Christopher J. Rozell

Feasible closed-loop stimulation for neural latent dynamics control

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.

Dopamine and serotonin transients predict depressive symptom relief following deep brain stimulation of human subcallosal cingulate cortex

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

Ephaptic coupling and power fluctuations in 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.

Longitudinal Changes in Cortical Response Dynamics with Deep Brain Stimulation to the Subcallosal Cingulate for Treatment-Resistant 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.

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

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.

Neural Interoceptive Processing is Modulated by Deep Brain Stimulation for Treatment Resistant Depression

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

Decoding Depression During Subcallosal Cingulate Deep Brain Stimulation

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

Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices

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.

Learning sparse codes from compressed representations with biologically plausible local wiring constraints

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

State-space optimal feedback control of optogenetically driven neural activity

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.