# A circuit mechanism for the interactions between distinct learning systems > **NIH NIH R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2024 · $500,228 ## Abstract Project Summary Our ability to learn and to remember countless experiences is essential for our daily lives. However, our dependence on these processes also makes us vulnerable to disorders which affect the underlying neuronal circuits. Notably, disorders causing cognitive impairments may broadly disrupt multiple forms of learning and memory, even without directly affecting the underlying circuits themselves. This may be due to disruptions of processes that coordinate learning across multiple learning systems. While the systems that form our different kinds of memories, like motor skills or directions, are largely separate, they interact in various and surprising ways. Memorizing facts can for example interfere with or boost the learning of motor skills. This suggests that the continuous stream of experiences and learning events needs to be tightly controlled to prevent disruptions and to take advantage of existing memories. We suggest that prefrontal cortex (PFC) exerts such cognitive control and coordinates learning across multiple systems. Cognitive impairments disrupting PFC function may therefore impair learning broadly. In particular, we suggest that PFC, to coordinate learning processes, extracts common rules and task components. In the long term, this allows for identification of hidden relationships, generalization across tasks and improved learning of new tasks. We hypothesize, however, that in the short term, the underlying mechanisms interfere with and, under certain circumstances, also enhance learning. We will test this using our cutting-edge custom infrastructure for high-throughput behavioral training and for continuous (24/7) long-term in vivo electrophysiological recordings. With this, we will address three interrelated, but fully independent aims. Aim 1 will identify the task components which, when shared across tasks, drive interactions between learning systems. In large-scale behavioral experiments we will train animals on pairings of procedural and declarative tasks and manipulate shared procedural or declarative task components. This will reveal whether the two learning systems differentially affect the mechanisms underlying learning interactions. Aim 2 will determine the neural activity dynamics in PFC during learning interactions, track the development of representations of shared task components, and show how optogenetically identified PFC projection neurons mediate learning interactions. Importantly, for this we will integrate high-density Neuropixels probes with our system for long-term electrophysiological recordings. Finally, Aim 3 will causally test if and at what times during training PFC and its projections are necessary for learning interactions, using chemogenetic manipulations via implanted micro-infusion pumps over weeks of training. This multi-level, orthogonal experimental approach promises vertical advances toward an understanding of the circuit mechanisms underlying learning interactions and the relationsh... ## Key facts - **NIH application ID:** 10933749 - **Project number:** 1R01MH136968-01 - **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE - **Principal Investigator:** Steffen Wolff - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $500,228 - **Award type:** 1 - **Project period:** 2024-07-18 → 2029-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10933749 ## Citation > US National Institutes of Health, RePORTER application 10933749, A circuit mechanism for the interactions between distinct learning systems (1R01MH136968-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10933749. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*