Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page

Endogenous distributions in multi-agents models: the example of endogenization of ends and time constants

Multi-agents modelers recurrently face the problem of the choice of their parameters' values while most of them are exogenous. In this paper we address the issue of endogenization of these parameters when it makes sense in a social learning perspective within the formalism of metamimetic games. We first show how its is possible to endogeneize the agents' ends distribution with a spatial prisoner's dilemma as case study. Then we apply the method to endogenization of time constants in the model, each agent having its own subjective perception of time. In this perspective, the values of endogenous parameters are the outcome of a dynamical process characterized by agent's cognitive capacities and environmental constraints.parameters endogenization, endogenous distributions, spatial games, time constants, evolution of cooperation, metamimetic games

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

The evolutionary approach to understand human low fertility phenomenon

Is it possible to reverse the low total fertility rate (TFR) in the developed world? Using a hypothetical model of population we have analysed the decline of the TFR which have took place in the background of ongoing global economic changes, and a liberalization process after the end of the Cold War. These phenomena have affected more than 110 millions of inhabitants of Central Europe and the Baltics and approximately 80 millions of inhabitants in Germany. The model has features of complex and evolving system of interacting individuals, and it enables to investigate a broad spectrum of input factors on individual decisions to limit the offspring. In the case of the TFR <1.5, our initial analysis show a need of radical changes of the global economy that will stimulate series of self-regulations of demographic processes and evolution toward the safe TFR>2.1. The changes should stimulate more uniform spatial distribution of wealth, capital and usage. They will increase a number of self-sufficient and cooperative territories, to decrease the income inequality, to decrease labour and social mobilities. Societies should investigate the impacts of economic regulations and actions on the TFR trends in advance and take into account a biological nature of women more responsible