Christopher J. Rozell

This study uses simulations to show that low-dimensional optogenetic stimulation can effectively control neural latent dynamics with fewer channels than neurons, guiding future hardware design.

This paper shows that dopamine and serotonin changes during deep brain stimulation predict symptom relief in treatment-resistant depression.

The paper analyzes LFP data from the subcallosal cingulate cortex following DBS implantation to explore neural dynamics and introduces two new measures to assess brain power fluctuations in depression.

This study tracks changes in cortical response dynamics during SCC-DBS for treatment-resistant depression, finding reduced latency and increased magnitude of stimulation-evoked potentials over time.

The paper introduces Cleo, a simulation testbed for systems neuroscience experiments, integrating optogenetics, electrophysiology, and closed-loop control to improve experimental design and model validation.

This study examines how deep brain stimulation affects interoceptive processing in treatment-resistant depression patients using EEG and HEP measurements.

This study decodes depression symptoms using neural recordings from deep brain stimulation patients, finding oscillatory patterns predictive of depression severity.

This paper uses an efficient coding model to show that optimal E:I ratios in sensory cortices arise from trade-offs between computational accuracy and representation capacity under brain volume constraints.

This paper develops a theoretical neuroscience model for sparse coding in compressed neural representations that respects local wiring constraints in cortical communication.

This paper applies state-space optimal control to feedback regulate neuronal activity in awake mice using optogenetics, showing effective single-neuron control and generalization to heterogeneous populations.