Working memory is essential: it serves to guide intelligent behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations ca...
In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural netwo...
International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Oct 14, 2020
BACKGROUND: Theoretical and empirical work suggest that addictive drugs potentiate dopaminergic reinforcement learning signals and disrupt the reward function of its neural targets, including the anterior midcingulate cortex (aMCC) and the basal gang...
The problem of common sense remains a major obstacle to progress in artificial intelligence. Here, we argue that common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the und...
Discovering new treatments and personalizing existing ones is one of the major goals of modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the realization of advanced intelligent systems able to learn about clinica...
Soft actuators, as an important part of soft robotics, have attracted significant attention due to their inherent compliance, flexibility and safety. However, low capacity in force and load limits their applications. Prestored elastic energy can impr...
Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely bee...
Despite active research on trading systems based on reinforcement learning, the development and performance of research methods require improvements. This study proposes a new action-specialized expert ensemble method consisting of action-specialized...
The emergence of powerful artificial intelligence (AI) is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning in tasks such as image classification. Ho...
Neural networks : the official journal of the International Neural Network Society
Jun 16, 2020
Similar to real snakes in nature, the flexible trunks of snake-like robots enhance their movement capabilities and adaptabilities in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degr...
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