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1.
Nat Neurosci ; 27(7): 1411-1424, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38778146

ABSTRACT

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.


Subject(s)
Machine Learning , Neurosciences , Neurosciences/methods , Animals , Humans , Social Behavior
3.
Neuron ; 106(6): 1026-1043.e9, 2020 06 17.
Article in English | MEDLINE | ID: mdl-32294466

ABSTRACT

The central amygdala (CeA) orchestrates adaptive responses to emotional events. While CeA substrates for defensive behaviors have been studied extensively, CeA circuits for appetitive behaviors and their relationship to threat-responsive circuits remain poorly defined. Here, we demonstrate that the CeA sends robust inhibitory projections to the lateral substantia nigra (SNL) that contribute to appetitive and aversive learning in mice. CeA→SNL neural responses to appetitive and aversive stimuli were modulated by expectation and magnitude consistent with a population-level salience signal, which was required for Pavlovian conditioned reward-seeking and defensive behaviors. CeA→SNL terminal activation elicited reinforcement when linked to voluntary actions but failed to support Pavlovian associations that rely on incentive value signals. Consistent with a disinhibitory mechanism, CeA inputs preferentially target SNL GABA neurons, and CeA→SNL and SNL dopamine neurons respond similarly to salient stimuli. Collectively, our results suggest that amygdala-nigra interactions represent a previously unappreciated mechanism for influencing emotional behaviors.


Subject(s)
Appetitive Behavior/physiology , Avoidance Learning/physiology , Central Amygdaloid Nucleus/physiology , Dopaminergic Neurons/physiology , GABAergic Neurons/physiology , Substantia Nigra/physiology , Animals , Conditioning, Classical/physiology , Emotions , Mice , Neural Pathways , Reinforcement, Psychology , Reward , Substantia Nigra/cytology
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