Design of robust flow processing networks with time-programmed responses

2017

Eur. Phys. J. B

Abstract. We present a study about the construction of functional flow processing networks that produce prescribed output patterns (target functions). The constructions are performed with a process of mutations and selections by an annealing-like algorithm. We consider the coevolution of the prescribed target functions during the optimization processes. We propose three different paths for these coevolutions in order to evolve from a simple initial function to a more complex final one. We compute several network properties during the optimizations by using the different path-coevolutions as mean values over network ensembles. As a function of the number of iterations of the optimization we find a similar behavior like a phase transition in the network structures. This result can be seen clearly in the mean motif distributions of the constructed networks. Coevolution allows to identify that feed-forward loops are responsible for the development of the temporal response of these systems. Finally, we observe that with a large number of iterations the optimized networks present similar properties despite the path-coevolution we employed.

Section: Modelsmentioning

confidence: 99%

“…Moreover, networks with high robustness against deletion of links or nodes and introduction of quenched noise have been obtained; statistical analysis of the properties of the designed networks has been performed [8,9]. Additionally, networks which generate programmable time-dependent responses have also been considered [10].…”

Section: Introductionmentioning

confidence: 99%

Coevolution of functional flow processing networks

2017

Eur. Phys. J. B

“…This kind of optimization, Metropolislike methods, have been used in the construction of genetic networks [13][14][15][16], and, flow processing networks [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Design of artificial genetic regulatory networks with multiple delayed adaptive responses*

Inoue

2016

Eur. Phys. J. B

Abstract. Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways.

“…Combinatorial optimization methods, such as stochastic Metropolis algorithms and simulated annealing, have been used in systems engineering problems [7][8][9][10][11][12][13][14][15][16]. Thus, analogs of biological signal transduction networks [7][8][9][10] and model oscillatory genetic networks with prescribed output patterns or oscillation periods [11,12] could be constructed. Moreover, the designed networks could, through further optimization, be made robust against local structural perturbations, such as deletion of links or nodes, or against noise [7][8][9][10].…”

mentioning

confidence: 99%

“…Thus, analogs of biological signal transduction networks [7][8][9][10] and model oscillatory genetic networks with prescribed output patterns or oscillation periods [11,12] could be constructed. Moreover, the designed networks could, through further optimization, be made robust against local structural perturbations, such as deletion of links or nodes, or against noise [7][8][9][10]. Model genetic networks, which could generate definite stationary expression patterns and thus imitate processes relevant for biological morphogenesis [13] or to produce required temporal responses [14], were developed and investigated.…”

mentioning

confidence: 99%

Autonomous learning by simple dynamical systems with delayed feedback

Mikhailov

2014

Phys. Rev. E

A general scheme for the construction of dynamical systems able to learn generation of the desired kinds of dynamics through adjustment of their internal structure is proposed. The scheme involves intrinsic time-delayed feedback to steer the dynamics towards the target performance. As an example, a system of coupled phase oscillators, which can, by changing the weights of connections between its elements, evolve to a dynamical state with the prescribed (low or high) synchronization level, is considered and investigated.