Our latest project find shared representations while controlling for confounds is out www.biorxiv.org/content/10.1... Check @s-michelmann.bsky.social 's thread for the executive summary. Code in python and matlab: github.com/s-michelmann... — Now is play time 👨‍💻

🧠 Paper out!

We investigated how hippocampal and cortical ripples support memory during movie watching. We found that:

🎬 Hippocampal ripples mark event boundaries
🧩 Cortical ripples predict later recall

Ripples may help transform real-life experiences into lasting memories!

rdcu.be/eui9l

Excited to share this project specifying a research direction I think will be particularly fruitful for theory-driven cognitive science that aims to explain natural behavior!

We're calling this direction "Naturalistic Computational Cognitive Science"

7/ In the era of limited funding, our work showcases how to use models to bridge neural and natural behavior data to (1) increase discrimination power over neural models, (2) improve behavioral predictions, and (3) reveal novel bioplausible algorithms for neural computation in natural settings.

6/ Finally, we studied the nonlinear accumulation model of song encoding more closely, revealing previously unknown song patterns driving female slowing, and a neural algorithm for encoding long input sequences that leverages nonlinear adaptation to remember fine temporal patterns for long periods.

5/ Methodologically, our work shows how natural behavior can refine predictions of how neural data generalize beyond their original experimental context AND that modeling hidden neural activity can improve pure natural behavior predictions, even relative to popular black-box deep networks.

4/ This suggests that linear-nonlinear feature detection is not enough, but rather that flies may encode long communication sequences via nonlinear accumulation along multiple dimensions of activity space in a heterogeneous neural population code for song history.

3/ We found that one encoding model, based on multi-dimensional, nonlinear accumulation, allowed us to predict female locomotion much better than a classic linear-nonlinear feature-detection model, also outperforming many other predictors, including several black-box artificial neural networks.

2/ To gain further model discrimination power, we turned to a separate pure-behavior dataset of naturalistic fly courtship. We simulated the female’s neural responses to the male’s song using the encoding models then tried to predict her locomotion from the simulated neural data.

1/ How is the male fruit fly’s complex courtship song encoded in the female fly brain? Calcium imaging of responses to simplified song stimuli suggest neural codes are spread across a population with heterogeneous selectivities and timescales, but multiple encoding models fit the data equally well.

How do we get more neuroscience out of our behavioral data? Excited to share new work with C.A.Baker, M.Murthy and @jpillowtime.bsky.social, where we use natural behavior data to extend predictions from neural recordings about population codes for dynamic social stimuli: tinyurl.com/2d3wwfyf

An intuitive way to derive Shannon's famous entropy formula that you may not have seen before (unless you're a physicist): rkp.science/an-alternati...

🧠🤖 Computational Neuroscience summer school IMBIZO in Cape Town is open for applications again!
 
💻🧬 3 weeks of intense coursework & projects with support from expert tutors and faculty
 
📈Apply until July 1st!

🔗https://imbizo.africa/