Computational neuroscience demos || ccli.substack.com
Interactive demo of Ching Fang’s repo here; all errors are mine. Built using Claude and Gemini.
From paper, Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding by Fang, Lindsey, Abbott, Aronov, & Chettih 2025.
Also check out the guided tour.
When does reward magnitude matter in classic behavioral algorithms?
From paper, Reward magnitude determines reinforcement learning efficiency by Gong, Martell, Dudman, & Coddington 2026.
Live, in-browser simulations of reward-seeking behavior emerging from local Hebbian-like plasticity: reward seeking, learning through neuron turnover, continual learning (Hebbian vs. three-factor RPE rule), and open-field search. Built as talk animations.
From paper, Reward-seeking behavior emerges from Hebbian-like plasticity by Li, Brenner, Ye, Du Pont, Kudarauskas, Chen, & Erlich 2026. The open-field demo uses the model from Neuron-level prediction and noise can implement flexible reward-seeking behavior by Li, Brenner, Boesky, Ramanathan, & Kreiman 2024.