Sensori-motor networks vs neural networks for visual stimulus prediction

机译：感觉运动网络与神经网络的视觉刺激预测

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摘要

This paper focuses on a recently developed special type of biologically inspired architecture, which we denote as a sensori-motor network, able to co-develop sensori-motor structures directly from the data acquired by a robot interacting with its environment. Such networks learn efficient internal models of the sensori-motor system, developing simultaneously sensor and motor representations (receptive fields) adapted to the robot and surrounding environment. In this paper we compare this sensori-motor network with a conventional neural network in the ability to create efficient predictors of visuomotor relationships. We confirm that the sensori-motor network is significantly more efficient in terms of required computations and is more precise (less prediction error) than the linear neural network in predicting self induced visual stimuli.

机译：本文关注于最近开发的一种特殊类型的受生物启发的体系结构，我们将其表示为感觉运动网络，它能够直接从机器人与其环境交互作用所获得的数据中共同开发感觉运动结构。这样的网络学习传感器-电机系统的有效内部模型，同时开发适合于机器人和周围环境的传感器和电机表示（接受场）。在本文中，我们将这种感觉运动网络与传统的神经网络进行了比较，以创建视觉运动关系的有效预测因子。我们确认，在预测自我诱发的视觉刺激方面，感觉运动网络在所需的计算方面明显更有效，并且比线性神经网络更为精确（预测误差较小）。

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来源
《The Fourth joint IEEE international conferences on development and learning and epigenetic robotics》|2014年|287-292|共6页
会议地点 Genoa(IT)
作者
Santos Ricardo; Ferreira Ricardo; Cardoso Angelo; Bernardino Alexandre;
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作者单位

Institute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Artificial neural networks; Image reconstruction; Optimization; Organizations; Robot sensing systems; Visualization; Stimulus prediction; neural networks; sensori-motor maps; visual and motor receptive fields;

机译：人工神经网络;图像重建;优化;组织;机器人传感系统;可视化;刺激预测;神经网络;感觉运动图;视觉和运动感受野;;

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Sensori-motor networks vs neural networks for visual stimulus prediction

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