title: A neuron-inspired computational architecture for spatiotemporal visual processing: real-time visual sensory integration for humanoid robots.
abstract: In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture's focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations - making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to ∼+6%).
authors.affiliation: Intstitute for Cognitive Systems (ICS), Technische Universität München, Munich, Germany, andreas.holzbach@tum.de.
authors.forename: Andreas
authors.initials: A
authors.lastname: Holzbach
authors.forename: Gordon
authors.initials: G
authors.lastname: Cheng
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009474:Neurons; D012371:Robotics; D012984:Software; D014793:Visual Cortex; D014796:Visual Perception