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In talking about art, the philosopher Alva Noë describes the concept of organized activities and lists a series of defining characteristics. An organized activity is a practice that has its roots in biology, requires cognition and thinking, unfolds over time with a specific temporal structure, emerges via interactions between the participants, has a function (or multiple functions) and is a potential source of pleasure (or disgust). Eating, like art, is an organized activity that requires the coordination of sensory, motor, physiological, affective and cognitive processes. Understanding eating implies studying how neural circuits learn to integrate these processes and relate to behavior. 

Our lab takes this perspective in studying the insular cortex (aka gustatory cortex) and other subcortical areas classically believed to be involved in taste and reward. We study these circuits in rats and mice. We identify food-related behaviors that require decisions, anticipations and perception and study the role of the insular cortex in mediating them. We use behavioral training, electrophysiology (multielectrode recordings and intracellular physiology), optical imaging (2photon calcium imaging), pharmacology, molecular tools, anatomy and computational methods.


Our current projects involve: i) understanding how populations of insular neurons encode taste related information; ii) understanding how food-predictive stimuli of various sensory modalities engage neural populations the insular cortex; iii) studying how dopamine modulates the cortical control of licking and taste-based behaviors;  iv) dissecting the role of the insular cortex in mediating taste-based decisions.​


In addition to our in-house work, we have extensive collaborations with the laboratories of Arianna Maffei (who uses synaptic physiology to dissect cortical circuits and their plasticity), Giancarlo La Camera (who uses theory and simulations to understand neural dynamics and coding), and Memming Park (who uses statistical modeling, signal processing and machine learning to analyze brain activity).

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