Revisiting the XOR problem using spiking neural networks in a robotic context

The XOR problem still represents a challenge in the study of cognition and computational simulations, since its precise neural substrate remains to be discovered in natural organisms that succeeded in the task. This paper focuses on this particular problem, in a context of a spiking neural network acting as a robot's brain controller. Hence, the ability to solve ambiguous nonlinear problems from a neuro-robotic model may contribute to understand the natural function as well as proposing an artificial embedded solution. By using the same core components in both virtual and real environments and from operant conditioning procedures, the robot was able to correctly learn the XOR rule. Beyond resolving a XOR-like problem within real world constraints, this bio-inspired neural circuit also integrates related associative learning scenarios. Particularly, this study explores the consequences of passing from a 2-bit to a 3-bit task, analyzing the effects on the overall neural network, its neurons and synapses. The experiment consists in a robot choosing between a left or right action, depending on an image shown to it and seen by its camera. Within few trials, the robot was able to successfully learn all possible rewarding rules. Moreover, behavioral adaptation was shown by switching the rewarding rules once the robot had learned them.

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