Design of spiking rate coded logic gates for C. elegans inspired contour tracking

Abstract

Bio-inspired energy efficient control is a frontier for autonomous navigation and robotics. Binary input-output neuronal logic gates are demonstrated in literature – while analog input-output logic gates are needed for continuous analog real-world control. In this paper, we design logic gates such as AND, OR and XOR using networks of Leaky Integrate-and-Fire neurons with analog rate (frequency) coded inputs and output, where refractory period is shown to be a critical knob for neuronal design. To demonstrate our design method, we present contour tracking inspired by the chemotaxis network of the worm C. elegans and demonstrate for the first time an end-to-end Spiking Neural Network (SNN) solution. First, we demonstrate contour tracking with an average deviation equal to literature with non-neuronal logic gates. Second, 2x improvement in tracking accuracy is enabled by implementing latency reduction leading to state of the art performance with an average deviation of 0.55% from the set-point. Third, a new feature of local extrema escape is demonstrated with an analog XOR gate, which uses only 5 neurons – better than binary logic neuronal circuits. The XOR gate demonstrates the universality of our logic scheme. Finally, we demonstrate the hardware feasibility of our network based on experimental results on 32 nm Silicon-on-Insulator (SOI) based artificial neurons with tunable refractory periods. Thus, we present a general framework of analog neuronal control logic along with the feasibility of their implementation in mature SOI technology platform for autonomous SNN navigation controller hardware.