Vittoradolfo Tambone, Paola Frati, Francesco De Micco, Giampaolo Ghilardi, Vittorio Fineschi

The Lancet

Globally, we are seeing a side-effect of COVID-19: political violence.1 This effect usually shows itself in three steps: denial of scientific evidence, judgement of the intentions of political decision makers as a conspiracy, and civil disobedience and street violence.
This dynamic embodies violence on an intellectual, institutional, and physical level. Intellectual violence tends to break the trust between the scientific world and public opinion, institutional violence aims to divide politics from society, and physical violence shatters civil coexistence. In other words, it is the best strategy to destroy the idea of basic democracy, as recalled by Josiah Ober.2

Read the full article at: www.thelancet.com

Ichiro Tsuda, Hiroshi Watanabe, Hiromichi Tsukada, and Yutaka Yamaguti

Entropy 2022, 24(2), 240

The focus of this article is the self-organization of neural systems under constraints. In 2016, we proposed a theory for self-organization with constraints to clarify the neural mechanism of functional differentiation. As a typical application of the theory, we developed evolutionary reservoir computers that exhibit functional differentiation of neurons. Regarding the self-organized structure of neural systems, Warren McCulloch described the neural networks of the brain as being “heterarchical”, rather than hierarchical, in structure. Unlike the fixed boundary conditions in conventional self-organization theory, where stationary phenomena are the target for study, the neural networks of the brain change their functional structure via synaptic learning and neural differentiation to exhibit specific functions, thereby adapting to nonstationary environmental changes. Thus, the neural network structure is altered dynamically among possible network structures. We refer to such changes as a dynamic heterarchy. Through the dynamic changes of the network structure under constraints, such as physical, chemical, and informational factors, which act on the whole system, neural systems realize functional differentiation or functional parcellation. Based on the computation results of our model for functional differentiation, we propose hypotheses on the neuronal mechanism of functional differentiation. Finally, using the Kolmogorov–Arnold–Sprecher superposition theorem, which can be realized by a layered deep neural network, we propose a possible scenario of functional (including cell) differentiation.

Read the full article at: www.mdpi.com