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Browsing by Subject "Neural Networks (Computer)"

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    • Article  

      Autoregressive and cepstral analyses of motor unit action potentials 

      Pattichis, Constantinos S.; Elia, Andreas G. (1999)
      Quantitative electromyographic signal analysis in the time domain for motor unit action potential (MUAP) classification and disease identification has been well documented over recent years. Considerable work has also been ...

    • Article  

      Behavioral plasticity through the modulation of switch neurons 

      Vassiliades, Vassilis; Christodoulou, Chris C. (2016)
      A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by ...

    • Article  

      Classification capacity of a modular neural network implementing neurally inspired architecture and training rules 

      Poirazi, Panayiota; Neocleous, Costas K.; Pattichis, Constantinos S.; Schizas, Christos N. (2004)
      A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab-but not between slabs- have the ...

    • Article  

      Comparing Different Classifiers for Automatic Age Estimation 

      Lanitis, A.; Draganova, C.; Christodoulou, Chris C. (2004)
      We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used ...

    • Article  

      Computer-aided classification of breast cancer nuclei 

      Schnorrenberg, F.; Pattichis, Constantinos S.; Schizas, Christos N.; Kyriacou, Kyriacos C.; Vassiliou, M. (1996)
      Breast cancer is the most common malignancy affecting the female population in industrialized countries. Prognostic factors, such as steroid receptors visualized in biopsy slides, provide critical information to oncologists ...

    • Article  

      Determining neutralization serotypes of HIV type 1 by neural networks 

      Kostrikis, Leontios G.; Michalopoulou, Z. -H; Cao, Yun Zhen; Moore, J. P.; Ho, David D. (1996)

    • Article  

      Distinguishing the causes of firing with themembrane potential slope 

      Koutsou, Achilleas; Christodoulou, Chris C.; Bugmann, G.; Kanev, J. (2012)
      In this letter, we aim to measure the relative contribution of coincidence detection and temporal integration to the firing of spikes of a simple neuron model. To this end, we develop a method to infer the degree of synchrony ...

    • Article  

      Does high firing irregularity enhance learning? 

      Christodoulou, Chris C.; Cleanthous, A. (2011)
      In this note, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the ...

    • Article  

      Efficient training of neural network models in classification of electromyographic data 

      Pattichis, Constantinos S.; Charalambous, Chris; Middleton, Lefkos T. (1995)

    • Article  

      An embedded saliency map estimator scheme: Application to video encoding 

      Tsapatsoulis, Nicolas; Rapantzikos, K.; Pattichis, Constantinos S. (2007)
      In this paper we propose a novel saliency-based computational model for visual attention. This model processes both top-down (goal directed) and bottom-up information. Processing in the top-down channel creates the so ...

    • Article  

      Improved detection of breast cancer nuclei using modular neural networks 

      Schnorrenberg, F.; Tsapatsoulis, Nicolas; Pattichis, Constantinos S.; Schizas, Christos N.; Kollias, S.; Vassiliou, M.; Adamou, Adamos K.; Kyriacou, Kyriacos C. (2000)
      A modular neural network-based approach to detect and classify breast cancer nuclei stained for steroid receptors in hispathological sections is evaluated. The system named biopsy analysis support system (BASS) is designed ...

    • Article  

      Learning optimisation by high firing irregularity 

      Cleanthous, A.; Christodoulou, Chris C. (2012)
      In a network of leaky integrate-and-fire (LIF) neurons, we investigate the functional role of irregular spiking at high rates. Irregular spiking is produced by either employing the partial somatic reset mechanism on every ...

    • Article  

      Ligand - Based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks 

      Afantitis, Antreas; Melagraki, G.; Koutentis, Panayiotis Andreas; Sarimveis, H.; Kollias, G. (2011)
      In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic ...

    • Article  

      Multiagent reinforcement learning: Spiking and nonspiking agents in the Iterated Prisoner's Dilemma 

      Vassiliades, Vassilis; Cleanthous, A.; Christodoulou, Chris C. (2011)
      This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory ...

    • Article  

      A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis 

      Afantitis, Antreas; Melagraki, G.; Sarimveis, H.; Koutentis, Panayiotis Andreas; Markopoulos, J.; Igglessi-Markopoulou, O. (2006)
      A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl] -6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV ...

    • Article  

      Self-control with spiking and non-spiking neural networks playing games 

      Christodoulou, Chris C.; Banfield, G.; Cleanthous, A. (2010)
      Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that ...

    • Article  

      Spiking neural networks with different reinforcement learning (RL) schemes in a multiagent setting 

      Christodoulou, Chris C.; Cleanthous, A. (2010)
      This paper investigates the effectiveness of spiking agents when trained with reinforcement learning (RL) in a challenging multiagent task. In particular, it explores learning through rewardmodulated spike-timing dependent ...

    • Article  

      A spiking neuron model: Applications and learning 

      Christodoulou, Chris C.; Bugmann, G.; Clarkson, T. G. (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 ...

    • Article  

      Time-scale analysis of motor unit action potentials 

      Pattichis, Constantinos S.; Pattichis, Marios S. (1999)
      Quantitative analysis in clinical electromyography (EMG) is very desirable because it allows a more standardized, sensitive and specific evaluation of the neurophysiological findings, especially for the assessment of ...

    • Article  

      Toward nonlinear local reinforcement learning rules through neuroevolution 

      Vassiliades, Vassilis; Christodoulou, Chris C. (2013)
      We consider the problem of designing local reinforcement learning rules for artificial neural network (ANN) controllers. Motivated by the universal approximation properties of ANNs, we adopt an ANN representation for the ...

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