Bromiley, PA, Thacker, NA, Bouhova-Thacker, E (2010) Shannon entropy, Renyi entropy, and information. Tina 2004-004, Statistic and Inf Series, Imaging Science and Biomedical Engineering, The University of Manchester, UK
Duyckaerts G, Godefroy G (2000) Voronoï tessellation to study the numerical density and the spatial distribution of neurons. J Chem Neuroanat 20(1):83–92
CAS
Article
PubMed
Google Scholar
Edelsbrunner H (2006) Geometry and topology for mesh generation. Cambridge University Press, Cambridge
Google Scholar
Frank NP, Hart SM (2010) A dynamical system using the Voronoï tessellation. Am Math Mon 117(2):92–112
Google Scholar
Freeman WJ (2007) Definitions of state variables and state space for brain-computer interface: part 1. Multiple hierarchical levels of brain function. Cogn Neurodyn 1(1):3–14. doi:10.1007/s11571-006-9001-x
Article
PubMed
Google Scholar
Mandelkow H, de Zwart JA, Duyn JH (2016) Linear discriminant analysis achieves high classification accuracy for the BOLD fMRI response to naturalistic movie stimuli. Front Hum Neurosci 10:128. doi:10.3389/fnhum.2016.00128
Article
PubMed
PubMed Central
Google Scholar
Nieuwenhuys R, Voogd J, van Huijzen C (2008) The human central nervous system. Springer, Heidelberg
Book
Google Scholar
Peters JF (2016) Computational proximity. In: Intelligent Systems Reference Library (ed) Excursions in the topology of digital images. Springer, Berlin. doi:10.1007/978-3-319-30262-1
Google Scholar
Peters JF, İnan E (2016) Strongly near Voronoï nucleus clusters. 1–7. arXiv:1602(03734)
Peters JF, Tozzi A, Ramanna S (2016) Brain tissue tessellation shows absence of canonical microcircuits. Neurosci Letters 626:99–105
CAS
Article
Google Scholar
Peters JF, Ramanna S, Tozzi A, Inan E (2017) BOLD-independent computational entropy assesses functional donut-like structures in brain fMRI image. Frontiers Hum Neurosci. doi:10.3389/fnhum.2017.00038
Google Scholar
Pexman PM, Siakaluk PD, Yap MJ (2013) Introduction to the research topic meaning in mind: semantic richness effects in language processing. Hum Neurosci, Front. doi:10.3389/fnhum.2013.00723
Google Scholar
Rényi A (1961) On measures of entropy and information. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, vol. I, University of California Press, Berkeley, pp 547–457 (MR0132570)
Rényi A (1966) On the amount of information in a random variable concerning an event. J Math Sci 1:30–33 (MR0210263)
Google Scholar
Rényi A (1982) Tagebuch über die Informationstheorie. VEB Deutcher der Wissenschaften, Berlin (MR0707097)
Google Scholar
Taylor P, Hobbs JN, Burroni J, Siegelmann HT (2015) The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions. Sci Rep 5:18112. doi:10.1038/srep18112
CAS
Article
PubMed
PubMed Central
Google Scholar
Tozzi A (2015) Information processing in the CNS: a supramolecular chemistry? Cogn Neurodyn 9(5):463–477
Article
PubMed
PubMed Central
Google Scholar
Tozzi A, Peters JF (2016a) Towards a fourth spatial dimension of brain activity. Cogn Neurodyn 10(3):189–199. doi:10.1007/s11571-016-9379-z
Article
PubMed
PubMed Central
Google Scholar
Tozzi A, Peters JF (2016b) A topological approach unveils system invariances and broken symmetries in the brain. J Neurosci Res 94(5):351–365. doi:10.1002/jnr.23720
CAS
Article
PubMed
Google Scholar
Werner S, Noppeney U (2009) Superadditive responses in superior temporal sulcus predict audiovisual benefits in object categorization. Cereb Cortex 20(8):1829–1842
Article
PubMed
Google Scholar
Xing M, Ajilore O, Wolfson OE, Abbott C, MacNamara A et al (2016) Brain informatics and health. Ser Lect Notes Comput Sci 9919:149. doi:10.1007/978-3-319-47103-7_15
Article
Google Scholar