{"id":117,"date":"2018-09-23T20:53:09","date_gmt":"2018-09-23T18:53:09","guid":{"rendered":"https:\/\/www.synapticart.it\/synapticart\/"},"modified":"2023-11-08T20:55:49","modified_gmt":"2023-11-08T19:55:49","slug":"synapticart","status":"publish","type":"page","link":"https:\/\/www.synapticart.it\/en\/","title":{"rendered":"Synapticart – Arts and Science Lab, Milano"},"content":{"rendered":"

SynapticArt<\/h1>\n

Neuroscience Laboratory
\nfor Visual System Technologies<\/h2>\n<\/div>
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  1. Synapticart is an Interdisciplinary Laboratory that aims to identify new languages for visual arts, design, and exhibition systems (artworks and high-end commercial objects).<\/li>\n<\/ol>\n
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    1. For the first time, the performances of these sectors are studied in the laboratory with scientific instruments in order to detect the impact they can have on the brain of the observers.<\/li>\n<\/ol>\n<\/div>
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      1. In this new perspective the focus of attention is no longer centered on the artist but on the user of the work.<\/li>\n<\/ol>\n
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        1. To measure the brain responses to aesthetic stimuli, the Synapticart researchers no longer use the invasive techniques used by Zeki, Rizzolatti and others (electrodes applied to brain areas, FMRI and PET) but operate on the basis of the modeling of artificial neural networks.<\/li>\n<\/ol>\n
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          1. This platform arises from surprising empirical evidence: the compositional elements of natural phenomena and man-made artifacts are connected to each other with the same synaptic logic of biological neurons, for which they become “artificial neurons” to all intents and purposes.
            \nConsequently, a work by Fontana, the Proust armchair by Mendini or an Armani suit are transformed into maps of artificial neurons.<\/li>\n<\/ol>\n
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            1. Since these maps highlight not only the product components but also their “connective weight” (Hebb), each single map allows you to “predict” the impact of your product on the observer’s brain. The predictability of the success of an artifact is the first innovative function of the Synapticart program.<\/li>\n<\/ol>\n
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              1. But the study of the brain system also allows us to identify the universal source of man’s needs and dreams together with his ability to perceive and understand new signals, still unknown in the art world: connective patterns, biopsychic indexes with automatic triggering , the double code, the broken symmetry, the energetic resonance, the cluster analysis and other expressive systems. This is the second innovative function activated by the Laboratory.<\/li>\n<\/ol>\n<\/div>
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                1. There are two appropriate tools to generate these signals:\n
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                  1. the SMI (innate motivational systems of behavior) discovered by human ethology (Eibl-Eibesfeldt) and subsequently perfected by clinical psychology (Liotti);<\/li>\n
                  2. the subsymbolic language of neural networks which is a natural language that we are all gifted with but without our knowledge (Parisi).<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n
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                    1. All information is processed through the bottom-up method which operates at a lower level than the cultural level of current communication systems. Interpret a work of art with this procedure we no longer start from the historical knowledge of the work and of the artist but we arrive at these data starting from the physical, formal and functional aspects of the work, ie from the material. Using this procedure in several research on works belonging to different periods and places managed to identify the “innate code of beauty” that acts under expressive sectors of fashion, design, architecture and interior.<\/li>\n<\/ol>\n<\/div>
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                      1. The expressive peak of Synapticart’s activity, however, lies in the “competitive creativity” of the project on the global market. This ability, commonly attributed to the intuition of a designer (Porcini) or the experience of a super expert (Noci), is instead supported by two fundamental scientific supports:\n
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                        1. the knowledge of self-generating dynamic systems, described by neural networks (Goldberg, Parisi, Kohonen);<\/li>\n
                        2. b. the knowledge of complex systems that use order and chaos to make the dynamics of the global market evolve. Indeed, for the physicists of complexity, chaos does not have a negative value as it is the very engine of change (Hopfield, Anderson, Kadanoff).<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<\/div>
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                          1. The reference to complexity and neural networks triggers chain consequences in the world of visual arts and design because it undermines their traditional canons starting from the classic one\u00a0that of symmetry.
                            \nThis notion has been considered for centuries as the emblem of beauty due to the proportion and harmony of its parts. But modern geometry (Klein, Galois) has identified the genesis of this notion in a process of internal transformation of its parts, subjected to rotation, translation and overturning on the basis of an invariant axis.
                            \nConsequently, symmetry is no longer configured as an immobile structure but as a dynamic system.<\/li>\n<\/ol>\n
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                            1. All this can also be confirmed by the neural processing of the images:\n
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                              1. the planar surface of any image contains a stratification of multiple levels, generated by the connective weights (Hebb) of its own information;
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                              2. even if to our eyes the image appears flat and two-dimensional it is nevertheless processed by our brain in a three-dimensional format of information.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<\/div>
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                                1. From these synthetic indications it is clear that the images have depth and that they transform under the eyes of the observer, bringing out the genesis of their intrinsic meaning.
                                  \nThis meaning necessarily translates into a story which, however, is not sequenced by the imagination of the observer but by the internal dynamics of the connective weights of the information of the work. Nothing to do with storytelling!<\/li>\n<\/ol>\n<\/div>
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                                  1. We are obviously talking about a paradigm shift that involves the entire structure of visual systems on which Synapticart has developed specific experimental research for over a decade that currently make up a unitary advanced training program called the Laboratory School.<\/li>\n<\/ol>\n
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                                    1. It is hardly necessary to specify that all the technological innovations mentioned in this brief presentation are the result of specific empirical results of research carried out in Synapticart and published in Italian and English on Synapticart Publishing, Amazon both in digital and in paper form:\n
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                                      • Nicola Colecchia, Filippo Zaccardi, (2000) La rottura di simmetria nella comunicazione visiva, FrancoAngeli, Milano<\/li>\n
                                      • Nicola Colecchia, (2018) what are artificial neurons?, Synapticart Publishing<\/li>\n
                                      • Nicola Colecchia, (2019) Fontana, from slashes to ignored expectations, Synapticart Publishing<\/li>\n
                                      • Nicola Colecchia, (2020) Women\u00a0and power in the works of Leonardo, Synapticart Publishing<\/li>\n
                                      • Nicola Colecchia, (2023) Il design di Mendini alla luce delle Neuroscienze, Synapticart Publishing<\/li>\n
                                      • Nicola Colecchia, (2017) Mona Lisa’s Neurons, Synapticart Publishing<\/li>\n
                                      • Nicola Colecchia (2023), L’altro Design cambio di paradigma\u00a0e nuovi\u00a0linguaggi, Synapticart\u00a0Publisching, Amazon<\/li>\n
                                      • Di imminente pubblicazione da parte dello stesso autore il saggio intitolato \u201cL\u2019estetica naturale del caos\u201d<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<\/div>
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