Rich Pang

This paper introduces PRISM plasticity, a unified model of synaptic plasticity rules, and shows it enables rapid learning of flexible neural dynamics and solves temporal credit assignment in machine learning.

This paper introduces a non-Hebbian plasticity rule for episodic memory that stores episodes as path vectors in a world model, enabling flexible memory and learning.

The paper develops a simulation-based model to infer neural dynamics of memory during fly courtship, finding that adaptive population mechanisms transform song history into a mnemonic format.

The paper investigates how a single neural pathway in Drosophila generates context-dependent song sequences by switching between two regimes based on sensory input.

The paper maps the auditory pathway in Drosophila, identifies new cell types, and reveals a network of neurons with continuous song mode preferences and non-hierarchical connectivity.

The paper introduces a model inspired by songbird brains that uses low-dimensional input fluctuations to correct timing errors in motor sequences, verified in zebra finch songs.

This paper introduces a model where sequences are represented as path vectors through cognitive states, showing how structured narratives improve memory recall by reducing path interference.

The paper demonstrates how rapid excitability changes in spiking neural networks can enable fast and flexible sequence replay, mimicking cognitive flexibility mechanisms in the hippocampus.