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Fakultät für Informatik
Informatik-Kolloquien

Informatik-Kolloquien

288. Informatik-Kolloquium

Professur Künstliche Intelligenz

Gastvortrag

Prof. Dr. Heiko Neumann

Ulm University
Institute of Neural Information Processing

"Cortical routines - from experimental data to neuromorphic brain-like computation"

Dienstag, 30.10.2018
15:00 Uhr, Straße der Nationen 62, Böttcher-Bau, 1/336 (neu: A12.336)

 
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Abstract:

A fundamental task of sensory processing is to group feature items that form a perceptual unit, e.g., shapes or objects, and to segregate them from other objects and the background. In the talk a conceptual framework is provided, which explains how perceptual grouping at early as well as higher-level cognitive stages may be implemented in cortex. Different grouping mechanisms are implemented which are attuned to basic features and feature combinations and evaluated along the forward sweep of stimulus processing. More complex combinations of items require integration of contextual information along horizontal and feedback connections to bind neurons in distributed representations via top-down response enhancement. The modulatory influence generated by such flexible dynamic grouping and prediction mechanisms is time-consuming and is primarily sequentially organized. The coordinated action of feedforward, feedback, and lateral processing motivates the view that sensory information, such as visual and auditory features, is efficiently combined and evaluated within a multiscale cognitive blackboard architecture.
This architecture provides a framework to explain form and motion detection and integration, higher-order processing of articulated motion, as well as scene segmentation and figure-ground segregation of spatio-temporal inputs which are labelled by enhanced neuronal responses. In addition to the activation dynamics in the model framework, steps are demonstrated how unsupervised learning mechanisms can be incorporated to automatically build early- and mid-level visual representations. Finally, it is demonstrated that the canonical circuit architecture can be mapped onto neuromorphic chip technology facilitating low-energy non-von Neumann computation.

Work supported by DFG & Baden-Württemberg Foundation

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