Photo of Andres D. Grosmark, Ph.D.

Andres D. Grosmark, Ph.D.

Assistant Professor of Neuroscience
Academic Office Location:
UConn Health
263 Farmington Avenue
Farmington, CT 06030-3401


Memory and Cognition Lab

B.A.Duke UniversityPhilosophy
B.S.Duke UniversityBiology; Concentration: Neuroscience
Ph.D.Rutgers UniversityNeuroscience

Name of Award/HonorAwarding Organization
Biomedical FellowshipRevson Foundation
Faculty of 1000 Recommendation of First Author Article (Science, 2016) Recommended by Drs. Edvard Moser and Dave Rowland
Faculty of 1000 Recommendation of First Author Article (Neuron, 2012) Recommended by Dr. Lu Chen
Minority Biomedical Research Fellow
Sloan Minority Ph.D. Fellowship
Presidential Graduate FellowRutgers University

What Are the Circuit Mechanisms of Long-Term Memory?
The overt learning that occurs while you are navigating the world around you is complemented by a covert form of re-learning, called memory consolidation, which occurs during your down-time. But how do these distinct memory mechanisms lead to distinct memory outcomes? A short thought experiment can help us tease apart these differences: imagine that you moved to a new city and go out to meet an old friend at a particularly great new bar. It takes you 20 minutes to get there, you then spend 3 enjoyable hours with him, and then another 20 minutes to get back. Now, due to both the length and quality of the time you spent there, it may seem logical for your brain to prioritize bar-specific memories for permanent consolidation. However, it’s only the 40 minutes you spent in transit which contain the information that you’ll need if you ever want to get back to that bar again in the future! Our recent work suggests memory consolidation plays a specific role in consolidating these less memorable, but often just as important aspects of experience. In other words, memory consolidation cognitively compliments ‘active’ learning by promoting the formation of ‘broad’ cognitive maps.

This division of cognitive labor in turns opens up exciting new questions. For instance, how does the local circuitry in the hippocampus, a structure associated with several forms of learning, change its function during active behaviors and during passive memory consolidation? And what can these differences teach us about the different cognitive ‘work’ carried out during each of these states? Our lab will answer these questions by combining cutting-edge two-photon imaging, electrophysiology, genetic targeting and closed-loop optogenetic manipulations together with advanced behavioral paradigms to dissect the initial formation and subsequent evolution of long-term memories.

Untangling Memory Consolidation’s Role in Schizophrenia and Aging
What are the circuit mechanisms of long-term memory deficits in mouse models for schizophrenia, and how can these deficits be rescued? While schizophrenia is most often associated with its positive, psychotic, symptoms and working-memory impairments, it is also associated with a range of cognitive deficits including large and well-documented impairments in long-term episodic memory. In a previous collaboration I showed that a mouse model for schizophrenia displayed not only impaired long-term spatial memory, but aberrantly high rates of sharp-wave ripples, which are short-lived, large population synchrony events strongly implicated in memory consolidation. Notably, alterations in sharp-wave rippling are also observed in age-related mild cognitive impairments as well as in Alzheimer’s disease – though the effect of these changes on their associated memory deficits remains little understood. By dissecting the different cognitive functions of memory formation and consolidation our lab’s work will aim to uncover new mechanisms by which pathologies affecting memory can come about – and in turn how specific interventions can ameliorate the resulting memory deficits.

Journal Articles

Book Chapters

  • Local Circuits
    Grosmark A., Milstein A.D., Losonczy A. & Soltesz I The Hippocampus Book, 2nd Edition


  • Recordings from hippocampal area CA1, PRE, during and POST novel spatial learning. Dataset HC11. (Dataset used in over 18 published articles and chapters).