NEUROMODULATORY REGULATION OF REINFORCEMENT-LEARNING SIGNALS DURING MEMORY-BASED BEHAVIOR IN HEALTH AND DISEASE
Cajal Neuroscience Center (CNC), CSIC
Presentation
Date TBA
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Poster Board
PS07-10AM-356
Poster
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Reinforcement learning provides a computational framework for adaptive behavior, where midbrain dopamine encodes reward prediction errors and locus coeruleus–norepinephrine activity conveys unexpected uncertainty. It has been recently shown that the hippocampus receives dopaminergic and noradrenergic projections that modulate spatial coding and memory formation. However, their regulation of hippocampus-dependent recall and spatial navigation remains unclear. To investigate this, mice performed a high-throughput 8-port navigation task (Morales et al., 2020), in which interference from memories of different age is parametrically controlled, and learning is dissociated from recall through unrewarded trials. This design allows the quantification of multiple behavioral features, including choice accuracy, memory-guided decision making, and strategy switching flexibility. Fiber photometry recordings of GRAB-DA in nucleus accumbens and dorsal hippocampus, and GRAB-NE in ventral hippocampus, measured how neuromodulator dynamics aligned to distinct behavioral epochs. Here, we hypothesize that hippocampal dopamine transients scale with reward prediction errors during recall, modulating trial-by-trial spatial memory performance even in the absence of reward. Instead, we predict that hippocampal norepinephrine signals increase at event boundaries and behavioral change-points, reflecting uncertainty and promoting adaptive strategy switching. Furthermore, we hypothesize that these neuromodulatory dynamics are affected in disease models, impairing recall and cognitive flexibility. Preliminary analyses from a limited number of sessions reveal neuromodulatory patterns consistent with these hypotheses, supporting the feasibility of the approach and motivating systematic investigation across animals, task conditions, and disease models. This work aims to establish a mechanistic link between neuromodulatory signaling, memory-based decision making, and cognitive dysfunction.
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