Abstract. In this paper we present a functional model of a spiking neuron intended for hardware implementation. Some features of biological spiking neuabstracted, while preserving ...
Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firin...
: We present the implementation of on-line Hebbian learning for NESPINN, the Neurocomputer for the simulation of spiking neurons. In order to support various forms of Hebbian learn...
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...
Abstract. One focus of recent research in the field of biologically plausible neural networks is the investigation of higher-level functions such as learning, development and modu...
Matthias Oster, Adrian M. Whatley, Shih-Chii Liu, ...