Neurotechnologies to Manage a Robotic System : (Keynote paper)

M. Talanov
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Abstract

In my talk I will discuss the AI and brain-computer interface (BCI) technologies overview in the context of integration with robotic and biological systems starting from classical Rosenblatt perceptron till current breaking through technologies of BCI that could change the scientific outlook of the field. This is what we currently broadly call now AI and neurotechnology. Two bright examples are ResNet inspired by the projections of a mammalian cortical column and U-Net with close to ResNet ideas and topology. The other interesting cortical column inspired NN architecture is hierarchical temporal memory introduced by the Numenta company. These brain or neuro inspired approaches are widely used in machine learning, computer vision, natural language processing, path planning and broader in robotics.In the neurotechnology field I want to reference a lot of interesting and projects dedicated to BCI. Starting from works of Miguel Nicolelis where researchers connected the robotic hand via a computer system with a motor cortex of a monkey and later Lebedev group demonstrated the adaptation of a mammalian brain to create the representation of robotic limb extending biological limbs. I want to pay the special attention to works of Kevin Warwick that implemented invasive nervous system to nervous system interface and the robot managed by neurons of a rat brain developed and trained during the experiment.The commercial company Neuralink recently demonstrated the most advanced 1536-channel BCI exploiting wireless Blue-tooth interface to record and process a brain activity. Elon Musk in his Neuralink presentation last year demanded that one of the nearest goals of the company is spinal cord injury and the recovery of the lost motor control of limbs. Several projects in neurorehabilitation targeted to the motor control demonstrated recently important success using medical programmed neurostimulators.The perspective but currently in early state of the development is the direction of neurosimulation based models that could be used for the robotic system control (exoskeleton) or as neuroprosthesis as the part of closed loop system for example projects simulating the spinal cord, where I can see interesting future opportunities both in medicine and robotics.
管理机器人系统的神经技术:(主题论文)
在我的演讲中,我将讨论与机器人和生物系统集成背景下的人工智能和脑机接口(BCI)技术概述,从经典的Rosenblatt感知器开始,直到目前可能改变该领域科学前景的BCI突破性技术。这就是我们现在所说的人工智能和神经技术。两个很好的例子是受哺乳动物皮质柱投影启发的ResNet和接近ResNet思想和拓扑结构的U-Net。另一个有趣的受皮层柱启发的神经网络架构是由Numenta公司引入的分层时间记忆。这些大脑或神经启发的方法被广泛应用于机器学习、计算机视觉、自然语言处理、路径规划以及更广泛的机器人领域。在神经技术领域,我想参考很多有趣的和致力于BCI的项目。从Miguel Nicolelis的研究开始,研究人员通过计算机系统将机器人手与猴子的运动皮层连接起来,后来Lebedev小组展示了哺乳动物大脑的适应性,以创造机械肢体的代表,延伸生物肢体。我想特别关注Kevin Warwick的作品,他实现了侵入性神经系统对神经系统的接口,以及在实验中开发和训练的由大鼠大脑神经元管理的机器人。商业公司Neuralink最近展示了最先进的1536通道脑机接口,利用无线蓝牙接口来记录和处理大脑活动。埃隆·马斯克(Elon Musk)去年在Neuralink的演讲中要求,该公司最近的目标之一是脊髓损伤和恢复失去的肢体运动控制。近年来,一些以运动控制为目标的神经康复项目使用程序化神经刺激器取得了重要的成功。目前处于早期发展阶段的前景是基于神经模拟的模型,可以用于机器人系统控制(外骨骼)或作为闭环系统的一部分的神经假体,例如模拟脊髓的项目,我可以在医学和机器人技术中看到有趣的未来机会。
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