Компромиссное разрешение конфликтных ситуаций в задачах оптимального управления при принятии решений в сложных ситуационных обстоятельствах

In present article it was proposed to apply mathematical model of the functional system of respiration for the simulation conflictcontrolled processes that appear during the self-organization of respiratory system under the conditions of complex situational stress in process of decisions’ making. The mathematical model of mass transfer and mass exchange of respiratory gases in human organism is represented as a system of ordinary differential equations, which in the dynamics of the respiratory cycle describe the changes in the stresses of oxygen and carbon dioxide at all stages of its path in human organism.Пропонується застосувати математичну модель функціональної системи дихання для імітації конфліктно-керованих процесів, які виникають при самоорганізації системи дихання в умовах складної ситуаційної напруги при прийнятті рішень. Математична модель масопереносу та масообміну респіраторних газів в організмі людини представлена системою звичайних диференційних рівнянь, які в динаміці дихального циклу описують зміну напружень кисню та вуглекислого газу на всіх етапах його шляху в організмі людини. Розв’язок цієї задачі з урахуванням як внутрішньо системних, так і між системних механізмів дозволяє прогнозувати оптимальні величини активних параметрів керування – вентиляції, об’ємних швидкостей системного та органних кровообігів.Предлагается применить математическую модель функциональной системы дыхания для имитации конфликтно-управляемых процессов, возникающих при самоорганизации системы дыхания в условиях сложного ситуационного напряжения при принятии решений. Математическая модель массопереноса и массообмена респираторных газов в организме человека представлена системой обыкновенных дифференциальных уравнений, которые в динамике дыхательного цикла описывают изменения напряжений кислорода и углекислого газа на всех этапах его пути в организме человека

Signal detection in extracellular neural ensemble recordings using higher criticism

Information processing in the brain is conducted by a concerted action of multiple neural populations. Gaining insights in the organization and dynamics of such populations can best be studied with broadband intracranial recordings of so-called extracellular field potential, reflecting neuronal spiking as well as mesoscopic activities, such as waves, oscillations, intrinsic large deflections, and multiunit spiking activity. Such signals are critical for our understanding of how neuronal ensembles encode sensory information and how such information is integrated in the large networks underlying cognition. The aforementioned principles are now well accepted, yet the efficacy of extracting information out of the complex neural data, and their employment for improving our understanding of neural networks, critically depends on the mathematical processing steps ranging from simple detection of action potentials in noisy traces - to fitting advanced mathematical models to distinct patterns of the neural signal potentially underlying intra-processing of information, e.g. interneuronal interactions. Here, we present a robust strategy for detecting signals in broadband and noisy time series such as spikes, sharp waves and multi-unit activity data that is solely based on the intrinsic statistical distribution of the recorded data. By using so-called higher criticism - a second-level significance testing procedure comparing the fraction of observed significances to an expected fraction under the global null - we are able to detect small signals in correlated noisy time-series without prior filtering, denoising or data regression. Results demonstrate the efficiency and reliability of the method and versatility over a wide range of experimental conditions and suggest the appropriateness of higher criticism to characterize neuronal dynamics without prior manipulation of the data

USSR Space Life Sciences Digest. Index to issues 1-4

This document is an index to issues 1 to 4 of the USSR Space Life Sciences Digest and is arranged in three sections. In section 1, abstracts from the first four issues are grouped according to subject; please note the four letter codes in the upper right hand corner of the pages. Section 2 lists the categories according to which digest entries are grouped and cites additional entries relevant to that category by four letter code and entry number in section 1. Refer to section 1 for titles and other pertinent information. Key words are indexed in section 3

Predictive feedback control and Fitts' law

Fitts’ law is a well established empirical formula, known for encapsulating the “speed-accuracy trade-off”. For discrete, manual movements from a starting location to a target, Fitts’ law relates movement duration to the distance moved and target size. The widespread empirical success of the formula is suggestive of underlying principles of human movement control. There have been previous attempts to relate Fitts’ law to engineering-type control hypotheses and it has been shown that the law is exactly consistent with the closed-loop step-response of a time-delayed, first-order system. Assuming only the operation of closed-loop feedback, either continuous or intermittent, this paper asks whether such feedback should be predictive or not predictive to be consistent with Fitts law. Since Fitts’ law is equivalent to a time delay separated from a first-order system, known control theory implies that the controller must be predictive. A predictive controller moves the time-delay outside the feedback loop such that the closed-loop response can be separated into a time delay and rational function whereas a non- predictive controller retains a state delay within feedback loop which is not consistent with Fitts’ law. Using sufficient parameters, a high-order non-predictive controller could approximately reproduce Fitts’ law. However, such high-order, “non-parametric” controllers are essentially empirical in nature, without physical meaning, and therefore are conceptually inferior to the predictive controller. It is a new insight that using closed-loop feedback, prediction is required to physically explain Fitts’ law. The implication is that prediction is an inherent part of the “speed-accuracy trade-off”

Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)