CogNovo Network is a multinational and interdisciplinary web-project which synthesises knowledge from a variety of domains (for instance, cognitive neuroscience, psychology, creativity research, and computer science, inter alia). It is a research spin-off that emerged from the European Union funded Marie Curie Actions CogNovo program at the University of Plymouth (United Kingdom). The system administrator of CogNovo Network is Dr. Christopher B. Germann (PhD, MSc, BSc / Marie Curie Alumnus).

The CogNovo Network logo has a deeper semantic and hermeneutical interpretative meaning,. Its topology symbolises “decentralised cognitive liberty” which is a condicio sine qua non for creativity, unfoldment of psychological potential, and brain development (i.e., neuro/synapto-plasticity, neuronal integration

Cf. Sensory summation/binding and the formation of higher-order abstract cognitive concepts., inter-hemispheric connectivity/synchronisation, etc.). The logo consists of a fully connected network which is also known as a non-centralised/non-hirachical full mesh topology in network science. In this specific network topology all nodes are connected to each other (viz., exhaustive interconnectivity). In graph theory this specific arrangement is termed a “complete graph” and the number of connections grows quadratically based on the number of network-nodes. This inclusive relational principle can be mathematically formalised as follows:

This document provides a comprehensive primer on various network typologies and contains numerous code-snippets for their implementation in R (statistical open-source software).

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See also:
Amaldi, E., Capone, A., Cesana, M., Filippini, I., & Malucelli, F. (2008). Optimization models and methods for planning wireless mesh networks. Computer Networks, 52(11), 2159–2171. doi.org/10.1016/j.comnet.2008.02.020

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Various network topologies

See also: Lewin, K. (1936). Principles of topological psychology. New York: McGraw-Hill.
Full-text: archive.org/details/PrinciplesOfTopologicalPsychology/

R code to plot various network structures (igraph package)

A fully connected network thus possess a non-hierarchical structure without any “centralised authority” and it possess a high degree of reliability/robustness due to the large number of redundant pathways. Besides its generic biological pertinence, the idea of equal distribution without any centralisation has obvious far-reaching political and philosophical implications as it is a truly liberal and democratic topology which allows for an “open dialogue” independent of any “top-down regulation”.
The idea of interconnectivity is also pertinent for the conceptualisation of interdiscipinary research, c.f.: holism.ga
Furthermore, it is an important idea in the context of creativity research. In neuroscience the concept of “spreading neuronal activation” is crucial for information processing in the brain (e.g., associative processes related to semantics, concept formation, and cognitive schemata). In a fully connected mesh topology information can spread freely (without inhibition/depression) and therefore ‘co-activate’ other nodes in the network. This ‘free flow’ of information (ideas/memes) is crucial for creativity and cognitive innovation – specifically in social systems (cf. cybernetics & quasi-evolutionary algorithms).

Join the forum to discuss – freely: forum.cognovo.net

also visit a related project of mine: cognitive-liberty.online

Patterson, K., Nestor, P. J., & Rogers, T. T.. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience

, 8(12), 976–987.
Plain numerical DOI: 10.1038/nrn2277
directSciHub download

Display further relevant references
Collins, A. M., & Loftus, E. F.. (1975). A spreading-activation theory of semantic processing. Psychological Review

Plain numerical DOI: 10.1037/0033-295X.82.6.407
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Anderson, J. R.. (2013). A Spreading Activation Theory of Memory. In Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence

Plain numerical DOI: 10.1016/B978-1-4832-1446-7.50016-9
directSciHub download

Dell, G. S.. (1986). A Spreading-Activation Theory of Retrieval in Sentence Production. Psychological Review

Plain numerical DOI: 10.1037/0033-295X.93.3.283
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Crestani, F.. (1997). Application of Spreading Activation Techniques in Information Retrieval. Artificial Intelligence Review

Plain numerical DOI: 10.1023/A:1006569829653
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Ziegler, C. N., & Lausen, G.. (2004). Spreading activation models for trust propagation. In Proceedings – 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE 2004

Plain numerical DOI: 10.1109/EEE.2004.1287293
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Anderson, J. R., & Pirolli, P. L.. (1984). Spread of activation. Journal of Experimental Psychology: Learning, Memory, and Cognition

Plain numerical DOI: 10.1037/0278-7393.10.4.791
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Ratcliff, R., & McKoon, G.. (1988). A Retrieval Theory of Priming in Memory. Psychological Review

Plain numerical DOI: 10.1037/0033-295X.95.3.385
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McNamara, T. P.. (1992). Theories of Priming: I. Associative Distance and Lag. Journal of Experimental Psychology: Learning, Memory, and Cognition

Plain numerical DOI: 10.1037/0278-7393.18.6.1173
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Neely, J. H.. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention. Journal of Experimental Psychology: General

Plain numerical DOI: 10.1037/0096-3445.106.3.226
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McKoon, G., & Ratcliff, R.. (1992). Spreading Activation Versus Compound Cue Accounts of Priming: Mediated Priming Revisited. Journal of Experimental Psychology: Learning, Memory, and Cognition

Plain numerical DOI: 10.1037/0278-7393.18.6.1155
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