Working memory, the temporary storage of information for future manipulation to guide actions, is an essential component of nearly all cognitive processes. Deficits in working memory contribute to a variety of mental health disorders, including schizophrenia, autism, bipolar disorder, and attention-deficit hyperactivity disorder. Working memory is thought to be an emergent property of neurons acting in groups to form microcircuits. However, due in large part to technical limitations, working memory has been studied nearly exclusively at the scales of entire brain regions or individual neurons, both of which fail to reveal interactions in neuronal populations. A major challenge in understanding the neural mechanisms for working memory is to develop tools to measure, analyze, and model working memory at the scale of the neuronal microcircuit. In this application, we present approaches to solve this challenge using two-photon imaging of activity in neuronal populations in mice performing working memory tasks in virtual reality. We will combine our large imaging data sets with theoretical modeling approaches to develop new computational models of how working memory is generated in microcircuits. First, we will examine how working memory representations are encoded in neuronal populations by examining the stability of representations over long timescales, using chronic two- photon imaging of the same neurons over weeks during working memory behaviors. Second, we will perform imaging experiments during novel working memory tasks to test competing, long-standing theoretical models of working memory, including models based on attractor dynamics and sequence dynamics. Third, we will develop a new theoretical modeling framework in conjunction with rapid experiment-model iterations to generate and test new microcircuit-scale hypotheses for the neural mechanisms underlying working memory. Together the proposed work is expected to establish new approaches to measure, analyze, and model microcircuit function during working memory in the mouse, leading toward mechanistic studies of how working memory and its underlying microcircuits are disrupted in mental illness.

Public Health Relevance

Working memory is essential for nearly all cognitive functions and is disrupted in diseases including schizophrenia, bipolar disorder, and attention-deficit hyperactivity disorder. The proposed projects combine experimental measurements and theoretical models to understand how working memory is implemented in neuronal microcircuits. By investigating working memory at a level not studied extensively (the microcircuit), our results are expected to reveal new insights into cognitive functions both in health and disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
3R01MH107620-03S1
Application #
9590197
Study Section
Program Officer
Buhring, Bettina D
Project Start
2018-02-22
Project End
2019-08-31
Budget Start
2018-02-22
Budget End
2018-06-30
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Biology
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
Jackman, Skyler L; Chen, Christopher H; Chettih, Selmaan N et al. (2018) Silk Fibroin Films Facilitate Single-Step Targeted Expression of Optogenetic Proteins. Cell Rep 22:3351-3361
Runyan, Caroline A; Piasini, Eugenio; Panzeri, Stefano et al. (2017) Distinct timescales of population coding across cortex. Nature 548:92-96
Driscoll, Laura N; Pettit, Noah L; Minderer, Matthias et al. (2017) Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex. Cell 170:986-999.e16
Panzeri, Stefano; Harvey, Christopher D; Piasini, Eugenio et al. (2017) Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior. Neuron 93:491-507
Rajan, Kanaka; Harvey, Christopher D; Tank, David W (2016) Recurrent Network Models of Sequence Generation and Memory. Neuron 90:128-42
Morcos, Ari S; Harvey, Christopher D (2016) History-dependent variability in population dynamics during evidence accumulation in cortex. Nat Neurosci 19:1672-1681