Reinforcement learning and its connections with neuroscience and psychology

Subramanian, Ajay; Chitlangia, Sharad; Baths, Veeky

doi:10.1016/j.neunet.2021.10.003

Cited by 27 publications

(13 citation statements)

References 171 publications

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“…We discuss only a few of the possible factors that influence the choice of agent here, however, the use of RL to model experiments in neuroscience and behavioral psychology has been reviewed in Subramanian et al ( 2022 ). Botvinick et al ( 2020 ) review Deep RL algorithms and their implications for neuroscience, and Bermudez-Contreras et al ( 2020 ) focus specifically on RL models of spatial navigation.…”

Section: Discussionmentioning

confidence: 99%

“…We discuss only a few of the possible factors that influence the choice of agent here, however, the use of RL to model experiments in neuroscience and behavioral psychology has been reviewed in Subramanian et al (2022). Botvinick et al (2020) CoBeL-RL can also be easily integrated with other existing RL simulation frameworks for machine learning.…”

Section: Flexibility and Extensibilitymentioning

confidence: 99%

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CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Diekmann

Vijayabaskaran²,

Zeng³

et al. 2023

Front. Neuroinform.

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Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development has been fueled by advances in understanding the role of RL in both the brain and artificial intelligence. However, while in machine learning a set of tools and standardized benchmarks facilitate the development of new methods and their comparison to existing ones, in neuroscience, the software infrastructure is much more fragmented. Even if sharing theoretical principles, computational studies rarely share software frameworks, thereby impeding the integration or comparison of different results. Machine learning tools are also difficult to port to computational neuroscience since the experimental requirements are usually not well aligned. To address these challenges we introduce CoBeL-RL, a closed-loop simulator of complex behavior and learning based on RL and deep neural networks. It provides a neuroscience-oriented framework for efficiently setting up and running simulations. CoBeL-RL offers a set of virtual environments, e.g., T-maze and Morris water maze, which can be simulated at different levels of abstraction, e.g., a simple gridworld or a 3D environment with complex visual stimuli, and set up using intuitive GUI tools. A range of RL algorithms, e.g., Dyna-Q and deep Q-network algorithms, is provided and can be easily extended. CoBeL-RL provides tools for monitoring and analyzing behavior and unit activity, and allows for fine-grained control of the simulation via interfaces to relevant points in its closed-loop. In summary, CoBeL-RL fills an important gap in the software toolbox of computational neuroscience.

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Section: Discussionmentioning

confidence: 99%

“…We discuss only a few of the possible factors that influence the choice of agent here, however, the use of RL to model experiments in neuroscience and behavioral psychology has been reviewed in Subramanian et al (2022). Botvinick et al (2020) CoBeL-RL can also be easily integrated with other existing RL simulation frameworks for machine learning.…”

Section: Flexibility and Extensibilitymentioning

confidence: 99%

CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Diekmann

Vijayabaskaran²,

Zeng³

et al. 2023

Front. Neuroinform.

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“…RL aims to find an optimal control policy using a reward function and has been used in process control [10]. To cope with large state spaces, traditional RL has been combined with deep learning, leading towards deep reinforcement learning (DRL) [11]. Model-free DRL is a trial-and-error approach where the RL agent repeatedly interacts with the environment.…”

Section: Background and Related Workmentioning

confidence: 99%

Using Deep Reinforcement Learning for Zero Defect Smart Forging

Kassler

Ahmed

et al. 2022

Advances in Transdisciplinary Engineering

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Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the environment. An essential reason for material waste is a low degree of automation, especially in industries that currently have a low degree of digitalization, such as steel forging. Those industries typically rely on heavy and old machinery such as large induction ovens that are mostly controlled manually or using well-known recipes created by experts. However, standard recipes may fail when unforeseen events happen, such as an unplanned stop in production, which may lead to overheating and thus material degradation during the forging process. In this paper, we develop a digital twin-based optimization strategy for the heating process for a forging line to automate the development of an optimal control policy that adjusts the power for the heating coils in an induction oven based on temperature data observed from pyrometers. We design a digital twin-based deep reinforcement learning (DTRL) framework and train two different deep reinforcement learning (DRL) models for the heating phase using a digital twin of the forging line. The twin is based on a simulator that contains a heating transfer and movement model, which is used as an environment for the DRL training. Our evaluation shows that both models significantly reduce the temperature unevenness and can help to automate the traditional heating process.

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“…In [ 19 ], the authors conducted a review on how neuroscience served as the source of guidance in constructing ANNs to deep networks and their further transformations. In [ 20 ], the authors reviewed how reinforcement learning correlates with neuroscience and psychology. In [ 21 ], the authors discussed the sharing relationship between AI and neuroscience by highlighting the intersections of biological vision and AI vision networks.…”

Section: Introductionmentioning

confidence: 99%

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Surianarayanan

Lawrence

Raj

et al. 2023

Sensors

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Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

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Reinforcement learning and its connections with neuroscience and psychology

Cited by 27 publications

References 171 publications

CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Using Deep Reinforcement Learning for Zero Defect Smart Forging

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

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