scholarly journals Kinetic and macroscopic models for active particles exploring complex environments with an internal navigation control system

2021 ◽  
pp. 1-27
Author(s):  
Luis Gómez Nava ◽  
Thierry Goudon ◽  
Fernando Peruani
Keyword(s):  
Markov Chain ◽  
Control System ◽  
Internal State ◽  
Biological Systems ◽  
Internal Dynamic ◽  
Transition Rates ◽  
Drift Term ◽  
Active Particles ◽  
Internal States ◽  
Navigation Control

A large number of biological systems — from bacteria to sheep — can be described as ensembles of self-propelled agents (active particles) with a complex internal dynamic that controls the agent’s behavior: resting, moving slow, moving fast, feeding, etc. In this study, we assume that such a complex internal dynamic can be described by a Markov chain, which controls the moving direction, speed, and internal state of the agent. We refer to this Markov chain as the Navigation Control System (NCS). Furthermore, we model that agents sense the environment by considering that the transition rates of the NCS depend on local (scalar) measurements of the environment such as e.g. chemical concentrations, light intensity, or temperature. Here, we investigate under which conditions the (asymptotic) behavior of the agents can be reduced to an effective convection–diffusion equation for the density of the agents, providing effective expressions for the drift and diffusion terms. We apply the developed generic framework to a series of specific examples to show that in order to obtain a drift term three necessary conditions should be fulfilled: (i) the NCS should possess two or more internal states, (ii) the NCS transition rates should depend on the agent’s position, and (iii) transition rates should be asymmetric. In addition, we indicate that the sign of the drift term — i.e. whether agents develop a positive or negative chemotactic response — can be changed by modifying the asymmetry of the NCS or by swapping the speed associated to the internal states. The developed theoretical framework paves the way to model a large variety of biological systems and provides a solid proof that chemotactic responses can be developed, counterintuitively, by agents that cannot measure gradients and lack memory as to store past measurements of the environment.

A Robot Model of Dynamic Appraisal and Response

2011 ◽  
pp. 1376-1382
Author(s):  
Carlos Herrera ◽  
Tom Ziemke ◽  
Thomas M. McGinnity
Keyword(s):  
Control System ◽  
Biological Systems ◽  
The Body ◽  
Emotional Behavior ◽  
Control Parameters ◽  
Collective Variables ◽  
Biological Phenomena ◽  
Internal States ◽  
General Goal ◽  
Environmental Relations

A general goal of biologically inspired robotics is to learn lessons from actual biological systems and to find applications in robot design. Neural controllers and adaptive algorithms are major tools to model, at some level of abstraction, functions, structures, and behaviors present in biological systems. This involves, of course, identifying in virtue of what biological systems exhibit the behavioral characteristics we want to explore. One of the biological phenomena of great interest is emotion. Despite the effort of leading researchers to raise the question “whether machines can be intelligent without any emotions” (Minsky., 1988), AI interest in emotional phenomena has increased only in the last decade. An underlying assumption is that many cognitive functions, such as memory, attention, learning, decision making and planning, are at least partly based on emotional mechanisms in biological systems (Damasio, 1995). One of the qualities of emotional behavior is its flexibility (Frijda, 1986), which contrasts with the rigidity of stereotyped behaviors such as reflexes or habits. Hence, it is relevant to investigate what it is that makes emotional behavior flexible. The body, through mostly chemical channels, produces diffuse effects on the neural system, processes at the root of emotional phenomena. Parisi has recently argued that in order “to understand the behavior of organisms more adequately we also need to reproduce in robots the inside of the body of organisms and to study the interactions of the robot’s control system with what is inside the body” (Parisi, 2004), using the term internal robotics to denote the study of the interactions between the (neural) control system and the rest of the body. Mechanisms that control homeostasis, based on hormonal modulation, can motivate appropriate behaviors (Avila-García & Cañamero, 2004; Gadanho & Hallam, 2001). Emergent behaviors from the interaction of a motivational system with the environment may be called emotional. Cañamero’s architecture, for example, consists of “a set of motivations; a repertoire of behaviors that can satisfy those internal needs or motivations as their execution carries a modification in the levels of specific variables; and a set of ‘basic’ emotions.”(Cañamero, 2005). We consider emotional phenomena to emerge from a dynamic interaction between internal states, current perceptions and environmental relations, such that certain neural/physiological states have a close causal link with relational situations. This is, in a nutshell, the embodied appraisal hypothesis (Carlos Herrera, 2002; Prinz, 2004). We use two major concepts from the dynamical systems (DS) approach to cognition (Clark, 1997; Kelso, 1995): collective variables and control parameters. In (Carlos Herrera, 2002) we argue that internal states can be interpreted as collective variables of agent/ environment interaction that allow tracing concern-relevant situations. These variables are “non-specific: they do not prescribe or contain a code for the emerging structure” (Kelso, 1995). They also can be considered control parameters, as activation in the agent’s physiological substrate affects overall action readiness (response, including perceptual and cognitive readiness).

Impaired discrimination of a subanesthetic dose of ketamine in a maternal immune activation model of schizophrenia risk

2021 ◽  
pp. 026988112110297
Author(s):  
Wayne Meighan ◽  
Thomas W Elston ◽  
David Bilkey ◽  
Ryan D Ward
Keyword(s):  
Animal Models ◽  
Drug Discrimination ◽  
Immune Activation ◽  
Internal State ◽  
Nmda Receptor Antagonist ◽  
Activation Model ◽  
Data Link ◽  
Maternal Immune Activation ◽  
Internal States ◽  
Reliable Methods

Background: Animal models of psychiatric diseases suffer from a lack of reliable methods for accurate assessment of subjective internal states in nonhumans. This gap makes translation of results from animal models to patients particularly challenging. Aims/methods: Here, we used the drug-discrimination paradigm to allow rats that model a risk factor for schizophrenia (maternal immune activation, MIA) to report on the subjective internal state produced by a subanesthetic dose of the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine. Results/outcomes: The MIA rats’ discrimination of ketamine was impaired relative to controls, both in the total number of rats that acquired and the asymptotic level of discrimination accuracy. This deficit was not due to a general inability to learn to discriminate an internal drug cue or internal state generally, as MIA rats were unimpaired in the learning and acquisition of a morphine drug discrimination and were as sensitive to the internal state of satiety as controls. Furthermore, the deficit was not due to a decreased sensitivity to the physiological effects of ketamine, as MIA rats showed increased ketamine-induced locomotor activity. Finally, impaired discrimination of ketamine was only seen at subanesthetic doses which functionally correspond to psychotomimetic doses in humans. Conclusion: These data link changes in NMDA responses to the MIA model. Furthermore, they confirm the utility of the drug-discrimination paradigm for future inquiries into the subjective internal state produced in models of schizophrenia and other developmental diseases.

scholarly journals On Robust State Estimation of Gene Networks

2010 ◽  
Vol 2 ◽  
pp. 117959721000200 ◽  
Author(s):  
Chia-Hua Chuang ◽  
Chun-Liang Lin
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Quantitative Analysis ◽  
State Estimation ◽  
Gene Networks ◽  
Gene Network ◽  
Biological Systems ◽  
Uncertain Process ◽  
Network Systems ◽  
Internal States

Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.

scholarly journals Infinitely divisible random transition probabilities with application to dependent Markov chains

1984 ◽  
Vol 25 (4) ◽  
pp. 463-472
Author(s):  
Peter L. Chesson
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
White Noise ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Transition Rates ◽  
Infinitely Divisible ◽  
Random Transition ◽  
Transition Probability Matrices ◽  
Probability Matrices

AbstractRandom transition probability matrices with stationary independent factors define “white noise” environment processes for Markov chains. Two examples are considered in detail. Such environment processes can be used to construct several Markov chains which are dependent, have the same transition probabilities and are jointly a Markov chain. Transition rates for such processes are evaluated. These results have application to the study of animal movements.

scholarly journals Autonomous Decision-Making While Drilling

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 969
Author(s):  
Eric Cayeux ◽  
Benoît Daireaux ◽  
Adrian Ambrus ◽  
Rodica Mihai ◽  
Liv Carlsen
Keyword(s):  
Decision Making ◽  
Internal State ◽  
Drilling Process ◽  
Autonomous Decision ◽  
Internal States ◽  
Markov Decision ◽  
Drilling Operations ◽  
Drilling System ◽  
Drilling Conditions ◽  
Erratic Behavior

The drilling process is complex because unexpected situations may occur at any time. Furthermore, the drilling system is extremely long and slender, therefore prone to vibrations and often being dominated by long transient periods. Adding the fact that measurements are not well distributed along the drilling system, with the majority of real-time measurements only available at the top side and having only access to very sparse data from downhole, the drilling process is poorly observed therefore making it difficult to use standard control methods. Therefore, to achieve completely autonomous drilling operations, it is necessary to utilize a method that is capable of estimating the internal state of the drilling system from parsimonious information while being able to make decisions that will keep the operation safe but effective. A solution enabling autonomous decision-making while drilling has been developed. It relies on an optimization of the time to reach the section total depth (TD). The estimated time to reach the section TD is decomposed into the effective time spent in conducting the drilling operation and the likely time lost to solve unexpected drilling events. This optimization problem is solved by using a Markov decision process method. Several example scenarios have been run in a virtual rig environment to test the validity of the concept. It is found that the system is capable to adapt itself to various drilling conditions, as for example being aggressive when the operation runs smoothly and the estimated uncertainty of the internal states is low, but also more cautious when the downhole drilling conditions deteriorate or when observations tend to indicate more erratic behavior, which is often observed prior to a drilling event.

Identifying Transition Rates of Ion Channels with One Loop

2011 ◽  
Vol 282-283 ◽  
pp. 395-398
Author(s):  
Xu Yan Xiang ◽  
Li Fang Liu ◽  
Ye Chen
Keyword(s):  
Markov Chain ◽  
Ion Channels ◽  
Lifetime Distribution ◽  
Transition Rates ◽  
Lifetime Distributions ◽  
Constrained Equations ◽  
Gating Scheme

The constrained equations between transition rates and the derivative of open lifetime and shut lifetime distribution for a given state set of Markov Chain are provided. For gating scheme of ion channels with one loop, it is derived by those underlying information that all transition rates can be identified by their open and shut lifetime distributions at state 0 and any other two adjacent states.

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