Brain Machine Intelligence

The goal of our research in Brain-Machine Intelligence is to understand how humans process information and effectively interact with the environment to develop smarter autonomous systems, enhance human performance, and develop novel processing systems. Our research starts with the development of large scale, neurobiologically and behaviorally faithful brain region models. We apply these models along with real-time measurements of brain activity (EEG, fMRI, fNIRS) to create advanced brain-computer interfaces, human decision aids, and neurostimulation-based enhanced training systems.  We also use our models to develop novel, brain-inspired sensor exploitation, machine learning, and control algorithms, and to develop low size, weight and power brain-based processing hardware for resilient autonomous systems, dexterous robots, and threat warning applications.

R&D Capabilities
Computational Neural Modeling Machine Augmented Intelligence Neuromorphic Computing

• Brain region models

- Spiking, rate-coded, Bayesian

• Brain-based computing

- Perception, learning, decision making

• Neuro-cognitive and neuro-mechanical digital humans

• Large-scale simulations

• Cognitive modesl for decision aids

• EEG, EMG, fNIRS, fMRI processing & decoding

• Brain-machine interfaces

• Neurostimulation-based enhanced training

• Spiking neurons

• Neural learning rules & mechanisms

• Large scale neural network compliers

• Low power, mixed signal CMOS design & fab