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Reservoir computing and the Sooner-is-Better bottleneck

Published online by Cambridge University Press:  02 June 2016

Stefan L. Frank  and
Hartmut Fitz
Stefan L. Frank
Affiliation:
Centre for Language Studies, Radboud University Nijmegen, 6500 HD Nijmegen, The Netherlands. [email protected]
Hartmut Fitz
Affiliation:
Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands. [email protected]/people/fitz-hartmut
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Abstract

Prior language input is not lost but integrated with the current input. This principle is demonstrated by “reservoir computing”: Untrained recurrent neural networks project input sequences onto a random point in high-dimensional state space. Earlier inputs can be retrieved from this projection, albeit less reliably so as more input is received. The bottleneck is therefore not “Now-or-Never” but “Sooner-is-Better.”

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e73
Copyright
Copyright © Cambridge University Press 2016 

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References

Bi, G. & Poo, M. (2001) Synaptic modification of correlated activity: Hebb's postulate revisited. Annual Review of Neuroscience 24:139–66.Google Scholar
Buonomano, D. V. & Maass, W. (2009) State-dependent computations: Spatiotemporal processing in cortical networks. Nature Reviews Neuroscience 10:113–25.Google Scholar
Christiansen, M. H. & Chater, N. (1999) Toward a connectionist model of recursion in human linguistic performance. Cognitive Science 23:157–205.Google Scholar
Dominey, P. F., Hoen, M., Blanc, J.-M. & Lelekov-Boissard, T. (2003) Neurological basis of language and sequential cognition: Evidence from simulation, aphasia and ERP studies. Brain and Language 86:207–25.CrossRefGoogle ScholarPubMed
Fitz, H. (2011) A liquid-state model of variability effects in learning nonadjacent dependencies. In: Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Boston, MA, July 2011, ed. Carlson, L., Hölscher, C. & Shipley, T., pp. 897–902. Cognitive Science Society.Google Scholar
Frank, S. L. & Bod, R. (2011) Insensitivity of the human sentence-processing system to hierarchical structure. Psychological Science 22:829–34.CrossRefGoogle ScholarPubMed
Gerstner, W., Kistler, W. M., Naud, R. & Paninski, L. (2014) Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press.CrossRefGoogle Scholar
Hinaut, X. & Dominey, P. F. (2013) Real-time parallel processing of grammatical structure in the fronto-striatal system: A recurrent network simulation study using reservoir computing. PLOS ONE 8(2):e52946.CrossRefGoogle ScholarPubMed
Jaeger, H. & Haas, H. (2004) Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304:78–80.Google Scholar
Lukoševičius, M. & Jaeger, H. (2009) Reservoir computing approaches to recurrent neural network training. Computer Science Review 3:127–49.CrossRefGoogle Scholar
Maass, W., Natschläger, T. & Markram, H. (2002) Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14:2531–60.Google Scholar
Mongillo, G., Barak, O. & Tsodyks, M. (2008) Synaptic theory of working memory. Science 319:1543–46.Google Scholar
Petersson, K. M. & Hagoort, P. (2012) The neurobiology of syntax: Beyond string sets. Philosophical Transactions of the Royal Society B 367:1971–83.CrossRefGoogle ScholarPubMed
Rabinovich, M., Huerta, R. & Laurent, G. (2008) Transient dynamics for neural processing. Science 321:48–50.CrossRefGoogle ScholarPubMed
Rigotti, M., Barak, O., Warden, M. R., Wang, X. -J., Daw, N. D., Miller, E. K. & Fusi, S. (2013) The importance of mixed selectivity in complex cognitive tasks. Nature 497:585–90.Google Scholar
Singer, W. (2013) Cortical dynamics revisited. Trends in Cognitive Sciences 17:616–26.Google Scholar