AngoraPy:用于模拟拟人目标驱动传感器运动系统的 Python 工具包

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Tonio Weidler, Rainer Goebel, M. Senden
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引用次数: 0

摘要

目标驱动的深度学习越来越多地补充了计算神经科学中的经典建模方法。深度神经网络作为大脑模型的优势在于,它们能够自主学习解决复杂和生态有效任务所需的连接性,而无需人工设计或假设驱动的连接模式。因此,目标驱动模型可以根据网络的生物对应物的宏观和中观解剖学特性,生成有关皮层处理的神经计算假设。虽然目标驱动建模在感知神经科学领域已经非常普遍,但由于训练包含感觉-动作闭环的模型所需的方法非常复杂,其在感觉运动领域的应用目前受到了阻碍。本文介绍的 AngoraPy 是一个 Python 库,它为研究人员提供了训练复杂的递归卷积神经网络所需的工具,以模拟人类的感觉运动系统,从而缓解了这一障碍。为了使这一工具包的技术细节更加平易近人,本文在理论论述的同时还提供了一个训练手持物体操作递归玩具模型的示例。对各种经典、三维机器人和拟人控制任务的广泛基准测试证明了 AngoraPy 对各种任务的普遍适用性。再加上它能够自适应地处理定制架构,该工具包的灵活性证明了它在目标驱动的传感器运动建模方面的强大功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems
Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing that are grounded in macro- and mesoscopic anatomical properties of the network's biological counterpart. Whereas, goal-driven modeling is already becoming prevalent in the neuroscience of perception, its application to the sensorimotor domain is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. This paper describes AngoraPy, a Python library that mitigates this obstacle by providing researchers with the tools necessary to train complex recurrent convolutional neural networks that model the human sensorimotor system. To make the technical details of this toolkit more approachable, an illustrative example that trains a recurrent toy model on in-hand object manipulation accompanies the theoretical remarks. An extensive benchmark on various classical, 3D robotic, and anthropomorphic control tasks demonstrates AngoraPy's general applicability to a wide range of tasks. Together with its ability to adaptively handle custom architectures, the flexibility of this toolkit demonstrates its power for goal-driven sensorimotor modeling.
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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