Abstract
In this paper we describe an autonomous mobile robot whose sensory-motor connection were made by a three-layered spiking neural network (SNN) with only one hidden-layer neuron that makes synaptic contacts on motor neurons with synapses having spike timing-dependent plasticity (STDP) and presynaptic modulation, and we analyzed the roles of STDP for the autonomous behavior in environment. STDP is known as a kind of Hebbian rules with which the synaptic weights are increased or decreased in accordance with the relative timing of pre- and postsynaptic action potentials. An excitatory synapse may become an inhibitory one, or vise versa, in the STDP. We used one of the SNN models, called the spike response model (SRM), in which the neurons generate spikes, and a spike at presynaptic site generates a delayed, prolonged post synaptic potential (PSP). The postsynaptic neuron fires when the sum of PSPs becomes over a threshold. Once a neuron fires, it goes into a refractory period during which a larger input is necessary to generate the following spike. We considered a mobile robot with left and right front proximity sensors, and left and right wheels driven by independent motors. Each sensor value was converted to the probability of spike generation by the sensory neuron. The outputs from left and right sensory neurons were connected to one hidden-layer neuron with fixed synapses. The output of the hidden-layer neuron makes synaptic contacts on left and right motor neurons, and these synapses have the properties of STDP in regard to the presynaptic modulation signals from sensory neurons. The spiking rates were converted to analog values to drive the corresponding motors. We implemented this SNN in a miniature mobile robot Khepera. In a given environment, it gradually organized the weights and acquired the navigation and obstacle-avoidance behavior. Modeling and experimental data analyses showed that one hidden-layer neuron is sufficient for the task, and that such SNN is suitable for the navigation of mobile robots.
