Robot

Competitive learning

Synaptic plasticity

Neuroscience

Spike (software development)

Biology

Spike-timing-dependent plasticity

Associative learning

Receptor

Biochemistry

Computer science

Hebbian theory

Psychology

Mobile robot

Spiking neural network

Artificial intelligence

Software engineering

Chemistry

Artificial neural network

Machine learning

Frontiers in Neuroscience

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 key problem is in the design of robust learning algorithms aimed at building a "living computer" on the basis of 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 exploring 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 the 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. They also admit the use of memristive devices for hardware implementation.

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