Rebecca Naylor

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Kati (Katalin) Prajda

University of Vienna

Eitan Elaad

Ariel University

Edouard Robberechts

Aix-Marseille University

Anna Horolets

University of Warsaw

Roshin Mohan

Kerala Veterinary and Animal Science University

Maria Elena Rivera Heredia

Universidad Michoacana de San Nicolás de Hidalgo

Vet.Dr. Hussein Abdillahi Ahmed (Xuseen Dhoobaale)

Gollis University

K. Brookhuis

University of Groningen

Dragos Simandan

Brock University

Ivan Roussev

University of economics - Varna

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Papers by Rebecca Naylor

Using Behavioral Experiments to Expand Our Horizons and Deepen Our Understanding of Logistics and Supply Chain Decision Making

Journal of Business Logistics, 2011

... Sagi, A., and Friedland, N. 2007. “The Cost ... Management. Rebecca Walker Naylor (PhD The Un... more

Neural Pipelines: For the Co-ordination of Activity in a Multi-layered Neural Network

The`Neural Pipeline' is introduced as an articial neural network architecture that controls infor... more The`Neural Pipeline' is introduced as an articial neural network architecture that controls information ow using its own connection structure. The architecture is multi-layered with`external' connections between the layers to control the data. Excitatory connections transfer data from each layer to the next and inhibitory feedback connections run from each layer to the previous layer. Using these connections a layer can temporarily silence the previous layer and stop further inputs until it nishes processing. When excitation and inhibition are balanced, waves of activity propagate sequentially through the layers after each input; this is`correct' behaviour. When the system is`over' inhibited, the inhibitory feedback outweighs the excitation from the input. At least one layer remains inhibited for too long so further inputs cannot stimulate the layer. Over inhibition can be corrected by increasing the delay between inputs. When the system is`under' inhibited the excitation in the layer is larger than the inhibition. The layer is therefore not silenced and continues to spike. In the layers, excitatory and inhibitory spiking neurons are randomly interconnected. Changing layer parameters inuences the system behaviour. Recommendations for correct behaviour include: low neuron connectivity and balancing the external inhibition and layer activity. With variations of only the internal topology and weights, all three behaviours can be exhibited. Each layer is trained as a separate Liquid State Machine, with readout neurons trained to respond to a particular input. A set of six shapes can be learnt by all layers of a three layer Neural Pipeline. The layers are trained to recognise dierent features; layer 1 recognising the position while layer 2 identies the shape. The system can cope when the same noisy signal is applied to all inputs, but begins to make mistakes when dierent noise is applied to each input neuron. The thesis introduces and develops the Neural Pipeline architecture to provide a platform for further work.

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