Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar.

ABSTRACT: Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this

SUBMITTER: Lomp O 

PROVIDER: S-EPMC5089998 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature