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 paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement

“…We solved it numerically on digital computers, and that numerical solution was the only form in which algorithms intervened in the system. The numerical implementation of the model made use of Cedar (Lomp et al, 2016), an open source framework in which DFT models can be graphically assembled and interactively tuned. Cedar can be used to simulate robotic behavior, which was done for the results illustrated in this paper.…”

Scene memory and spatial inhibition in visual search

“…To simulate the experiment in the DFT model, activation time courses were numerically computed using the software framework cedar (Lomp et al, 2016). The visual stimuli, the timing, and the presentation procedure were the same as in the behavioral experiment and the same number of trials was simulated.…”

A neural integrator model for planning and value-based decision making of a robotics assistant

“…Weaker input "pre-activates" neural populations only, and the activity decays to resting level when the input is removed. For the building and numerically solving of complex control architectures consisting of many coupled DNFs (e.g., the HRI architectures in [7,19]) open source software frameworks such as Cedar [41] exist. They pro-vide graphical programming interfaces which allow the user to individually parameterize each field along the lines just discussed.…”