A biologically plausible algorithm for learning local predictive directions in nonlinear dynamical systems. Applied to natural videos, it learns both temporal and spatial filters that resemble those of retinal neurons. Cosyne poster 2-109, presented by Marco Zenari

Self-supervised learning from natural dynamical stimuli yields a circuit that resembles the Drosophila motion detector, reproducing both its directional selectivity and characteristic synaptic weight structure. By Abdelrahman Sharafeldin and Erik Schomburg at Cosyne poster 2-137

A more biologically plausible controller neuron model: ReLU emerges as the optimal solution to a reach–avoid, rather than stabilization, objective, solved with a multi-step policy in a potentially stochastic setting. Poster 1-142 at Cosyne presented by Abel Sagodi, happening now

#FlatironCCN researchers drew on lessons from neurobiology to enhance artificial systems using a new type of computational component that is more akin to real brains: https://www.simonsfoundation.org/biological-brains-inspire-a-new-building-block-for-artificial-neural-networks/ #science

ReSU: A novel biologically inspired computational primitive for dynamical data. Now at AAAI board 489

Why do we stop smelling odors that linger? In our new @prxlife.bsky.social paper with @pfrancois.bsky.social, @gautamreddy.bsky.social, and Massimo Vergassola, we develop a manifold learning theory of this olfactory habituation process in fluctuating environments. 🔗 doi.org/10.1103/q62z... 🧵🔽

Joint junior faculty position in Computational Neuroscience, between Ctr for Computational Neuroscience at @flatironinstitute.org and the CUNY Graduate Center @thegraduatecenter.bsky.social . Application deadline: 16 Jan 2026! www.simonsfoundation.org/flatiron/car... cuny.jobs/new-york-ny/...

Move over ReLU 🚀 Meet **ReSU** (Rectified Spectral Unit): a biologically inspired, self-supervised unit for learning from dynamical data. A backprop-free multilayer ReSU network learns predictive features and recapitulates *Drosophila* vision. To appear at AAAI: arxiv.org/abs/2512.23146

Delighted to talk at Brown about our self-supervised neuronal algorithm for modeling biological circuits—and challenging backprop along the way. Thanks to @leokoz8 for the kind invitation! youtu.be/AF3Uhrm__U4?...

Biological neurons cluster dynamical stimulus trajectories to predict what’s coming and infer what just happened. If you’re at NeurIPS, stop by our poster #2107 — on display now until 2pm

Interested in algorithmic neuron models and learning rules at the intersection of neuroscience, AI, dynamical systems, and control theory? Working towards a PhD in neuroscience, physics, EE, or math? Apply for a summer internship with us: apply.interfolio.com/177775 At NeurIPS? Feel free to DM me.

Interested in algorithmic neuron models and learning rules at the intersection of neuroscience, AI, dynamical systems, and control theory? Have a PhD in neuroscience, physics, EE, or math? Consider joining us: apply.interfolio.com/173400 If you’re at NeurIPS, feel free to DM me.

An exciting opportunity!

Thanks @hadivafaii.bsky.social for the invitation! x.com/hadivafaii/s...

The brain survives by predicting the future. We suggest that single neurons cluster trajectories with common futures. But when observations are noisy, retrospection helps. Even individual neurons may look backward—like LGN lagged cells and olfactory bulb mitral cells: dailyneuron.com/how-sensory-...

Our poster in San Diego now at Society for Neuroscience

Fantastic and timely workshop! Many thanks to the organizers! neuroai-multimodal-workshop.github.io

The neuron as a controller and a few other thoughts. Thanks to the GreyMatters podcast for hosting me: open.spotify.com/episode/1ox9...