chemogenetic manipulations within and downstream of the cortico-basal ganglia circuit identifies the specific cortico-basal ganglia synapse...

nature.com

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. ⤵️

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.