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Lopez, G. C.

Publications and source records attributed to Lopez, G. C..

2 recordsLinked to original sources

Region-specific Nucleus Accumbens Dopamine Signals Encode Distinct Aspects of Avoidance Learning

Avoidance learning - learning to avoid bad outcomes - is an essential survival behavior. Dopamine signals are widely observed in response to aversive stimuli, indicating they could play a role in learning about how to avoid these stimuli. However, it is unclear what computations dopamine signals perform to support avoidance learning. Furthermore, substantial heterogeneity in dopamine responses to aversive stimuli has been observed across nucleus accumbens (NAc) subregions. To understand how heterogeneous dopamine responses to aversive stimuli contribute to avoidance learning, we recorded NAc core (Core) and NAc ventromedial shell (vmShell) dopamine during a task in which mice could avoid a footshock punishment by moving to the opposite side of a 2-chamber apparatus during a five-second warning cue. We found that NAc Core and vmShell dopamine signals responded oppositely during shocks and warning cues. Both signals evolved substantially - but differently - with learning. NAc vmShell dopamine responses to cues and shocks were present during early learning but not sustained during expert performance. NAc Core dopamine responses strengthen with learning and are especially evident during expert performance. Our data support a model in which NAc vmShell dopamine guides initial cue-shock associations by signaling salience, while NAc Core dopamine encodes prediction errors that guide the consolidation of avoidance learning.

neuroscience↗

GuPPy, a Python toolbox for the analysis of fiber photometry data

Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be a challenge for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is provided as a Jupyter notebook, a well-commented interactive development environment (IDE) designed to operate across platforms. GuPPy presents the user with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs produced by GuPPy can be exported into various image formats for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.

neuroscience↗