The retrieval dynamics of neural networks constructed from local and nonlocal learning rules are compared via computer simulations and shown to be very similar. Furthermore, the simulations show no hope for determining the long time behavior of the system in terms of the first step dynamics as has been advocated by Kepler and Abbott.Les dynamiques de rappel de réseaux neuronaux construits à partir de règles d'apprentissage locales et non locales sont comparées à l'aide de simulations numériques et apparaissent très similaires. Nos simulations indiquent qu'il ne semble pas possible de déterminer le comportement des systèmes aux temps longs en fonction de la dynamique aux premiers instants, comme cela avait été récemment suggéré par Kepler et...
We propose tools to probe the nature of attractors in dynamical systems. These include the activity ...
In this paper we study an attractor network with units that compete locally for activation and we pr...
An attractor neural network on the small-world topology is studied. A learning pattern is presented ...
Attractor properties of a popular discrete-time neural network model are illustrated through numeric...
Local learning neural networks have long been limited by their inability to store correlated pattern...
The neural net computer simulations which will be presented here are based on the acceptance of a se...
A dynamic threshold, which controls the nature and course of learning, is a pivotal concept in Page'...
I propose tools to probe the nature of the retrieval attractors in neural networks. These include th...
We perform an extensive numerical investigation on the retrieval dynamics of the synchronous Hopfiel...
I propose tools to probe the nature of the retrieval attractors in neural networks. These include th...
Attractor properties of a popular discrete-time neural network model are illustrated through numeric...
We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamic...
In this thesis, I show that a single class of unsupervised learning rules that can be inferred from ...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
Experimental data show that biological synapses behave quite differently from the symbolic synapses ...
We propose tools to probe the nature of attractors in dynamical systems. These include the activity ...
In this paper we study an attractor network with units that compete locally for activation and we pr...
An attractor neural network on the small-world topology is studied. A learning pattern is presented ...
Attractor properties of a popular discrete-time neural network model are illustrated through numeric...
Local learning neural networks have long been limited by their inability to store correlated pattern...
The neural net computer simulations which will be presented here are based on the acceptance of a se...
A dynamic threshold, which controls the nature and course of learning, is a pivotal concept in Page'...
I propose tools to probe the nature of the retrieval attractors in neural networks. These include th...
We perform an extensive numerical investigation on the retrieval dynamics of the synchronous Hopfiel...
I propose tools to probe the nature of the retrieval attractors in neural networks. These include th...
Attractor properties of a popular discrete-time neural network model are illustrated through numeric...
We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamic...
In this thesis, I show that a single class of unsupervised learning rules that can be inferred from ...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
Experimental data show that biological synapses behave quite differently from the symbolic synapses ...
We propose tools to probe the nature of attractors in dynamical systems. These include the activity ...
In this paper we study an attractor network with units that compete locally for activation and we pr...
An attractor neural network on the small-world topology is studied. A learning pattern is presented ...