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Training bidirectional recurrent neural network architectures with the scaled conjugate gradient algorithm
(2016)
Predictions on sequential data, when both the upstream and downstream information is important, is a difficult and challenging task. The Bidirectional Recurrent Neural Network (BRNN) architecture has been designed to deal ...
On modelling cognitive styles of users in adaptive interactive systems using artificial neural networks efi papatheocharous1 efi.papatheocharous
(2012)
User modelling in interactive Web systems is an essential quality to optimally filter, personalise and adapt their content and functionality to serve the intrinsic needs of individual users. The mechanism for obtaining the ...
Coefficient of variation vs. mean interspike interval curves: What do they tell use about the brain?
(2001)
A number of models have been produced recently to explain the high variability of natural spike trains (Softky and Koch, J. Neurosci. 13 (1) (1993) 334). These models use a range of different biological mechanisms including ...
A review on the stochastic firing behaviour of real neurons and how it can be modelled
(1995)
The types of spike trains recorded in real neurons from different parts of the brain, can either be completely random or bursty. Certainly, at very, high firing rates regular spike trains are observed. This paper examines ...
A spiking neuron model: Applications and learning
(2002)
This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational ...
The temporal noisy-leaky integrator neuron with additional inhibitory inputs
(1993)
The Temporal Noisy-Leaky Integrator (TNU) neuron model with additional inhibitory inputs is presented together with its theoretical mathematical basis. The TNU is a biologically inspired hardware neuron which models ...
Modelling of the high firing variability of real cortical neurons with the temporal noisy-leaky integrator neuron model
(IEEE, 1994)
Using the Temporal Noisy-Leaky Integrator (TNLI) neuron model with reset, we observed that high firing variability can be achieved for certain input parameter values which results from the temporal summation of noise in ...
On the firing variability of the integrate-and-fire neurons with partial reset in the presence of inhibition
(2002)
It has been reported (Phys. Rev. Lett. 82 (1999) 4731) that the firing variability of integrate-and-fire (I&F) neurons is strongly dependent on the level of inhibitory input unlike the Hodgkin-Huxley and FitzHugh-Nagumo ...
Analysis of fluctuation-induced firing in the presence of inhibition
(IEEE, 2000)
This paper examines the computational role of inhibition as it moves towards balancing concurrent excitation using the biologically-inspired Temporal Noisy-Leaky Integrator (TNLI) neuron model. The TNLI incorporates ...
Testing the predictability of the Cyprus Stock Exchange: The case of an emerging market
(IEEE, 2000)
A systematic investigation of the effect of different neural network architecture alternatives for predicting the future course of stock prices in the Cyprus Stock Exchange (CSE) market is conducted. This market exhibited ...