Neural dynamics of sequence generation and behavioral organization
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1
Ruhr-Universität Bochum, Institut für Neuroinformatik, Germany
Recently, we have introduced a neural-dynamic model for sequence generation within the framework of Dynamic Field Theory (DFT). In this model, the sequence consists of stable states of the dynamics of neural fields, defined over one [1] or several [2] characteristic, behaviorally relevant dimensions. The sequential transitions are triggered by a neural-dynamic representation of the condition of satisfaction, which detects a match between the expected perceptual state at the end of a sequential action and the actual perceptual input. We have shown that this neural-dynamic model for sequence generation is robust against noise in neural representations and sensory inputs as well as against variable duration of actions in a behavioral sequence.
Here, we demonstrate how the DFT framework for sequence generation may be extended to accommodate task-dependent constraints on the order of actions within a sequence. In particular, we develop a model for behavioral organization of neural-dynamic modules, or elementary behaviors, which guide the behavior of an agent. Earlier attempts to use dynamical systems to solve the problem of behavioral organization [3] revealed its complexity and inherent instability. Formulating the whole model -- both the sensory-motor systems and the task-driven constraints -- in the same framework of dynamic neural fields demonstrates that the rules of behavioral organization [4] may be embedded in continuous neuronal dynamics. As the controlled instabilities lead to sequential switches between the stable dynamical states, the rule-based sequencing emerges on-the-fly during the behavior from the interplay of the neuronal activation dynamics with the perceptual inputs. We implemented the model on a robot to demonstrate how it may be embodied in an acting agent.
Acknowledgements
The authors acknowledge support from the German Federal Ministry of Education and Research within the National Network Computational Neuroscience - Bernstein Focus: “Learning behavioral models: From human experiment to technical assistance”, grant FKZ 01GQ0951
References
[1] Sandamirskaya, Y. & Schöner, G. An embodied account of serial order: How instabilities drive sequence generation. Neural Networks, 2010, 23, 1164-1179
[2] Sandamirskaya, Y. & Schöner, G. Serial order in an acting system: a multidimensional dynamic neural fields implementation. Development and Learning, 2010. ICDL 2010. 9th IEEE International Conference on, 2010
[3] Steinhage, A. & Schöner, G. Schenker P S, M. G. T. (ed.) Dynamical Systems for the Behavioral Organization of Autonomous Robot Navigation. Sensor Fusion and Decentralized Control in Robotic Systems: Proceedings of SPIE, SPIE-publishing, 1998, 3523, 169-180
[4] Brooks, R. A. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 1986, RA-2, 12-23
Keywords:
action selection,
Cognitive Robotics,
Dynamic Neural Fields,
sequence generation
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Poster
Topic:
neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords)
Citation:
Sandamirskaya
Y,
Richter
M and
Schöner
G
(2011). Neural dynamics of sequence generation and behavioral organization.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00041
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Received:
23 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Dr. Yulia Sandamirskaya, Ruhr-Universität Bochum, Institut für Neuroinformatik, Bochum, 44780, Germany, sandayci@rub.de