Psychological Function in Computational Models of Neural Networks | CU Experts

AbstractThe overarching goal of cognitive neuroscience is to understand how the brain gives rise to thought. We describe how computer models can help accomplish this goal by simulating networks of interacting neurons and measuring cognitive function in these networks at the same time. The variables in these networks can be easily manipulated and observed, so that their effects on cognitive processes can be clearly understood. We provide an up‐to‐date review of some of the core principles and prominent applications of computational models in cognitive neuroscience. We begin with a summary of some of the main questions confronting computational modelers in cognitive neuroscience, ranging from the basic properties of individual neurons and networks thereof, up to issues surrounding how networks of neurons can subserve complex cognitive functions. We then discuss provisional answers to these questions, showing how they apply to a range of empirical data in domains including perception, learning and memory, and higher‐level cognition. Throughout, and in closing, we discuss challenges to neural network models.

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