Abstract
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments.
DOI
10.1016/j.neunet.2016.09.001
Publication Date
2017-01-01
Publication Title
Neural Networks
Volume
85
Publisher
Elsevier BV
ISSN
0893-6080
Embargo Period
2024-11-22
First Page
85
Last Page
105
Recommended Citation
Carmantini, G., beim Graben, P., Desroches, M., & Rodrigues, S. (2017) 'A modular architecture for transparent computation in recurrent neural networks', Neural Networks, 85, pp. 85-105. Elsevier BV: Available at: https://doi.org/10.1016/j.neunet.2016.09.001
Comments
publisher: Elsevier articletitle: A modular architecture for transparent computation in recurrent neural networks journaltitle: Neural Networks articlelink: http://dx.doi.org/10.1016/j.neunet.2016.09.001 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved.