On the visual analytic intelligence of neural networks.

Visual oddity task was conceived to study universal ethnic-independent analytic intelligence of humans from a perspective of comprehension of spatial concepts. Advancements in artificial intelligence led to important breakthroughs, yet excelling at such abstract tasks remains challenging. Current approaches typically resort to non-biologically-plausible architectures with ever-growing models consuming substantially more energy than the brain. Motivated by the brain's efficiency and reasoning capabilities, we present a biologically inspired system that receives inputs from synthetic eye movements - reminiscent of saccades, and processes them with neuronal units incorporating dynamics of neocortical neurons. We introduce a procedurally generated visual oddity dataset to train an architecture extending conventional relational networks and our proposed system. We demonstrate that both approaches are capable of abstract problem-solving at high accuracy, and we uncover that both share the same essential underlying mechanism of reasoning in seemingly unrelated aspects of their architectures. Finally, we show that the biologically inspired network achieves superior accuracy, learns faster and requires fewer parameters than the conventional network.