Reinforcement learning theory has been extensively applied within neuroscience to interpret empirical findings related to dopaminergic signaling and associated behaviors. However, the nuanced analysis of scenarios where dopamine and serotonin release have synergistic effects on post-synaptic neurons in the striatum remains relatively uncharted. Additionally, we aim to employ machine learning algorithms for a more comprehensive characterization of behavioral patterns. This advanced analytical approach is poised to facilitate the identification of correlations between neuromodulatory activities and behavior, potentially uncovering associations that have previously eluded detection.

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