Deciphering neural codes of valence based emotional memories
Pioneering studies from the last decades have shown that we can use animal models to study the neural correlates of learning and emotional memories. These studies have identified a network of brain regions that co-orchestrate the...
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Descripción del proyecto
Pioneering studies from the last decades have shown that we can use animal models to study the neural correlates of learning and emotional memories. These studies have identified a network of brain regions that co-orchestrate the dynamic processes that occur during acquisition, retrieval and update of memories with valence. In primates, these areas and their connectivity have evolved to allow a complex interaction between cognitive abilities and emotions. Although we know that emotional memories differ from neutral memories, the actual neural codes that underlie them are largely unknown. Understanding emotional memories at a detailed level in a primate model is a crucial step in our goal to understand ourselves, and would allow specific therapeutic interventions for a spectrum of psychiatric disorders.
Our primary objectives are: a. to develop a comprehensive battery of behavioral paradigms that targets emotional learning and memory in non-human primates; b. to understand the neural mechanisms that underlie long-term evolvement of these memories; c. to develop a computational and experimental framework to understand coding in these high-dimensionality networks. We combine large-scale inter- and intra-regional simultaneous electrophysiological recordings in the primate amygdala and prefrontal-cortices, with a set of focused paradigms that use behavioral generalization as a tool to probe the underlying neural building blocks. Generalization is a highly-evolved cognitive ability of primates and a desired feature of any learning algorithm. Importantly, it is a tool to modify the brain and unveil the structure of its internal representations i.e. the neural code. Together, it will form a unified framework to understand neural codes of learning and memory formation in valence-based situations. Our research will help establishing an emerging field of computational affective neuroscience, and our vision holds that Emotion is finally within the reach of neurophysiology.