Evolution of Modular Networks Under Selection for Non-Linearly Denoising

2016 ◽  
Vol 20 (5) ◽  
pp. 705-711 ◽  
Author(s):  
Yusuke Ikemoto ◽  
◽  
Kosuke Sekiyama ◽  
Keyword(s):  
Simple Model ◽  
Network Evolution ◽  
Upper And Lower Bounds ◽  
Positive Role ◽  
Modular Network ◽  
Modular Networks ◽  
Selection For ◽  
Modular Structures ◽  
Target Output ◽  
Nonlinear Denoising

Many biological and artifact networks often represent modular structures in which the network can be decomposed into several subnetworks. Here, we propose a simple model for the modular network evolution based on the nonlinear denoising in node activities. This model suggests that modular networks can evolve under certain conditions — if the stipulated goals for the networks or the input and target output pairs involve modular features, or if the signal transfer in a node is carried out in a nonlinear manner with respect to the saturation at the upper and lower bounds. Our model highlights the positive role played by noise in modular network evolution.

Noise and the Evolution of Neural Network Modularity

2011 ◽  
Vol 17 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Boye Annfelt Høverstad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Binary Classification ◽  
Selective Advantage ◽  
Modular Network ◽  
Modular Networks ◽  
Network Topologies ◽  
Noisy Conditions ◽  
Network Modularity ◽  
Neural Network Classifiers

We study the selective advantage of modularity in artificially evolved networks. Modularity abounds in complex systems in the real world. However, experimental evidence for the selective advantage of network modularity has been elusive unless it has been supported or mandated by the genetic representation. The evolutionary origin of modularity is thus still debated: whether networks are modular because of the process that created them, or the process has evolved to produce modular networks. It is commonly argued that network modularity is beneficial under noisy conditions, but experimental support for this is still very limited. In this article, we evolve nonlinear artificial neural network classifiers for a binary classification task with a modular structure. When noise is added to the edge weights of the networks, modular network topologies evolve, even without representational support.

scholarly journals Simplified group activity selection with group size constraints

2021 ◽  
Author(s):  
Andreas Darmann ◽  
Janosch Döcker ◽  
Britta Dorn ◽  
Sebastian Schneckenburger
Keyword(s):  
Computational Complexity ◽  
Simple Model ◽  
Lower Bounds ◽  
Group Activity ◽  
Core Stability ◽  
Upper And Lower Bounds ◽  
Size Constraints ◽  
Broad Variety ◽  
Special Case ◽  
Additional Constraints

AbstractSeveral real-world situations can be represented in terms of agents that have preferences over activities in which they may participate. Often, the agents can take part in at most one activity (for instance, since these take place simultaneously), and there are additional constraints on the number of agents that can participate in an activity. In such a setting, we consider the task of assigning agents to activities in a reasonable way. We introduce the simplified group activity selection problem providing a general yet simple model for a broad variety of settings, and start investigating its special case where upper and lower bounds of the groups have to be taken into account. We apply different solution concepts such as envy-freeness and core stability to our setting and provide a computational complexity study for the problem of finding such solutions.

Collective dynamics of neuronal activities in various modular networks

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Myung Uk Park ◽  
Yonghee Bae ◽  
Kyo-Seok Lee ◽  
Jun Ho Song ◽  
Sun-Mi Lee ◽  
...  
Keyword(s):  
Network Structure ◽  
Electrode Array ◽  
Modular Network ◽  
Collective Dynamics ◽  
Modular Networks ◽  
Multi Electrode Array

Three type of modular networks are constructed using polydimethylsiloxane (PDMS) microstructures fabricated on a multi-electrode array (MEA) without transfer to investigate how neuronal activities are affected by modular network structure.

scholarly journals The evolutionary origins of modularity

2013 ◽  
Vol 280 (1755) ◽  
pp. 20122863 ◽  
Author(s):  
Jeff Clune ◽  
Jean-Baptiste Mouret ◽  
Hod Lipson
Keyword(s):  
Biological Networks ◽  
Network Performance ◽  
Indirect Selection ◽  
Direct Selection ◽  
Network Nodes ◽  
Evolutionary Origins ◽  
Modular Networks ◽  
Key Driver ◽  
Selection For ◽  
The Cost

A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.

scholarly journals Breakdown of modularity in complex networks

2017 ◽  
Author(s):  
Sergi Valverde
Keyword(s):  
Simple Model ◽  
General Theory ◽  
Complex Networks ◽  
Stress Responses ◽  
Common Property ◽  
Cost Constraints ◽  
Modular Networks ◽  
Wide Range ◽  
Modular Organisation ◽  
Different Parts

The presence of modular organisation is a common property of a wide range of complex systems, from cellular or brain networks to technological graphs. Modularity allows some degree of segregation between different parts of the network and has been suggested to be a prerequisite for the evolvability of biological systems. In technology, modularity defines a clear division of tasks and it is an explicit design target. However, many natural and artificial systems experience a breakdown in their modular pattern of connections, which has been associated to failures in hub nodes or the activation of global stress responses. In spite of its importance, no general theory of the breakdown of modularity and its implications has been advanced yet. Here we propose a new, simple model of network landscape where it is possible to exhaustively characterise the breakdown of modularity in a well-defined way. We found that evolution cannot reach maximally modular networks under the presence of functional and cost constraints, implying the breakdown of modularity is an adaptive feature.

scholarly journals Mimicry in motion and morphology: do information limitation, trade-offs or compensation relax selection for mimetic accuracy?

2021 ◽  
Vol 288 (1952) ◽  
pp. 20210815
Author(s):  
Donald James McLean ◽  
Marie E. Herberstein
Keyword(s):  
Simple Model ◽  
Incomplete Information ◽  
Complex Traits ◽  
Selection Pressure ◽  
Comparative Tests ◽  
Trade Offs ◽  
Taxonomic Range ◽  
Selection For ◽  
Imperfect Mimicry ◽  
General Explanation

Many animals mimic dangerous or undesirable prey as a defence from predators. We would expect predators to reliably avoid animals that closely resemble dangerous prey, yet imperfect mimics are common across a wide taxonomic range. There have been many hypotheses suggested to explain imperfect mimicry, but comparative tests across multiple mimicry systems are needed to determine which are applicable, and which—if any—represent general principles governing imperfect mimicry. We tested four hypotheses on Australian ant mimics and found support for only one of them: the information limitation hypothesis. A predator with incomplete information will be unable to discriminate some poor mimics from their models. We further present a simple model to show that predators are likely to operate with incomplete information because they forage and make decisions while they are learning, so might never learn to properly discriminate poor mimics from their models. We found no evidence that one accurate mimetic trait can compensate for, or constrain, another, or that rapid movement reduces selection pressure for good mimicry. We argue that information limitation may be a general principle behind imperfect mimicry of complex traits, while interactions between components of mimicry are unlikely to provide a general explanation for imperfect mimicry.

scholarly journals Emergence of multicellularity in a model of cell growth, death and aggregation under size-dependent selection

2015 ◽  
Vol 12 (102) ◽  
pp. 20140982 ◽  
Author(s):  
Salva Duran-Nebreda ◽  
Ricard Solé
Keyword(s):  
Cell Death ◽  
Simple Model ◽  
Cluster Size ◽  
Life Forms ◽  
Cell Populations ◽  
Evolutionary Patterns ◽  
Size Dependent ◽  
Clonal Development ◽  
Selection For ◽  
Waste Accumulation

How multicellular life forms evolved from unicellular ones constitutes a major problem in our understanding of the evolution of our biosphere. A recent set of experiments involving yeast cell populations have shown that selection for faster sedimenting cells leads to the appearance of stable aggregates of cells that are able to split into smaller clusters. It was suggested that the observed evolutionary patterns could be the result of evolved programmes affecting cell death. Here, we show, using a simple model of cell–cell interactions and evolving adhesion rates, that the observed patterns in cluster size and localized mortality can be easily interpreted in terms of waste accumulation and toxicity-driven apoptosis. This simple mechanism would have played a key role in the early evolution of multicellular life forms based on both aggregative and clonal development. The potential extensions of this work and its implications for natural and synthetic multicellularity are discussed.

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