Computational Neuroscience

To complement our experimental recordings, we employ computational neuroscience to investigate how information is processed, transmitted, and consolidated across complex neuronal networks. By building detailed in silico models, we can simulate large-scale neural dynamics to uncover the fundamental principles governing both healthy cognition and motor dysfunction. Our computational efforts range from exploring how biophysical phenomena—such as stochastic resonance—optimize memory consolidation, to modeling the disrupted basal ganglia-thalamic circuits seen in Parkinson's disease. These circuit-level simulations allow us to systematically test how interventions like Deep Brain Stimulation (DBS) abolish pathological activity and restore high-fidelity information relay, bridging the gap between single-neuron biophysics and macroscopic brain function.

Caston RM, Wilson MG, Comeaux PD, Dorval AD (2022). “Stochastic resonance governs memory consolidation accuracy in a neural network model.” Proc of the IEEE Eng in Med and Biol Soc, 2022:2254-2257, PMID36085728.

Dorval AD, Panjwani N, Qi RY, Grill WM (2009) “Deep brain stimulation that abolishes parkinsonian activity in basal ganglia improves thalamic relay fidelity in a computational circuit.” Proc IEEE EMBS, 1:4230-4233. PMC2819373.