A Spiking Neural Network Based Autonomous Reinforcement Learning Model and Its Application in Decision Making

2016 ◽  
pp. 125-137
Author(s):  
Guixiang Wang ◽  
Yi Zeng ◽  
Bo Xu
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Model ◽  
Spiking Neural Network ◽  
Reinforcement Learning Model

Imaginative Reinforcement Learning: Computational Principles and Neural Mechanisms

2017 ◽  
Vol 29 (12) ◽  
pp. 2103-2113 ◽  
Author(s):  
Samuel J. Gershman ◽  
Jimmy Zhou ◽  
Cody Kommers
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Internal Model ◽  
Learning Model ◽  
Neural Mechanisms ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
The Impact ◽  
To Receive ◽  
Reinforcement Learning Model

Imagination enables us not only to transcend reality but also to learn about it. In the context of reinforcement learning, an agent can rationally update its value estimates by simulating an internal model of the environment, provided that the model is accurate. In a series of sequential decision-making experiments, we investigated the impact of imaginative simulation on subsequent decisions. We found that imagination can cause people to pursue imagined paths, even when these paths are suboptimal. This bias is systematically related to participants' optimism about how much reward they expect to receive along imagined paths; providing feedback strongly attenuates the effect. The imagination effect can be captured by a reinforcement learning model that includes a bonus added onto imagined rewards. Using fMRI, we show that a network of regions associated with valuation is predictive of the imagination effect. These results suggest that imagination, although a powerful tool for learning, is also susceptible to motivational biases.

A whole process interpretable and multi-modal deep reinforcement learning for diagnosis and analysis of Alzheimer's disease

2021 ◽  
Author(s):  
Quan Zhang ◽  
Qian Du ◽  
Guohua Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Reinforcement Learning ◽  
Learning Model ◽  
Imaging Biomarkers ◽  
Unknown Etiology ◽  
Reinforcement Learning Model ◽  
Individual Information

Abstract Objective Alzheimer's disease (AD), a common disease of the elderly with unknown etiology, has been bothering many people, especially with the aging of the population and the younger trend of this disease. Current AI methods based on individual information or magnetic resonance imaging (MRI) can solve the problem of diagnostic sensitivity and specificity, but still face the challenges of interpretability and clinical feasibility. In this study, we propose an interpretable multimodal deep reinforcement learning model for inferring pathological features and diagnosis of Alzheimer's disease. Approach First, for better clinical feasibility, the compressed-sensing MRI image is reconstructed by an interpretable deep reinforcement learning model. Then, the reconstructed MRI is input into the full convolution neural network to generate a pixel-level disease probability of risk map (DPM) of the whole brain for Alzheimer's disease. Finally, the DPM of important brain regions and individual information are input into the attention-based fully deep neural network to obtain the diagnosis results and analyze the biomarkers. 1349 multi-center samples were used to construct and test the model. Main Results Finally, the model obtained 99.6%±0.2, 97.9%±0.2, and 96.1%±0.3 area under curve (AUC) in ADNI, AIBL, and NACC, respectively. The model also provides an effective analysis of multimodal pathology and predicts the imaging biomarkers on MRI and the weight of each individual information. In this study, a deep reinforcement learning model was designed, which can not only accurately diagnose AD, but also analyze potential biomarkers. Significance In this study, a deep reinforcement learning model was designed. The model builds a bridge between clinical practice and artificial intelligence diagnosis and provides a viewpoint for the interpretability of artificial intelligence technology.

scholarly journals Decision-Making Processes Related to Perseveration Are Indirectly Associated With Weight Status in Children Through Laboratory-Assessed Energy Intake

2021 ◽  
Vol 12 ◽  
Author(s):  
Bari A. Fuchs ◽  
Nicole J. Roberts ◽  
Shana Adise ◽  
Alaina L. Pearce ◽  
Charles F. Geier ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Food Intake ◽  
Energy Intake ◽  
Weight Status ◽  
Eating Behaviors ◽  
Positive Outcome ◽  
Learning Model ◽  
Decision Making Processes ◽  
Reinforcement Learning Model

Decision-making contributes to what and how much we consume, and deficits in decision-making have been associated with increased weight status in children. Nevertheless, the relationships between cognitive and affective processes underlying decision-making (i.e., decision-making processes) and laboratory food intake are unclear. We used data from a four-session, within-subjects laboratory study to investigate the relationships between decision-making processes, food intake, and weight status in 70 children 7-to-11-years-old. Decision-making was assessed with the Hungry Donkey Task (HDT), a child-friendly task where children make selections with unknown reward outcomes. Food intake was measured with three paradigms: (1) a standard ad libitum meal, (2) an eating in the absence of hunger (EAH) protocol, and (3) a palatable buffet meal. Individual differences related to decision-making processes during the HDT were quantified with a reinforcement learning model. Path analyses were used to test whether decision-making processes that contribute to children’s (a) expected value of a choice and (b) tendency to perseverate (i.e., repeatedly make the same choice) were indirectly associated with weight status through their effects on intake (kcal). Results revealed that increases in the tendency to perseverate after a gain outcome were positively associated with intake at all three paradigms and indirectly associated with higher weight status through intake at both the standard and buffet meals. Increases in the tendency to perseverate after a loss outcome were positively associated with EAH, but only in children whose tendency to perseverate persistedacross trials. Results suggest that decision-making processes that shape children’s tendencies to repeat a behavior (i.e., perseverate) are related to laboratory energy intake across multiple eating paradigms. Children who are more likely to repeat a choice after a positive outcome have a tendency to eat more at laboratory meals. If this generalizes to contexts outside the laboratory, these children may be susceptible to obesity. By using a reinforcement learning model not previously applied to the study of eating behaviors, this study elucidated potential determinants of excess energy intake in children, which may be useful for the development of childhood obesity interventions.

scholarly journals Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task

2021 ◽  
Vol 134 ◽  
pp. 1-10
Author(s):  
Xiaohan Zhang ◽  
Lu Liu ◽  
Guodong Long ◽  
Jing Jiang ◽  
Shenquan Liu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Episodic Memory ◽  
Learning Model ◽  
Reinforcement Learning Model

scholarly journals Modeling individual variation in visual search with reinforcement learning

2020 ◽  
Author(s):  
Ben Lonnqvist ◽  
Micha Elsner ◽  
Amelia R. Hunt ◽  
Alasdair D F Clarke
Keyword(s):  
Visual Acuity ◽  
Reinforcement Learning ◽  
Visual Search ◽  
Individual Variation ◽  
Learning Model ◽  
Participant Preferences ◽  
Search Experiment ◽  
Reinforcement Learning Model ◽  
Specific Learning

Experiments on the efficiency of human search sometimes reveal large differences between individual participants. We argue that reward-driven task-specific learning may account for some of this variation. In a computational reinforcement learning model of this process, a wide variety of strategies emerge, despite all simulated participants having the same visual acuity. We conduct a visual search experiment, and replicate previous findings that participant preferences about where to search are highly varied, with a distribution comparable to the simulated results. Thus, task-specific learning is an under-explored mechanism by which large inter-participant differences can arise.

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