|Interacting Memory Systems Network Labs|
Gyorgy Buzsaki, PI
In general terms, our main interest is how neuronal circuitries of the brain support its cognitive capacities. Our goal is to provide rational, mechanistic explanations of cognitive functions at a descriptive level. In our view, the most promising area of cognitive faculties for scientific inquiry is memory, since it is a well-circumscribed term, can be studied in animals and substantial knowledge has accumulated on the molecular mechanisms of synaptic plasticity. To address these issues, we are using large-scale recordings from neurons and local field potentials in behaving animals.
The Mozer Lab uses computational models to understand the mechanisms of human learning and cognition. Particular focus has been in the areas of visual perception, selective attention, memory, and executive control. Given a computational understanding of the mind, the lab develops software that helps individuals to learn and perform better. Current projects include: drill-and-practice software that leverages spacing of study to optimize human learning (e.g., foreign language vocabulary), visual highlighting techniques to promote efficient training on complex visual tasks (e.g., matching fingerprints), saliency-based image enhancement to assist human analysts (e.g., satellite imagery), methods of improving training on concept learning (e.g., sequencing of training examples), and obtaining more meaningful human judgments by automatic removal of human biases (e.g., sequential dependencies). All of these projects depend not only on computational models of the mind but also on state-of-the-art statistical techniques such as collaborative filtering, deep networks, and Bayesian models.
In the past, the lab has worked on applications of machine learning techniques to solve practical problems. In one project, the Adaptive House, a control system was built that learned to manage energy resources (air heat, water heat, lighting, and ventilation) in an actual residence to maximize the satisfaction of the inhabitants and minimize energy consumption.
Our lab develops computational and formal models of the biological bases of cognition (computational cognitive neuroscience), focusing on specialization of function in and interactions between hippocampus, prefrontal cortex/basal ganglia, and posterior neocortex in learning, memory, attention, and controlled processing. We test predictions from these models using a range of behavioral and other experimental techniques.
Learning, Attention, and Perception Lab, UCSD
Hal Pashler, PI
The Learning, Attention, and Perception Lab works on a broad range of questions about human attention, memory and learning. In the area of memory and learning, the research is focused not only on understanding basic mechanisms, but also at uncovering principles that have direct practical application in enhancing learning in educational and skill-learning contexts. In the area of attention, the lab has for some years explored the relationship between attention and visual perception, and charted basic human multitasking limitations. In most of these areas, research involves a combination of behavioral experimentation and formal analysis.
Memory Research Lab, UCSD
Larry Squire, PI
The Complex and Intelligent Systems group at the University of Queensland has strengths in cross-disciplinary research in natural and artificial systems, from systems biology to systems neuroscience, and from biorobotics to intelligent information systems.