Gennady Livitz, Heather Ames, Ben Chandler, A. Gorchetchnikov, Jasmin Léveillé, Zlatko Vasilkoski, Massimiliano Versace, E. Mingolla, G. Snider, R. Amerson, Dick Carter, H. Abdalla, M. Qureshi
{"title":"Visually-guided adaptive robot (ViGuAR)","authors":"Gennady Livitz, Heather Ames, Ben Chandler, A. Gorchetchnikov, Jasmin Léveillé, Zlatko Vasilkoski, Massimiliano Versace, E. Mingolla, G. Snider, R. Amerson, Dick Carter, H. Abdalla, M. Qureshi","doi":"10.1109/IJCNN.2011.6033608","DOIUrl":null,"url":null,"abstract":"A neural modeling platform known as Cog ex Machina1 (Cog) developed in the context of the DARPA SyNAPSE2 program offers a computational environment that promises, in a foreseeable future, the creation of adaptive whole-brain systems subserving complex behavioral functions in virtual and robotic agents. Cog is designed to operate on low-powered, extremely storage-dense memristive hardware3 that would support massively-parallel, scalable computations. We report an adaptive robotic agent, ViGuAR4, that we developed as a neural model implemented on the Cog platform. The neuromorphic architecture of the ViGuAR brain is designed to support visually-guided navigation and learning, which in combination with the path-planning, memory-driven navigation agent - MoNETA5 - also developed at the Neuromorphics Lab at Boston University, should effectively account for a wide range of key features in rodents' navigational behavior.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2011 International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2011.6033608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
A neural modeling platform known as Cog ex Machina1 (Cog) developed in the context of the DARPA SyNAPSE2 program offers a computational environment that promises, in a foreseeable future, the creation of adaptive whole-brain systems subserving complex behavioral functions in virtual and robotic agents. Cog is designed to operate on low-powered, extremely storage-dense memristive hardware3 that would support massively-parallel, scalable computations. We report an adaptive robotic agent, ViGuAR4, that we developed as a neural model implemented on the Cog platform. The neuromorphic architecture of the ViGuAR brain is designed to support visually-guided navigation and learning, which in combination with the path-planning, memory-driven navigation agent - MoNETA5 - also developed at the Neuromorphics Lab at Boston University, should effectively account for a wide range of key features in rodents' navigational behavior.
在DARPA SyNAPSE2项目的背景下，一个被称为Cog ex Machina1 (Cog)的神经建模平台提供了一个计算环境，在可预见的未来，可以创建自适应的全脑系统，为虚拟和机器人代理提供复杂的行为功能。Cog被设计在低功耗、存储密度极高的记忆体硬件上运行，这些硬件将支持大规模并行、可扩展的计算。我们报告了一个自适应机器人代理，ViGuAR4，我们开发了一个在Cog平台上实现的神经模型。ViGuAR大脑的神经形态架构旨在支持视觉引导的导航和学习，它与同样由波士顿大学神经形态实验室开发的路径规划、记忆驱动的导航代理(MoNETA5)相结合，应该有效地解释啮齿动物导航行为的广泛关键特征。
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
