Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot

Sergey A Lobov; Alexey N Mikhaylov; Maxim Shamshin; Valeri A Makarov; Victor B Kazantsev

doi:10.3389/fnins.2020.00088

Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot

Front Neurosci. 2020 Feb 26:14:88. doi: 10.3389/fnins.2020.00088. eCollection 2020.

Authors

Sergey A Lobov^{1

2}, Alexey N Mikhaylov¹, Maxim Shamshin¹, Valeri A Makarov^{1

3}, Victor B Kazantsev^{1

2}

Affiliations

¹ Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
² Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia.
³ Instituto de Matemática Interdisciplinar, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, Madrid, Spain.

Abstract

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.

Keywords: learning; memristive devices; neural competition; neuroanimat; neurorobotics; spike-timing-dependent plasticity; spiking neural networks; synaptic competition.