Chaitanya Chintaluri

This paper shows that Dalean connectivity, stability, and nonlinear responses can generate diverse neural dynamics without data fitting.

This study shows that reducing Letm1 increases mitochondrial Ca2+ retention, overactivates metabolism, and enhances long-term memory in flies and mice.

The paper introduces a standardized ion channel model based on Hodgkin-Huxley formulation, demonstrating its ability to fit most voltage-gated models and enable parameter comparison and inference.

kCSD-python provides a Python package for reliable current source density estimation with quality control techniques and diagnostic tools.

The paper proposes that non-input driven neuronal firing serves as a mechanism to protect neurons from mitochondrial toxicity by regulating metabolic products and reactive oxygen species.

The paper analyzes what information about brain current sources can be recovered or remains unobservable using extracellular multielectrode recordings, across different experimental setups.

This paper introduces a machine learning method using deep neural density estimators to perform Bayesian inference on neural dynamics models, demonstrating its application to various neural systems.

The paper derives and corrects analytical formulas for scalp potentials in the four-sphere EEG head model and compares them with FEM simulations.