This paper presents a simple continuous analog hardware realization of the Random Neural Network (RNN) model. The proposed circuit uses the general principles resulting from the u...
Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firin...
Abstract— This paper introduces a novel design of an artificial neural network tailored for wafer-scale integration. The presented VLSI implementation includes continuous-time a...
Johannes Schemmel, Johannes Fieres, Karlheinz Meie...
— We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a network of integrate-and-fire neurons. This biologically inspired synapse is hi...
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, ...