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Building brains that communicate like machines

Published online by Cambridge University Press:  10 November 2017

Daniel Graham
Daniel Graham*
Affiliation:
Department of Psychology, Hobart & William Smith Colleges, Geneva, NY 14456. graham@hws.eduhttp://people.hws.edu/graham
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Abstract

Reverse engineering human cognitive processes may improve artificial intelligence, but this approach implies we have little to learn regarding brains from human-engineered systems. On the contrary, engineered technologies of dynamic network communication have many features that highlight analogous, poorly understood, or ignored aspects of brain and cognitive function, and mechanisms fundamental to these technologies can be usefully investigated in brains.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e266
Copyright
Copyright © Cambridge University Press 2017 

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References

Briggs, F. & Usrey, W. M. (2007) A fast, reciprocal pathway between the lateral geniculate nucleus and visual cortex in the macaque monkey. The Journal of Neuroscience 27(20):5431–36.Google Scholar
Fasolo, A. (2011) The theory of evolution and its impact. Springer.Google Scholar
Fornito, A., Zalesky, A. & Bullmore, E. (2016) Fundamentals of brain network analysis. Academic Press.Google Scholar
Giambene, G. (2005) Queuing theory and telecommunications networks and applications. Springer Science + Business Media.Google Scholar
Graham, D. J. (2014) Routing in the brain. Frontiers in Computational Neuroscience 8:44.Google Scholar
Graham, D. J. and Rockmore, D. N. (2011) The packet switching brain. Journal of Cognitive Neuroscience 23(2):267–76.Google Scholar
Granger, R. (2006) Engines of the brain: The computational instruction set of human cognition. AI Magazine 27(2):15.Google Scholar
Mišić, B., Sporns, O. & McIntosh, A. R. (2014) Communication efficiency and congestion of signal traffic in large-scale brain networks. PLoS Computational Biology 10(1):e1003427.Google Scholar
Olshausen, B. A., Anderson, C. H. & Van Essen, D. C. (1993) A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. The Journal of Neuroscience 13(11):4700–19.Google Scholar
Poggio, T. (1984) Routing thoughts. Massachusetts Institute of Technology Artificial Intelligence Laboratory Working Paper 258.Google Scholar
Schlegel, A., Alexander, P. & Peter, U. T. (2015) Information processing in the mental workspace is fundamentally distributed. Journal of Cognitive Neuroscience 28(2):295–307.Google Scholar
Sizemore, A., Giusti, C., Betzel, R. F. & Bassett, D. S. (2016) Closures and cavities in the human connectome. arXiv preprint 1608.03520. Available at: https://arxiv.org/abs/1608.03520.Google Scholar
Trettenbrein, P. C. (2016) The demise of the synapse as the locus of memory: A looming paradigm shift? Frontiers in Systems Neuroscience 10:88.Google Scholar
U.S. Postal Service Historian (2016) Pieces of mail handled, number of post offices, income, and expenses since 1789. Available at: https://about.usps.com/who-we-are/postal-history/pieces-of-mail-since-1789.htm.Google Scholar
Wiskott, L. (2006). How does our visual system achieve shift and size invariance? In: 23 Problems in systems neuroscience, ed. Van Hemmen, J. L. & Sejnowski, T. J., pp. 322–40. Oxford University Press.Google Scholar
Wiskott, L. & von der Malsburg, C. (1996) Face recognition by dynamic link matching. In: Lateral interactions in the cortex: structure and function, ed. Sirosh, J., Miikkulainen, R. and Choe, Y., ch 11. The UTCS Neural Networks Research Group.Google Scholar