On the visual analytic intelligence of neural networks.

ABSTRACT: 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

SUBMITTER: Wozniak S 

PROVIDER: S-EPMC10520053 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature