David G. Clark

Associative synaptic plasticity creates dynamic persistent activity

The paper shows that coupling neuronal and synaptic dynamics in recurrent networks enables persistent oscillations, a dynamic form of memory without explicit storage or retrieval.

Structure, disorder, and dynamics in task-trained recurrent neural circuits

This study explores how structured and disordered connectivity in recurrent neural networks affects population dynamics and task performance, finding that minimal restructuring can achieve task-relevant representations.

A theory of multi-task computation and task selection

The paper develops a theoretical model to explain multi-task computation in neural networks, showing how task selection can overcome interference between different task dynamics.

Symmetries and continuous attractors in disordered neural circuits

This paper shows that continuous-attractor dynamics can operate in complex mammalian neural circuits despite apparent heterogeneity in neural responses.