Lobov Sergey A, Mikhaylov Alexey N, Shamshin Maxim, Makarov Valeri A, Kazantsev Victor B
Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia.
Front Neurosci. 2020 Feb 26;14:88. doi: 10.3389/fnins.2020.00088. eCollection 2020.
Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a "living computer" based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.
开发控制移动机器人的脉冲神经网络（SNN）是计算神经科学和人工智能领域的现代挑战之一。这种网络作为生物神经网络的复制品，预计比传统人工神经网络（ANN）具有更高的计算潜力。关键问题在于设计强大的学习算法，旨在构建基于SNN的“活体计算机”。在此，我们提出一种简单的SNN，其配备了以脉冲时间依赖可塑性（STDP）形式存在的赫布规则。该SNN通过利用STDP的空间特性来实现关联学习。我们表明，由SNN控制的乐高机器人能够展现经典条件作用和操作性条件作用。SNN中脉冲传导通路的竞争在建立神经连接关联方面起着基础性作用。它会根据刺激的变化用新的关联取代不相关的关联。因此，当环境变化时，机器人具备重新学习的能力。所提出的SNN和刺激协议可在培养神经元的过程中进一步增强和测试，并允许使用忆阻器件进行硬件实现。
