Discontinuity – Continuity: Physics, Neuroscience and the Search for Their New Relationship

Abstract

This paper is a development of the author’s research within the framework of the project “Atomism and World Culture”. The author believes that the study of atomism in the light of the discreteness – continuity problem is very promi­sing. From this angle, various problems of methodology in individual sciences and in interdisciplinary research can be elucidated. In particular, models asso­ciated with cognitive processes. This paper draws historical parallels between the development of atomistic ideas in physics and neuroscience at the beginning of the 20th century, which serves as an important analogy in the search for ele­mentary structures in cognitive systems with the help of physical and mathemati­cal methods. The paper proposes a theoretical approach to modeling cognitive functions which refers to the boundary between discreteness and continuity. This allows to develop some new ideas on elementarity and atomism in a more global perspective. The author elucidates a correspondence (and differences) of these models in relation to known approaches and methods in the field of neuro­science. He attempts to correlate the proposed model with the concept of cogni­tion (Konstantin V. Anokhin), linking the latter with the self-closed structures of connections of neural networks and the systems of nerve impulses passing through them. The philosophical meaning of the introduced new model is clari­fied with the help of a system of cyclic structures and corresponding signals in neural networks, represented by the graph formalism, as well as through the study of achieving minimal significant systems that can be compared with “atoms of consciousness”. The paper also discusses some prospects of this
approach for explaining some cognitive phenomena. Closed – open is one
of the images of discrete – continuous, which in the system of cyclic structures acquires a very specific direction. The author believes in the possibility of crea­ting AI (artificial intelligence) models with new properties that express the abi­lity of self-reflection inherent in the system of signal cycles.