In this paper it is analysed how emerging behaviour of an adaptive network can be related to characteristics of the adaptive network’s structure (which includes the adaptation structure). In particular, this is addressed for mental networks based on Hebbian learning. To this end relevant properties of the network and the adaptation that have been identified are discussed. As a result it has been found that in an achieved equilibrium state the value of a connection weight has a functional relation to the values of the connected states
ArticleWe present a mathematical analysis of the effects of Hebbian learning in random recurrent neu...
This paper presents the double loop feedback model, which is used for structure and data flow modell...
In this chapter it is analysed how emerging behaviour in an adaptive social network for bonding can ...
In this chapter another challenge is analysed for how emerging behaviour of an adaptive network can ...
In this paper, the challenge for dynamic network modeling is addressed how emerging behavior of an a...
In this paper it is analysed how emerging behaviour of an adaptive network can be related to charact...
We present a novel produce which allows a neural network to evolve to a quasi-optimal connectivity s...
In this chapter, the notion of network reification is introduced: a construction by which a given (b...
The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural ...
International audienceThe aim of the present paper is to study the effects of Hebbian learning in ra...
We evolve small continuous-time recurrent neural networks with fixed weights that perform Hebbian le...
In this paper the notion of network reification is introduced: a construction by which a given (base...
This chapter contributes an analysis of how in mental and social processes, humans often apply speci...
Networks provide an intuitive, declarative way of modeling with a wide scope of applicability. In ma...
This book addresses the challenging topic of modeling adaptive networks, which often have inherently...
ArticleWe present a mathematical analysis of the effects of Hebbian learning in random recurrent neu...
This paper presents the double loop feedback model, which is used for structure and data flow modell...
In this chapter it is analysed how emerging behaviour in an adaptive social network for bonding can ...
In this chapter another challenge is analysed for how emerging behaviour of an adaptive network can ...
In this paper, the challenge for dynamic network modeling is addressed how emerging behavior of an a...
In this paper it is analysed how emerging behaviour of an adaptive network can be related to charact...
We present a novel produce which allows a neural network to evolve to a quasi-optimal connectivity s...
In this chapter, the notion of network reification is introduced: a construction by which a given (b...
The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural ...
International audienceThe aim of the present paper is to study the effects of Hebbian learning in ra...
We evolve small continuous-time recurrent neural networks with fixed weights that perform Hebbian le...
In this paper the notion of network reification is introduced: a construction by which a given (base...
This chapter contributes an analysis of how in mental and social processes, humans often apply speci...
Networks provide an intuitive, declarative way of modeling with a wide scope of applicability. In ma...
This book addresses the challenging topic of modeling adaptive networks, which often have inherently...
ArticleWe present a mathematical analysis of the effects of Hebbian learning in random recurrent neu...
This paper presents the double loop feedback model, which is used for structure and data flow modell...
In this chapter it is analysed how emerging behaviour in an adaptive social network for bonding can ...