Ian Cone

This paper shows how hunger reconfigures a reward circuit in Drosophila to support memory formation by switching dopamine neuron activity modes.

This paper introduces a generalized BTSP rule for neural network learning and shows it can perform unsupervised and supervised tasks but faces challenges with network depth.

This paper introduces a new theoretical framework, FLEX, which challenges the TD learning model by proposing flexible temporal representations for reward prediction and demonstrates its feasibility through a biophysically plausible model.

This paper presents a biophysically plausible model of hippocampal networks that co-evolve latent representations with behavioral task structure, showing task-specific development of necessary representations.

This paper introduces a biophysically realistic neural network model that learns and recalls spatiotemporal sequences using modular architecture and eligibility trace learning rules.

The paper develops a mathematical model to analyze how behavioral time scale plasticity affects place fields in the hippocampus, showing how synaptic eligibility traces and instructive signals lead to fixed points in place field dynamics.