Description: Self-Organized Artificial Grammar Learning in Spiking Neural Networks

This title appears in the Scientific Report : 2014

Self-Organized Artificial Grammar Learning in Spiking Neural Networks

The Artificial Grammar Learning (AGL) paradigm provides a means to study the nature of syntactic processing and implicit sequence learning. With mere exposure and without perfor-mance feedback, human beings implicitly acquire knowledge about the structural regularities implemented by complex rule sy...

Personal Name(s):	Duarte, Renato (Corresponding Author)
	Morrison, Abigail
Contributing Institute:	Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6
Published in:	2014
Imprint:	2014
Conference:	36th Annual Conference of the Cognitive Science Society, Quebec City (Canada), 2014-07-23 - 2014-07-26
Document Type:	Conference Presentation
Research Program:	W2/W3 Professorinnen Programm der Helmholtzgemeinschaft (Dys-)function and Plasticity ohne Topic
	Publikationsportal JuSER

The Artificial Grammar Learning (AGL) paradigm provides a means to study the nature of syntactic processing and implicit sequence learning. With mere exposure and without perfor-mance feedback, human beings implicitly acquire knowledge about the structural regularities implemented by complex rule systems. We investigate to which extent a generic cortical mi-crocircuit model can support formally explicit symbolic com-putations, instantiated by the same grammars used in the hu-man AGL literature and how a functional network emerges, in a self-organized manner, from exposure to this type of data. We use a concrete implementation of an input-driven recurrent network composed of noisy, spiking neurons, built according to the reservoir computing framework and dynamically shaped by a variety of synaptic and intrinsic plasticity mechanisms operating concomitantly. We show that, when shaped by plas-ticity, these models are capable of acquiring the structure of a simple grammar. When asked to judge string legality (in a manner similar to human subjects), the networks perform at a qualitatively comparable level.