Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

Feedforward neural networks with constrained weights

Synaptic plasticity in spiking neural networks (SP2INN): a system approach

In this paper, we present a digital system called (SP/sup 2/INN) for simulating very large-scale spiking neural networks (VLSNNs) comprising, e.g., 1000000 neurons with several million connections in total. SP/sup 2/INN makes it possible to simulate VLSNN with features such as synaptic short term plasticity, long term plasticity as well as configurable connections. For such VLSNN the computatio...

Plasticity in memristive devices for spiking neural networks

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse p...

Spiking Neural P Systems – A Natural Model for Sorting Networks

This paper proposes two simulations of sorting networks with spiking neural P systems. A comparison between different models is also made.

Humanoid Robot Control Using Spiking Neural Networks

In recent years there has been an increasing amount of interest in spiking neural networks, which are more biologically realistic than rate based models. Whilst a lot of simulation work has been carried out on spiking neural networks, relatively little research has been done on the use of spiking neural networks to control robots in an environment. The main obstacle to the development of embodi...