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Dynamic neural fields as a step toward cognitive neuromorphic architectures.

Yulia Sandamirskaya¹

¹Chair for Theory of Cognitive Systems, Institute for Neural Computation, Ruhr-University Bochum Bochum, Germany.

Frontiers in Neuroscience

|January 31, 2014

Summary

Dynamic Field Theory (DFT) unifies cognitive mechanisms using Dynamic Neural Fields (DNFs), equivalent to soft winner-take-all (WTA) networks. This research integrates DFT with neuromorphic hardware for embodied cognition, enabling autonomous learning and behavior.

Keywords:

autonomous learning cognitive neuromorphic architecture dynamic neural fields neural dynamics soft winner-take-all

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Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Dynamic Field Theory (DFT) models embodied cognition through neuronal dynamics.
Dynamic Neural Fields (DNFs) are the core computational elements in DFT.
DNFs are equivalent to soft winner-take-all (WTA) networks under specific dynamic constraints.

Purpose of the Study:

To revise and integrate established DFT mechanisms within a unified framework.
To identify novel DFT mechanisms suitable for neuromorphic VLSI implementation.
To demonstrate a unified architecture for embodied cognition and autonomous learning.

Main Methods:

Leveraging the equivalence between DFT and soft WTA networks.
Systematic revision and integration of existing DFT mechanisms.
Identifying and proposing novel computational and architectural mechanisms for neuromorphic implementation.

Main Results:

Established DFT mechanisms are revised and integrated into a cohesive framework.
Novel DFT mechanisms, including working memory stabilization and sensory-motor coupling, are identified for neuromorphic VLSI.
A unified architecture is proposed for generating behavior and autonomous learning.

Conclusions:

DFT mechanisms can be effectively implemented in neuromorphic hardware via WTA networks.
The proposed unified architecture supports embodied cognition, intentionality, and autonomous learning.
This work advances the integration of computational neuroscience models with neuromorphic engineering.