We propose and investigate a simple model for dynamic stochastic synapses that can easily be integrated into common models for neural computation. We show ...

May 15, 1999 · In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning.

Abstract. In most neural network models, synapses are treated as static weights that change only on the slow time scales of learning.

We propose and investigate a simple model for dynamic stochastic synapses that can easily be integrated into common models for neural computation. We show ...

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, how-.

Maass, W., & Zador, A. (1998). Dynamic stochastic synapses as computational units (extended abstract). Advances of Neural Information Processing Systems, 10, ...

We propose and investigate a simple model for dynamic stochastic synapses that can easily be integrated into common models for neural computation. We show ...

We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-andfire neurons (spiking ...

Dynamic stochastic synapses as computational units · Synapses as dynamic memory buffers · Emergent population activity in metric-free and metric networks of ...

Maass and Zador (1998) incorporated the processes of facilitation and depression in their “dynamic stochastic synapse” as a basic computational unit in neural ...