Stochastic Information Processing in Biological Systems II — Statistics, Dynamics, and Phase Transitions

1985 ◽  
pp. 131-144
Author(s):  
Harold M. Hastings
Keyword(s):  
Information Processing ◽  
Phase Transitions ◽  
Biological Systems

scholarly journals Optimal Size for Maximal Energy Efficiency in Information Processing of Biological Systems Due to Bistability

2015 ◽  
Vol 32 (11) ◽  
pp. 110501 ◽  
Author(s):  
Chi Zhang ◽  
Li-Wei Liu ◽  
Long-Fei Wang ◽  
Yuan Yue ◽  
Lian-Chun Yu
Keyword(s):  
Energy Efficiency ◽  
Information Processing ◽  
Biological Systems ◽  
Optimal Size ◽  
Maximal Energy

Modeling of Complex Biological Systems. 2. Effect of Chain Length on the Phase Transitions Observed in Diglyceride Monolayers

Langmuir ◽  
1995 ◽  
Vol 11 (10) ◽  
pp. 4072-4081 ◽  
Author(s):  
Guenther H. Peters ◽  
S. Toxvaerd ◽  
O. H. Olsen ◽  
A. Svendsen
Keyword(s):  
Phase Transitions ◽  
Chain Length ◽  
Biological Systems

Neural constructivism or self-organization?

2000 ◽  
Vol 23 (5) ◽  
pp. 783-784 ◽  
Author(s):  
Peter C. M. Molenaar ◽  
Han L. J. van der Maas
Keyword(s):  
Information Processing ◽  
Phase Transitions ◽  
Cognitive Development ◽  
Nonlinear Systems ◽  
Signal Analysis ◽  
Self Organization ◽  
Essential Ingredient ◽  
Pattern Information ◽  
Adaptive Signal

Three arguments are given to show that neural constructivism lacks an essential ingredient to explain cognitive development. Based on results in the theory of adaptive signal analysis, adaptive biological pattern information and self-organization in nonlinear systems of information processing, it is concluded that neural constructivism should be further extended to accommodate the occurrence of phase transitions generating qualitative development in the sense of Piaget.

scholarly journals A modified Ising model of Barabási–Albert network with gene-type spins

2020 ◽  
Vol 81 (3) ◽  
pp. 769-798
Author(s):  
Jeyashree Krishnan ◽  
Reza Torabi ◽  
Andreas Schuppert ◽  
Edoardo Di Napoli
Keyword(s):  
Phase Transitions ◽  
Ising Model ◽  
Large Scale ◽  
Biological Systems ◽  
Ising Models ◽  
Network Size ◽  
Living Systems ◽  
Computational Techniques ◽  
Solid State Physics ◽  
Gene Type

Abstract The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási–Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size.

The Embodiment of Synthetic Emotion

2015 ◽  
pp. 204-212
Author(s):  
Carlos Herrera ◽  
M.G. Sánchez-Escribano ◽  
Ricardo Sanz
Keyword(s):  
Information Processing ◽  
Biological Systems ◽  
Control Action ◽  
Cognitive Economy ◽  
Interaction Dynamics ◽  
Actual Role ◽  
Physiological Processes ◽  
Novel Approach ◽  
Fundamental Phenomenon

Emotions are fundamentally embodied phenomena - but what exactly does this mean? And how is embodiment relevant for synthetic emotion? The specific role of embodied processes in the organisation of cognition and behaviour in biological systems is too complex to analyse without abstracting away the vast majority of variables. Robotic approaches have thus ignored physiological processes. At most, they hypothesise that homeostatic processes play a role in the cognitive economy of the agent – “gut feeling” is the embodied phenomenon to be modelled. Physiological processes play an actual role in the control of behaviour and interaction dynamics beyond information-processing. In this chapter, the authors introduce a novel approach to emotion synthesis based on the notion of morphofunctionality: the capacity to modulate the function of subsystems, changing the overall functionality of the system. Morphofunctionality provides robots with the capacity to control action readiness, and this in turn is a fundamental phenomenon for the emergence of emotion.

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