BRAIN EAGER: Integrative Cross-Modal and Cross-Species Brain Models: Motivation and Reward

点击翻译按钮获取中文摘要

Motivation translates goals into action, and significantly impacts cognition along several dimensions. The motivation for reward biases attention, perception, and memory, and enhances learning, with effects evidenced behaviorally as well as in specific brain regions. However, given such broad effects of reward motivation, the question remains: how does reward motivation propagate throughout the brain and how does it dynamically change the greater neural circuitry to prime us to behave appropriately? In order to answer these questions we develop statistical models for the effect of motivation on neural circuitry, which combine data recorded using different instruments across a variety of species. A fuller understanding of the neural effects of motivation would elucidate how it impacts important cognitive processes, yielding insights that are useful for better performance in various arenas, from education to therapy. In order to gain a more complete understanding of the neural network dynamics underlying the behavioral and cognitive effects of motivation, it is necessary to integrate research in human subjects, and in animal models. While extensive and crucial research has been carried out on reward motivation separately in humans and animal models, there is a clear need for improved translation across species. An overarching analytical framework for translation is extremely important, due to the complexity of the problems being addressed, and to leverage the strengths offered by each species and available technology. The aim of this work is the development of dynamic, hierarchical Bayesian models to discover functional neural networks that can translate across species and data collection modalities. Bayesian models of human behavior, and Bayesian machine learning inspired methods for neural network modeling, have been extremely successful, making Bayesian methods fertile ground for explorations into translational neural network discovery.