bioRxivNeurosciencePreprint
Spatially organized striatal neuromodulator release encodes trajectory errors
Eleanor H. BrowniD, Yihan ZiiD, Mai-Anh T. VuiD, Safa BouabidiD, Jack W. LindseyiD, Chinyere Godfrey-Nwachukwu, Aaquib Attarwala, Ashok Litwin-KumariD, Brian DePasqualeiD, Mark W. HoweiD
Abstract
Goal-directed navigation requires animals to continuously evaluate their current direction and speed of travel relative to landmarks to discern whether they are approaching or deviating from their goal. Striatal dopamine and acetylcholine are powerful modulators of goal-directed behavior, but it is unclear whether and how neuromodulator dynamics at landmarks incorporate relative motion for effective behavioral guidance. Using optical measurements in mice, we demonstrate that cue-evoked striatal dopamine release encodes bi-directional trajectory errors reflecting relationships between ongoing speed and direction of locomotion and visual flow relative to optimal goal trajectories. Striatum-wide micro-fiber array recordings resolved an anatomical gradient of trajectory error signaling across the anterior-posterior axis, distinct from trajectory error independent cue signals. Dynamic regression modeling revealed that positive and negative trajectory error encoding emerges early and late respectively during learning and over different time courses in the medial and lateral striatum, enabling region specific contributions to learning. Striatal acetylcholine release also encodes trajectory errors, but encoding is more spatially restricted, opposite polarity, and delayed relative to dopamine, supporting distinct roles in modulating striatal output and behavior. Dopamine trajectory error signaling and task performance were reproduced in a reinforcement learning model incorporating a conjunctive state space representation, suggesting a potential neural substrate for trajectory error generation. Our results establish region specific neuromodulator signals positioned to guide the speed and direction of locomotion to reach goals based on environmental landmarks during navigation.
- organism: mouse
- assay: electronic health records
- research focus: cell-cell communication
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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