We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biol...
This paper describes a novel approach to representing experimental biological data in metabolic networks. The aim is to allow biologists to visualise and analyse the data in the c...
In this paper, we describe a means for automatically building very large neural networks (VLNNs) from definition texts in machine-readable dictionaries, and demonstrate the use of...
This paper presents a novel self-creating neural network scheme which employs two resource counters to record network learning activity. The proposed scheme not only achieves the b...
We introduce a game-theoretic model of diffusion of technologies, advertisements, or influence through a social network. The novelty in our model is that the players are intereste...
Noga Alon, Michal Feldman, Ariel D. Procaccia, Mos...