Mapping Memory (G. Lynch)

Gary Lynch and his team are providing the first maps of synapses exhibiting LTP-like plasticity in their search for the memory engram laid down after short sessions of unsupervised learning.

Cortical Models of Unsupervised Learning (T. Sejnowski)

Terrence Sejnowski and his team are initiating new cortical network modeling that incorporates plasticity and decision making to study the mechanisms of unsupervised learning.

Training Studies (H. Pashler)

Hal Pashler and his team are developing new dynamic categorization tasks to examine the role that unsupervised learning can play in training.

Brain Dynamics Underlying Unsupervised Spatial Learning (H. Poizner)

Howard Poizner and his team are utilizing new technologies they developed for synchronous recording of limb, head, and body movements, and high density cortical EEG, while subjects navigate in large scale immersive virtual environments to examine biomarkers for unsupervised learning and memory.

Optimization of Action in Dynamic Environments (S. Gepshtein)

Sergei Gepshtein and his colleagues are developing new computational models and experimental paradigms for research of action and decision making, to improve our understanding of how subjects act in rapidly changing environments, under risk and uncertainty.

Simultaneous EEG-fMRI (T. Liu)

Tom Liu and his team are developing new algorithms and methods of analysis of simultaneous EEG-fMRI brain imaging recordings in order to develop a new methodology of spatiotemporal functional brain imaging.

Spatiotemporal Functional Brain Imaging during Unsupervised Spatial Learning (H. Poizner)

Howard Poizner and his team are incorporating haptic robotic interactions in virtual environments during simultaneous EEG-FMRI recordings in order to examine spatiotemporal brain function during unsupervised spatial learning.

Intracranial Electrophysiology of Unsupervised Spatial Learning (E. Halgren)

Eric Halgren and his team are directly recording from the human brain using intracranial electrophysiology during unsupervised spatial learning in virtual environments.

Genetic Control of Dopaminergic-Dependent Learning in Drosophila (R. Greenspan)

Ralph Greenspan and his team are using new genetic tools to examine genes relevant to dopaminergic control of interregional brain coherence and learning in Drosophila, so as to then be able to bootstrap up to mammalian systems.