Modeling Social Cognition and its Neurologic Deficits with Artificial Neural Networks

Laurent P. Mertens
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Abstract

Artificial Neural Networks (ANNs) are computer models loosely inspired by the functioning of the human brain. They are the state-of-the-art method for tackling a variety of Artificial Intelligence (AI) problems, and an increasingly popular tool in neuroscientific studies. However, both domains pursue different goals: in AI, performance is key and brain resemblance is incidental, while in neuroscience the aim is chiefly to better understand the brain. This PhD is situated at the intersection of both disciplines. Its goal is to develop ANNs that model social cognition in neurotypical individuals, and that can be altered in a controlled way to exhibit behavior consistent with individuals with one of two clinical conditions, Autism Spectrum Disorder and Frontotemporal Dementia.
用人工神经网络模拟社会认知及其神经缺陷
人工神经网络(ann)是一种受人脑功能启发的计算机模型。它们是解决各种人工智能(AI)问题的最先进方法,也是神经科学研究中越来越受欢迎的工具。然而,这两个领域追求不同的目标:在人工智能中,性能是关键,大脑相似性是偶然的,而在神经科学中,目标主要是更好地理解大脑。这个博士学位位于两个学科的交叉点。其目标是开发神经正常个体的社会认知模型的人工神经网络,并且可以以可控的方式改变,以显示与患有自闭症谱系障碍和额颞叶痴呆两种临床病症之一的个体一致的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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