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Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscienc ...
The synchronous oscillatory activity characterizing many neurons in a network is often considered to be a mechanism for representing, binding, conveying, and organizing information. A number of models have been proposed to explain high-frequency oscillatio ...
The wireless multiple-unicast problem is considered over a layered network, where the rates of transmission are limited by the relaying and interference effect. The deterministic model is used to capture the broadcasting and multiple access effects. The ca ...
A new programmable neighborhood mechanism for the Winner Takes Most (WTM) self-organizing Kohonen map has been proposed in this paper. Described circuit is an asynchronous solution, which does not require the controlling clock generator. The winning neuron ...
The wireless multiple-unicast problem is considered over a layered network, where the rates of transmission are limited by the relaying and interference effect. The deterministic model introduced in 131 is used to capture the broadcasting and multiple acce ...
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We study the dynamics of a network consisting of N diffusively coupled, stable- limit-cycle oscillators on which individual frequencies are parametrized by wk,k=1,...,N. We introduce a learning rule which influences the wk by driving the system t ...
Nowadays, advances in telecommunication network design and performance analysis often rely on dedicated software tools. Unfortunately, developing new tools is a very time and resources consuming activity. To rationalise development costs, existing applicat ...
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...
Artificial neural networks represent a simple but efficient way to model and correct known errors existing between commonly used density functional computations and experimental data. The recently proposed X1 approach combines B3LYP energies with a neural- ...
memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...
