Sameness/difference spiking neural circuit as a relational concept precursor model: A bio-inspired robotic implementation

This paper presents a bio-inspired virtual and physical robotic model of sameness/difference (SD) abstract relations investigated through associative learning tasks. Considering that the invertebrate bees presumably possess this higher cognitive ability with a relatively tiny brain (Giurfa, Zhang, Jenett, Menzel, & Srinivasan, 2001), an hypothesis of a neural correlate may hold as a minimalist cellular circuit. In order to simulate it, we implemented the model using an artificial spiking neural network (SNN) acting as a robot's brain-controller and a reward-modulated spike-timing dependent plasticity as the learning rule. The model is tested with different operant conditioning procedures of delayed-matching-to-sample and delayed-non-matching-to-sample tasks from dual choices of colors. Apart SD, the color and side simpler associative rules also co-exist. This allows the robot to learn in different scenarios, not knowing the rule to come. Therefore, we showed that depending on its action and the applied reinforcing rules (SD, color or side), the robot dynamically learns any of it, independently and one after the other, all supported by a single SNN. We believe that this SD SNN model could be used as a precursor base to understand and build a generic form of a relational concept mechanism.