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

Kaluza, Pablo; Inoue, Masayo

doi:10.1140/epjb/e2016-70172-9

Cited by 6 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In effect, in a system with a fixed complex target this relationship can be difficult to elucidate since the final system generates several tasks simultaneously. An example of these systems are genetic regulatory networks with adaptive responses [19,20]. These networks do not only generate adaptive responses, but also they require delays for their onsets.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Coevolution of functional flow processing networks

Kaluza

2017

Eur. Phys. J. B

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Coevolution of functional flow processing networks

Kaluza

2017

Eur. Phys. J. B

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, the errors of such systems with respect to target functions are not defined during these processing intervals. Examples of these systems are feed-forward neural networks [2], spiking neural networks [3], gene regulatory systems with adaptive responses [4,5], signal transduction networks [6], etc.…”

Section: Introductionmentioning

confidence: 99%

Autonomous learning by simple dynamical systems with a discrete-time formulation

Bilen¹,

Kaluza²

2017

Eur. Phys. J. B

Self Cite

View full text Add to dashboard Cite

We present a discrete-time formulation for the autonomous learning conjecture.The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.

show abstract

“…Model genetic oscillatory networks with prescribed oscillation periods could be designed by evolutionary optimization [10] and made robust against knock-outs of genes, removal of regulatory interactions, and introduction of noise [11]. Moreover, genetic regulatory networks with predefined adaptive dynamical responses [12,13] or giving rise to definite stationary expression patterns [14] could be constructed in a similar way. Stochastic Monte Carlo optimization methods with replica exchange were employed to design phase oscillator networks with improved synchronization tolerance against heterogeneities [15] or noise [16].…”

Section: Introductionmentioning

confidence: 99%

Autonomous learning by simple dynamical systems with delayed feedback

Kaluza

Mikhailov

2014

Phys. Rev. E

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 6 publications

References 22 publications

Coevolution of functional flow processing networks

Coevolution of functional flow processing networks

Autonomous learning by simple dynamical systems with a discrete-time formulation

Autonomous learning by simple dynamical systems with delayed feedback

Contact Info

Product

Resources

About