Evolution of canalizing Boolean networks

Szejka, Agnes; Drossel, Barbara

doi:10.1140/epjb/e2007-00135-2

Cited by 41 publications

(51 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[15]. More recently, works addressing the evolvability of robustness in BNs have been presented [1,5,27]. In the same direction is a recent paper, in which the global fitness function is defined as the sum of single functions, each related to a network parameter linked to network robustness [8].…”

Section: Design Methodologymentioning

confidence: 99%

On the Design of Boolean Network Robots

Roli

Manfroni

Pinciroli

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Dynamical systems theory and complexity science provide powerful tools for analysing artificial agents and robots. Furthermore, they have been recently proposed also as a source of design principles and guidelines. Boolean networks are a prominent example of complex dynamical systems and they have been shown to effectively capture important phenomena in gene regulation. From an engineering perspective, these models are very compelling, because they can exhibit rich and complex behaviours, in spite of the compactness of their description. In this paper, we propose the use of Boolean networks for controlling robots' behaviour. The network is designed by means of an automatic procedure based on stochastic local search techniques. We show that this approach makes it possible to design a network which enables the robot to accomplish a task that requires the capability of navigating the space using a light stimulus, as well as the formation and use of an internal memory.

show abstract

Section: Design Methodologymentioning

confidence: 99%

On the Design of Boolean Network Robots

Roli

Manfroni

Pinciroli

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…In a multi-layer perceptron, for example, this might be done by scaling the output of a node's activation function. In a Boolean network, it would be similar to the use of canalising functions (such as AND) in which one or more inputs strongly determine a node's output [48], and which have been hypothesised to improve robustness by stabilising a network's attractors [99]. It also resembles multiplicative coupling in autocatalytic networks, a mechanism that allows nodes to exercise control over other nodes, and which is discussed in [27] in the context of connectionist architectures.…”

Section: Higher-order Functionsmentioning

confidence: 99%

Biochemical connectionism

et al. 2013

View full text Add to dashboard Cite

In this paper, we discuss computational architectures that are motivated by connectionist patterns that occur in biochemical networks, and speculate about how this biochemical approach to connectionism might complement conventional neural approaches. In particular, we focus on three features of biochemical networks that make them distinct from neural networks: their diverse, complex nodal processes, their emergent organisation, and the dynamical behaviours that result from higher-order, self-modifying processes. We also consider the growing use of evolutionary algorithms in the design of connectionist systems, noting how this enables us to explore a wider range of connectionist architectures, and how the close relationship between biochemical networks and biological evolution can guide us in this endeavour.

show abstract

“…The adaptive walk is a hill climbing process that leads to a local fitness maximum and thus can yield insights into the fitness landscape of a system. In [5], it was found that there is a huge plateau with the maximum fitness value that spans the network configuration space. Mutations change the connections and the update functions of the nodes.…”

Section: Network Evolved For Robustnessmentioning

confidence: 99%

“…In fact, it was found that networks that were generated by some evolutionary algorithm may have a very long but unique attractor that attracts all of state space [11], or that in such networks initially similar states may diverge at first exponentially fast but then converge to the same fixed point [5]. These are two examples where features of the 'frozen' and 'chaotic' phases are united within the same network.…”

Section: Introductionmentioning

confidence: 99%

Perturbation propagation in random and evolved Boolean networks

Fretter¹,

Szejka²,

Drossel³

2009

New J. Phys.

View full text Add to dashboard Cite

We investigate the propagation of perturbations in Boolean networks by evaluating the Derrida plot and modifications of it. We show that even small Random Boolean Networks agree well with the predictions of the annealed approximation, but non-random networks show a very different behaviour. We focus on networks that were evolved for high dynamical robustness. The most important conclusion is that the simple distinction between frozen, critical and chaotic networks is no longer useful, since such evolved networks can display properties of all three types of networks. Furthermore, we evaluate a simplified empirical network and show how its specific state space properties are reflected in the modified Derrida plots.

show abstract

Evolution of canalizing Boolean networks

Cited by 41 publications

References 19 publications

On the Design of Boolean Network Robots

On the Design of Boolean Network Robots

Biochemical connectionism

Perturbation propagation in random and evolved Boolean networks

Contact Info

Product

Resources

About