We aim to uncover the mechanisms of neural information processing in three levels.

1. Micro-level (single neuron)

Aim: Identification of the biophysical mechanisms of a single neuron.

Strategy: We consider a single neuron as a complex system whose input-output relationship is unknown. Using biologically realistic models, we develop advanced mathematical/statistical tools to infer the unknown parameters of these models from sparse observation (e.g., membrane potential). These models with appropriate parameters will decipher the biophysical mechanisms of the corresponding neuron.

2. Medium-level (neural network)

Aim: Identification of the neural coding mechanisms of ensemble of neurons.

Strategy: We consider a neural network as a very complex system. we use a combination of bottom-up and top-down strategies to identify its coding mechanisms. In bottom-up strategy, we construct specific structures of the neural network (e.g., feed-forward) using biophysically-detailed neuron models (similar to 1). In top-down strategy, we develop abstract models (e.g., linear non-linear model) as well as other computational techniques to fit the model’s output to the neural activity of the constructed network. With these strategies, we are able to infer the mechanisms of information representation in network-level as well as the underlying biophysical properties in cellular-level.

3. Macro-level (Brain & Body)

Aim: Improvement (quantity & quality) of the accessibility to neural activity

Strategy: We develop novel technologies that enable us to access multiple bio-signals (e.g., EEG, EoG, EMG and ECG) simultaneously. By embedding electronics within wearable textiles, we develop novel signal processing algorithms to infer bio-information from these bio-signals (e.g., alpha wave from EEG and heart rate variability from ECG). Our ultimate goal is to infer the correlational patterns between these bio-signals corresponding to the neurological diseases (e.g., Parkinson disease).

Research Interests:

• Theoretical and Computational Neuroscience, System Neuroscience
• Information Theory, Statistical Signal Processing and Bayesian Inference
• Neural Networks, Signal Processing and Adaptive Filtering
• Bio-sensors and Wearable Neurotechnology