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The secret is at the crossways: Hodotopic organization and nonlinear dynamics of brain neural networks

Published online by Cambridge University Press:  21 November 2013

Tobias A. Mattei
Tobias A. Mattei*
Affiliation:
Interdisciplinary Group for Research in Neuroscience, Epistemology and Cognition, Neurological Department, The Ohio State University, Columbus, OH 43210. [email protected]
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Abstract

By integrating the classic psychological principles of ancient art of memory (AAOM) with the most recent paradigms in cognitive neuroscience (i.e., the concepts of hodotopic organization and nonlinear dynamics of brain neural networks), Llewellyn provides an up-to-date model of the complex psychological relationships between memory, imagination, and dreams in accordance with current state-of-the-art principles in neuroscience.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 6 , December 2013 , pp. 623 - 624
Copyright
Copyright © Cambridge University Press 2013 

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References

Abásolo, D., James, C. J. & Hornero, R. (2007) Non-linear analysis of intracranial electroencephalogram precordings with approximate entropy and Lempel-Ziv complexity for epileptic seizure detection. Conference Proceedings of IEEE Engineering Medicine and Biology Society 2007:1953–56.Google Scholar
Afraimovich, V., Young, T., Muezzinoglu, M. K. & Rabinovich, M. I. (2011) Nonlinear dynamics of emotion-cognition interaction: When emotion does not destroy cognition? Bulletin of Mathematical Biology 73:266–84.Google Scholar
Aiello, G. L. (2012) Cognitive aspects of chaos in random networks. Nonlinear Dynamics Psychology, and Life Sciences 16:23–35.Google ScholarPubMed
Berker, E. A., Berker, A. H. & Smith, A. (1986) Translation of Broca's 1865 report: Localization of speech in the third left frontal convolution. Archives of Neurology 43:1065–72.Google Scholar
Brainerd, C. J. & Reyna, V. F. (2001) Fuzzy-trace theory: Dual processes in memory, reasoning, and cognitive neuroscience. Advances in Child Development and Behavior 28:41–100.Google Scholar
Bressler, S. L. & Tognoli, E. (2006) Operational principles of neurocognitive networks. International Journal of Psychophysiology 60 (1–2):139–48.Google Scholar
Damoiseaux, J. & Greicius, M. D. (2009) Greater than the sum of its parts: A review of studies combining structural connectivity and resting-state functional connectivity. Brain Structure and Function 213:525–33.Google Scholar
de Benedictis, A. & Duffau, H. (2011) Brain hodotopy: From esoteric concept to practical surgical applications. Neurosurgery 68:1709–23.Google Scholar
de Sousa, A. (2011) Freudian theory and consciousness: A conceptual analysis**. Mens Sana Monographs 9:210–17. doi:10.4103/0973-1229.77437.Google Scholar
Elbert, T., Ray, W. J., Kowalik, Z. J., Skinner, J. E., Graf, K. E. & Birbaumer, N. (1994) Chaos and physiology: Deterministic chaos in excitable cell assemblies. Physiological Reviews 74:1–47.Google Scholar
Harrisson, J. G. (2010) Cultural memory and imagination: Dreams and dreaming in the Roman Empire 31 BC – AD 200. Unpublished doctoral dissertation, University of Birmingham. Retrieved January 5, 2012, from http://etheses.bham.ac.uk/469/1/Harrisson09PhD.pdf.Google Scholar
Horwitz, B. & Braun, A. R. (2004) Brain network interactions in auditory, visual and linguistic processing. Brain Language 89:377–84.Google Scholar
Huber, M. T., Krieg, J. C., Dewald, M. & Braun, H. A. (2000) Stochastic encoding in sensory neurons: Impulse patterns of mammalian cold receptors. Chaos, Solitons, and Fractals 11:1895–903.Google Scholar
Korn, H. & Faure, P. (2003) Is there chaos in the brain? II. Experimental evidence and related models. Comptes Rendus Biologies 326:787–840.Google Scholar
Laxton, A. W. & Lozano, A. M. (2012) Deep brain stimulation for the treatment of Alzheimer disease and dementias. World Neurosurgery, June 19. doi:10.1016/j.wneu.2012.06.028.Google Scholar
Laxton, A. W., Tang-Wai, D. F., McAndrews, M. P., Zumsteg, D., Wennberg, R., Keren, R., Wherrett, J., Naglie, G., Hamani, C., Smith, G. S. & Lozano, A. M. (2010) A phase I trial of deep brain stimulation of memory circuits in Alzheimer's disease. Annals of Neurology 68:521–34.Google Scholar
Litwin-Kumar, A. & Doiron, B. (2012) Slow dynamics and high variability in balanced cortical networks with clustered connections. Nature Neuroscience 15:1498–505. doi:10.1038/nn.3220.Google Scholar
Magrans, R., Gomis, P., Caminal, P. & Wagner, G. (2010) Multifractal and nonlinear assessment of autonomous nervous system response during transient myocardial ischaemia. Physiological Measurement 31:565–80.Google Scholar
McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S. & Smith, L. B. (2010) Letting structure emerge: Connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences 14:348–56.Google Scholar
McClelland, J. L. & Rogers, T. T. (2003) The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience 4:310–22.CrossRefGoogle ScholarPubMed
McIntosh, A. R. (2000) Towards a network theory of cognition. Neural Network 13:861–70.Google Scholar
Pearce, J. M. (2005) Brodmann's cortical maps. Journal of Neurology, Neurosurgery, and Psychiatry 76:259.Google Scholar
Polack, P. O. & Contreras, D. (2012) Long-range parallel processing and local recurrent activity in the visual cortex of the mouse. Journal of Neuroscience 32:11120–31.Google Scholar
Seung, H. S. (2009) Reading the book of memory: Sparse sampling versus dense mapping of connectomes. Neuron 62:17–29.CrossRefGoogle ScholarPubMed
Sporns, O. (2011b) The human connectome: A complex network. New York Academy of Sciences 1224:109–25. doi:10.1111/j.1749-6632.2010.05888.x.Google Scholar
Toga, A. W., Clark, K. A., Thompson, P. M., Shattuck, D. W. & van Horn, J. D (2012) Mapping the human connectome. Neurosurgery 71:1–5. doi:10.1227/NEU.0b013e318258e9ff.Google Scholar
Tokuda, I. T., Han, C. E., Aihara, K., Kawato, M. & Schweighofer, N. (2010) The role of chaotic resonance in cerebellar learning. Neural Network 23:836–42. doi:10.1016/j.neunet.2010.04.006.Google Scholar
Turrigiano, G. G. & Nelson, S. B. (2004) Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience 5:97–107.Google Scholar
Uhlhaas, P. J. & Singer, W. (2012) Neuronal dynamics and neuropsychiatric disorders: Toward a translational paradigm for dysfunctional large-scale networks. Neuron 75:963–80.Google Scholar
Von Economo, C. (1930) Cytoarchitectony and progressive cerebration. Psychiatric Quarterly 4:142–50.Google Scholar
Wernicke, K. (1970) The aphasia symptom-complex: A psychological study on an anatomical basis. Archives of Neurology 22:280–82.Google Scholar