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Conference Paper

Artificial grammar recognition using two spiking neural networks

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Petersson,  Karl Magnus
Neurobiology of Language Group, MPI for Psycholinguistics, Max Planck Society;
Unification, MPI for Psycholinguistics, Max Planck Society;
Radboud University of Nijmegen, Nijmegen, Netherlands;

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Cavaco-090519.pdf
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Citation

Cavaco, P., Curuklu, B., & Petersson, K. M. (2009). Artificial grammar recognition using two spiking neural networks. Frontiers in Neuroinformatics. Conference abstracts: 2nd INCF Congress of Neuroinformatics. doi:10.3389/conf.neuro.11.2009.08.096.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-3CBF-F
Abstract
In this paper we explore the feasibility of artificial (formal) grammar recognition (AGR) using spiking neural networks. A biologically inspired minicolumn architecture is designed as the basic computational unit. A network topography is defined based on the minicolumn architecture, here referred to as nodes, connected with excitatory and inhibitory connections. Nodes in the network represent unique internal states of the grammar’s finite state machine (FSM). Future work to improve the performance of the networks is discussed. The modeling framework developed can be used by neurophysiological research to implement network layouts and compare simulated performance characteristics to actual subject performance.