Home Research Our Labs Interacting Memory Systems Network
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.

CRLCenter for Research in Language, UCSD
Jeff Elman

The Center for Research in Language (CRL) brings together faculty, students and research associates who share an interest in the nature of language, the processes by which it is acquired and used, and the mediation of language in the human brain. CRL is housed in the Cognitive Science Building on the Thurgood Marshall Campus at the University of California, San Diego, with an interdisciplinary academic staff comprising specialists in cognitive science, computer science, communication disorders, developmental psychology, linguistics, neurosciences, communication, pediatrics and psycholinguistics.

CRLLaboratory of Genetics LOG-G
Gage Lab





 Cortical Processing Lab
Ken Harris, PI

Many theories for brain function have been proposed over the last century. But only in the last few years has it become possible to record simultaneously from large enough numbers of neurons to put these theories to the test experimentally. This is an unprecedented opportunity, but it opens up a new question: how do we go from the gigabytes of experimental data that we now have, to concise conclusions about the function of the brain?

The data processing methods traditionally used in neuroscience are not sophisticated enough to exploit this new flood of information. Fortunately, modern statistics and machine learning theory is making great strides in precisely the type of techniques needed to process these large multivariate databases. By applying these methods to neuronal data, we can now test long-standing hypotheses about brain function.

 Mozer Lab, Colorado, Boulder
Michael Mozer, PI

The way we study material influences how well we retain it. Psychologists have established that spaced practice leads to better retention than massed practice. However, the exact relationship between spacing and retention depends in a significant way on the duration of time over which the material must be retained. We are exploring existing and novel computational models to explain a range of data on massed versus spaced practice.

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

Our interest is in the organization and structure of mammalian memory (humans and rodents) in terms of anatomy and function at the level of neural systems and cognition. Our research draws on the traditions of neuroscience, neuropsychology, and cognitive science. A part of our research involves studies of identified patients with amnesia. The analysis of such cases provides useful information about the structure and organization of normal memory. In addition, the facility for functional imaging at UCSD is affording the possibility of studying brain systems of human memory in normal subjects. This technology opens a new era of investigation into the brain systems of human memory. We also study rodents, particularly with respect to questions about the anatomy of memory and the function of the brain systems that support memory. This work is done under the leadership of Dr. Robert Clark.


Wiles LabComplex & Intelligent Systems Research Group at the
University of Queensland, Australia
Janet Wiles, Group Leader

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.