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MRI brain

Emotional Cognition Theory

  • Derive the origin of emotion based on first principles in evolution theoretically
  • Develop a comprehensive model of emotional processing in autonomous being (animals and autonomous robots) called EMOTION (Emotional Model of The Theoretical Interpretation of Neuroprocessing
  •    » EMOTION-I model: accounts for "emotional feel" in sensations in evolution
  •    » EMOTION-II model: accounts for emergence of happy and unhappy emotions in evolution
  • Reveal the role of emotions in cognitive processing in neural decision making processes
  • Confirm the EMOTION models experimentally in human

Neural Spike Train Analysis

  • Develop statistical techniques to analyze multiple spike train signals
  • Derive the principles of spike train interactions among neurons in a neural network
  • Correlate the spike train signals with physiological and cognitive functions to deduce their signal contents

Neural Signal Decoding

  • Extract neural spike code signal from extraceullar recordings of action potentials
  • Reverse-engineer the principles of operations in neural processing
  • Decode the neural signals used by neurons in a neural network
  • Solve the encoding/decoding problems of repesentation of signals by pulse-coded signal
  • Abstract the low-level spike train signals into higher-level representation of message content

Neural Simulator

  • Develop a simulation model for reconstructing the neuroelectrophysiological functions of a neuron called MacNeuron.
  • Develop a simulation model of neural network with auto-associative reinforcement learning
  • Implement the generalizeable, scaleable principles for building a computational model of the brain
  • Implement the object-oriented principles for building an extensible model of the brain
  • Integrate analytical model in the neural simulators

Neuro-Assisted Prosthetic Device Control

  • Develop efficient representation of neural signals for predictive control in neuro-assisted prosthetic devices
  • Implement the encoding and decoding schemes of neural representation for neuroprosthetic control
  • Develop self-adaptive, predictive control for error-correcting neuroprosthetic motor control
  • Develop brain-computer interface (BCI) to use brain-controlled commands for robotics control

Autonomous Robot Control

  • Investigate the principles for self-actuating, autonomous systems
  • Apply the neural network learning algorithms in autonomous robot control
  • Investigate the real-world feedback interactions for evolving controling functions
  • Translate the principles between autonomous robots and self-actuating biological organisms

Optical Imaging

  • Brain imaging using non-invasive optical signals that penetrate brain tissues
  • Optical imaging of the brain using functional near-infrared spectroscopy
  • Record cortical brain activations based on oxy- and deoxy-hemoglobin optical signals
  • Correlation of cognitive activities with hemodynamics in localized cortical areas
  • Imaging of motor cortical activities to decode intentional movement directions

Exercise Neuroscience

  • Effects of physical exercise (aerobic activities) on improvement of cognitive performance
  • Identify specific cognitive enhancements from specific motivated/ skilled aerobic exercises
  • Identify the neural correlates of mind-body connection between physical and mental activities

Evolutionary Neuroscience

  • Derive the principles and criteria for consolidating learned functions into the next generation
  • Explore the mechanisms for transfer from learned circuitry to reflex control circuitry
  • Implement a neural network model for evolutionary learning that spans multiple generations