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Masakazu Agetsuma, Ph.D.
October 11 @ 1:00 p.m. - 5:00 p.m.
FreeThe Center for Neural Circuit Mapping will host guest speaker Dr. Masakazu Agetsuma, Associate Professor of Neuroscience at the National Institute for Physiological Sciences, Japan
“Activity-dependent organization of prefrontal hub-networks for associative learning and signal transformation”
Associative learning is crucial for adapting to environmental changes. Interactions among neuronal populations involving the dorso-medial prefrontal cortex (dmPFC) in rodents are proposed to regulate associative memory. Although neural network remodeling is generally believed to underlie learning and memory, how this process occurs to store and process associative memory remains one of the most critical open questions in the field. To tackle this question, we developed a pipeline for longitudinal two-photon imaging and mathematical dissection of neural population activities in mouse dmPFC during fear-conditioning procedures, enabling us to detect learning-dependent changes in the dmPFC information coding and network topology. After confirming that the dmPFC contributes to the expression of the conditioned responses (CR) by chemogenetic silencing, we recorded neural population activities and analyzed them by regularized regression methods and graphical modeling. We found that fear conditioning drove dmPFC reorganization to generate a neuronal ensemble encoding CR, which was characterized by enhanced internal coactivity and functional connectivity. Importantly, neurons strongly responding to unconditioned stimuli during fear conditioning subsequently became hubs of this novel network and revealed enhanced association with conditioned stimuli (CS) specifically in the CR ensemble, which may work as an information-processing neural network implementing CS-triggered CR. Altogether, we demonstrate learning-dependent dynamic modulation of population coding structured on the activity-dependent formation of the hub network within the dmPFC. features reflecting the biomechanical constraints and evolutionary origins of these motor control systems.”