Email updates

Keep up to date with the latest news and content from BMC Neuroscience and BioMed Central.

This article is part of the supplement: Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Open Access Poster presentation

Syntax processing properties of generic cortical circuits

Renato Duarte12*, Peggy Seriès2 and Abigail Morrison134

Author Affiliations

1 Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, 79104, Germany

2 Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK

3 Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Center, Jülich, 52425, Germany

4 Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, Bochum, 44801, Germany

For all author emails, please log on.

BMC Neuroscience 2013, 14(Suppl 1):P283  doi:10.1186/1471-2202-14-S1-P283

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/14/S1/P283


Published:8 July 2013

© 2013 Duarte et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

Higher cognitive functioning is assumed to be largely symbolic/representational and compositional in nature. At various processing stages, from perceptual to motor, discrete structural elements with intricate temporal dependencies are combined into increasingly complex constructs [1]. Mapping such complex computational processes to the underlying neuronal infrastructure and assessing the properties of the neuronal system responsible for their implementation is not straightforward, but it is likely to yield important insights into the nature of neural computation.

In order to address these issues, we adopt ideas and formalisms developed by theoretical linguistics to study the nature of rule-like or compositional behavior in the language domain, namely the acquisition of formal (artificial) grammars. The Artificial Grammar Learning (AGL) paradigm has a long tradition in psycholinguistic research (see, e.g. [2] for an overview), as a means to study the nature of syntactic processing and implicit sequence learning.

With mere exposure and without performance feedback, human beings implicitly acquire knowledge about the structural regularities implemented by complex rule systems.

In this work, we investigate to which extent generic cortical microcircuits can support formally explicit symbolic computations, instantiated by the same grammars used in the human AGL literature and implementing various types of local and non-adjacent dependencies between the sequence elements, thus requiring varying degrees of computational complexity and online processing memory to be adequately learned. We use concrete implementations of input-driven recurrent networks 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 [3]. Additionally, we compare supervised and unsupervised learning rules for the decoding algorithms, with varying degrees of biological plausibility. We show that, when shaped by plasticity, these models are capable of acquiring the structure of simple (regular) grammars. When asked to judge string legality (in a manner similar to human subjects), the networks perform at a qualitatively comparable level. We uncover which plasticity mechanisms are crucial for the task, with the aim of specifying a minimal model. Furthermore, the capability of the networks to process (bounded) recursive constructions including multiple patterns of non-adjacent dependencies accurately reflects recent results of human performance, highlighting inherent limitations imposed by the nature of neuronal processing.

Acknowledgements

Partially funded by the Erasmus Mundus Joint Doctoral Program EuroSPIN, BMBF Grant 01GQ0420 to BCCN Freiburg, the Junior Professor Program of Baden-Württemberg, the Helmholtz Alliance on Systems Biology (Germany), the Initiative and Networking Fund of the Helmholtz Association and the Helmholtz Portfolio theme "Supercomputing and Modelling for the Human Brain".

References

  1. Battaglia FP, Borensztajn G, Bod R: Structured cognition and neural systems: from rats to language.

    Neuroscience and biobehavioral reviews 2012, 36:1626-1639. PubMed Abstract | Publisher Full Text OpenURL

  2. Petersson KM, Hagoort P: The neurobiology of syntax: beyond string sets.

    Philosophical transactions of the Royal Society of London Series B, Biological sciences 2012, 367:1971-1983. PubMed Abstract | Publisher Full Text OpenURL

  3. Zheng P, Dimitrakakis C, Triesch J: Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex.

    PLoS computational biology 2013, 9(1):e1002848. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL