Chris' Home Page

Dr Chris Christodoulou

Professor

	Contact information

	Room FST 01 113

	Department of Computer Science
	University of Cyprus
	75 Kallipoleos Avenue
	P.O. Box 20537
	1678 Nicosia, Cyprus

	E-Mail:     cchrist @ ucy.ac.cy
	Telephone:  +357 22892752
  	Fax:        +357 22892701

Biography

Chris received a BEng degree in Electronic Engineering from Queen Mary and Westfield College, University of London and a PhD in Neural Networks/Computational Neuroscience from King's College, University of London. He also holds a BA degree in German from Birkbeck College, University of London. He worked as a Postgraduate Research Assistant (1991-1995) and a Postdoctoral Research Associate (1995-1997) at the Centre for Neural Networks, King's College, University of London. He joined Birkbeck College, University of London as a Lecturer in 1997 where he worked till 2005 and he has also been a Visiting Research Fellow at King's College (1997-2001). Currently, he is a Professor at the University of Cyprus after joining in 2005. Since 2005 he is also a Visiting Research Fellow at Birkbeck College.

Academic research interests

Computational Neuroscience

Neural Networks

Machine Learning

Neural coding and the effect of high firing irregularity on learning
Modelling of self-control behaviour
Computational neuronal modelling
Multi-agent reinforcement learning (with spiking/non-spiking agents)
Machine Learning/Neural Network applications

Research Group

Computational Intelligence and Neuroscience research group (CIN)

Publications

Guest Editorial of Journal Special Issues

Christodoulou, C , D'Onofrio, G., Stiber, M. and Villa, A. E. P. (2023). Editorial: Selected papers from the 14th International Neural Coding Workshop, Seattle, Washington, 2021, BioSystems, 223, Article 104818 (8 papers, 77 pages), doi: 10.1016/j.biosystems.2022.104818

Christodoulou, C , Kostal, L. and Sacerdote, L. (2020). Editorial, Neural Coding 2018, Special issue of BioSystems on Selected papers presented at the Thirteenth International Workshop on Neural Coding, Turin, Italy, 2018, BioSystems, 187, Article 104049 (11 papers, 111 pages). doi: 10.1016/j.biosystems.2019.104049

Christodoulou, C. , Kostal, L. and Bueschges (2017). Editorial, Special issue of BioSystems on Selected papers presented at the Twelveth International Workshop on Neural Coding, Cologne, Germany, 2016, BioSystems, 161, 1-2 (8 papers, pp. 1-76). doi: 10.1016/j.biosystems.2017.09.012

Lucas, P., Rospars J-P. and Christodoulou, C. (2015). Editorial, Special issue of BioSystems on Selected papers presented at the Eleventh International Workshop on Neural Coding, Versailles, France, 2014, BioSystems, 136, 1-2 (15 papers, pp. 1-142).

Lansky, P., Rospars, J-P. and Christodoulou, C. (2013). Foreword, Special issue of Brain Research on Selected papers presented at the Tenth International Workshop on Neural Coding, Prague, Czech Republic, 2-7 September 2012, Brain Research, 1536, 1 (14 papers, pp. 1-176).

Christodoulou, C. , Lansky, P. and Rospars, J-P. (2012). Foreword, Special issue of Brain Research on Selected papers presented at the International Workshop on Neural Coding, Limassol, Cyprus, 29 October - 3 November 2010, Brain Research, 1434, 1 (23 papers, pp. 1-284).

Articles in Refereed Archival Journals

Pafitis, M., Constantinou, M. and Christodoulou C. (2024). Accelerating training of convolutional neural networks with Hessian-free optimization for detecting Alzheimer s disease in brain MRI. IEEE Access, 12, 176184-176198. doi: 10.1109/ACCESS.2024.3487114

Nikodemou, A. and Christodoulou, C. (2024). Deconstructing emotions in self-control through computational modeling. Cognitive Systems Research, 88, Article 101294. doi: 10.1016/j.cogsys.2024.101294

Charalampous, K., Agathocleous, M., Christodoulou, C. and Promponas, V. (2022). Solving the Protein Secondary Structure Prediction Problem With the Hessian Free Optimization Algorithm. IEEE Access, 10, 27759-27770. doi: 10.1109/ACCESS.2022.3156888

Zavou, C., Kkoushi, A., Koutsou, A. and Christodoulou, C. (2017). Synchrony measure for a neuron driven by excitatory and inhibitory inputs and its adaptation to experimentally-recorded data. BioSystems, 161, 46-56. doi: 10.1016/j.biosystems.2017.09.010

Kanev, J., Koutsou, A., Christodoulou, C. and Obermayer, K. (2016). Integrator or Coincidence Detector: A Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode. Neural Computation, 28(10), 2091-2128. doi: 10.1162/NECO_a_00875

Vassiliades, V. and Christodoulou, C. (2016). Behavioral Plasticity Through the Modulation of Switch Neurons. Neural Networks, 74, 35-51.

Koutsou, A., Kanev, J., Economidou, M. and Christodoulou, C. (2016). Integrator or Coincidence Detector - What shapes the relation of stimulus synchrony and the operational mode of a neuron? Mathematical Biosciences and Engineering, 13(3), 521-535.

Koutsou, A., Bugmann, G. and Christodoulou, C. (2015). On learning time delays between the spikes from different input neurons in a biophysical model of a pyramidal neuron. BioSystems, 136, 80-89.

Vassiliades, V. and Christodoulou, C. (2013). Toward Nonlinear Local Reinforcement Learning Rules through Neuroevolution. Neural Computation, 25(11), 3020-3043.

Koutsou, A., Kanev, J., and Christodoulou, C. (2013). Measuring input synchrony in the Ornstein-Uhlenbeck neuronal model through input parameter estimation. Brain Research, 1536, 97-106.

Zachariou, M. and Christodoulou, C. (2013). A Biophysical Model of Endocannabinoid-Mediated Short Term Depression of Excitation in Hippocampus. BMC Neuroscience, 14(Suppl 1):P66.

Zachariou, M., Alexander, S., Coombes, S. and Christodoulou, C. (2013). A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition. PLoS ONE, 8(3): e58926.

Koutsou, A., Christodoulou, C., Bugmann, G. and Kanev, J. (2012). Distinguishing the Causes of Firing with the Membrane Potential Slope. Neural Computation, 24(9), 2318-2345.

Kountouris, P., Agathocleous, M., Promponas, V.J., Christodoulou, G., Hadjicostas, S., Vassiliades, V. and Christodoulou, C. (2012). A comparative study on filtering protein secondary structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(3), 731-739.

Cleanthous, A. and Christodoulou, C. (2012). Learning optimisation by high firing irregularity. Brain Research, 1434, 115-122.

Jayne, C., Lanitis, A. and Christodoulou, C. (2012). One-to-many neural network mapping techniques for face image synthesis. Expert Systems With Applications, 39(10), 9778-9787.

Vassiliades, V., Cleanthous, A. and Christodoulou, C. (2011). Multiagent Reinforcement Learning: Spiking and Nonspiking Agents in the Iterated Prisoner's Dilemma. IEEE Transactions on Neural Networks, 22(4), 639-653.

Christodoulou, C. and Cleanthous, A. (2011). Does high firing irregularity enhance learning? Neural Computation, 23(3), 656-663.

Moustra, M., Avraamides, M. and Christodoulou, C. (2011). Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals. Expert Systems With Applications, 38, 15032-15039.

Jayne, C., Lanitis, A. and Christodoulou, C. (2011). Neural network methods for one-to-many multi-valued mapping problems. Neural Computing and Applications, 20, 775-785.

Christodoulou, C., Banfield, G. and Cleanthous, A. (2010). Self-control with spiking and non-spiking neural networks playing games. Journal of Physiology - Paris, 104, 108-117.

Christodoulou, C. and Cleanthous A. (2010). Spiking neural networks with different reinforcement learning (RL) schemes in a multiagent setting. Chinese Journal of Physiology, 53(6), 447-453.

Krambia Kapardis, M., Christodoulou, C. and Agathocleous, M. (2010). Neural Networks: The panacea in fraud detection? Managerial Auditing Journal, 25, 659-678.

Cleanthous, A. and Christodoulou, C. (2009). Is self control a learned strategy employed by a reward maximizing brain? BMC Neuroscience, 10(Suppl 1):P14.

Lanitis, A., Draganova, C. and Christodoulou, C. (2004). Comparing Different Classifiers for Automatic Age Estimation. IEEE Transactions on Systems, Man, and Cybernetics; Part B: Cybernetics, 34, 1, 621-628.

Christodoulou, C., Bugmann, G. and Clarkson, T. G. (2002). A Spiking Neuron Model: Applications and Learning. Neural Networks, 15, 891-908.

Christodoulou, C. (2002). On the firing variability of the integrate-and-fire neurons with partial reset in the presence of inhibition. Neurocomputing, 44-46, 81-84.

Clarkson, T. G., Christodoulou, C., Guan, Y., Gorse, D., Romano-Critchley, D. and Taylor, J. G. (2001). Speaker identification for security systems using reinforcement-trained pRAM neural network architectures. IEEE Transactions on Systems, Man, and Cybernetics; PART C: Applications & Reviews, 31, 1, 65-76.

Christodoulou, C. and Bugmann, G. (2001). Coefficient of variation vs. mean interspike interval curves: What do they tell us about the brain? Neurocomputing, 38-40, 1141-1149.

Lanitis, A., Taylor, C. and Christodoulou, C. (2001). Automatic Person Identification using Face Images and Speech Signals. Y Magazine, Cyprus Research Promotion Foundation, Feb. 2001, Issue 1, 29-32 (published in both Greek and English).

Christodoulou, C. and Bugmann, G. (2000). Near-Poisson-Type Firing Produced by Concurrent Excitation and Inhibition. BioSystems, 58, 41-48.

Bugmann, G., Christodoulou, C. and Taylor, J. G. (1997). Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset. Neural Computation, 9(5), 985-1000.

Clarkson, T. G., Ng, C. K., Christodoulou, C. and Bean, J. (1993). Review of hardware pRAMs. Neural Network World, 3, No. 5, 551-564.

Refereed Articles in Books, Book Series, Compiled Volumes and Full Conference Proceedings

K. Christou, C. Christodoulou and V. Vassiliades (2023). Quality Diversity optimization using the IsoLineDD operator: forward and backward directions are equally important. GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation , Lisbon, Portugal, New York, NY: ACM, pp. 639-642. doi: 10.1145/3583133.3590737

Dionysiou, A., Agathocleous, M., Christodoulou, C. and Promponas, V. (2018). Convolutional Neural Networks in combination with Support Vector Machines for complex sequential data classification. Artificial Neural Networks and Machine Learning - ICANN 2018, Lecture Notes in Computer Science, ed. by V. Kurkova, Y. Manolopoulos, B. Hammer, L. Iliadis, I. Maglogiannis, Cham: Springer, 11140, Part II, 444-455. doi: 10.1007/978-3-030-01421-6_43

Agathocleous, M., Christodoulou, C.,Promponas, V., Kountouris, P. and Vassiliades, V. (2016). Training Bidirectional Recurrent Neural Network Architectures with the Scaled Conjugate Gradient Algorithm. Artificial Neural Networks and Machine Learning - ICANN 2016, Lecture Notes in Computer Science, ed. by A. E. P. Villa, P. Masulli and A. J. P. Rivero, Springer-Verlag, 9886, 123-131.

Jayne, C., Lanitis, A. and Christodoulou, C. (2012). Automatic Landmark Location for Analysis of Cardiac MRI Images. Proceedings of the 13 International Conference on Engineering Applications of Neural Networks (EANN), London, September 2012, Communications in Computer and Information Science, ed. by C. Jayne, S. Yue and L.S. Iliadis, 311, Berlin: Springer-Verlag, 203-212.

Lambrou, I., Vassiliades, V. and Christodoulou, C. (2012). An extension of a hierarchical reinforcement learning algorithm for multiagent settings. Recent Advances in Reinforcement Learning, EWRL (European Workshop on Reinforcement Learning) 2011, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), ed. by S. Sanner and M. Hutter, 7188, Berlin: Springer-Verlag, 261-272.

Agathocleous, M., Christodoulou, G., Promponas, V., Christodoulou, C., Vassiliades, V. and Antoniou, A. (2010). Protein Secondary Structure Prediction with Bidirectional Recurrent Neural Nets: can weight updating for each residue enhance performance? In: AIAI 2010. H. Papadopoulos, A. S. Andreou and M. Bramer (eds.), IFIP International Federation for Information Processing AICT, Berlin: Springer-Verlag, 339, 128-137.

Vassiliades, V. and Christodoulou, C. (2010). Multiagent Reinforcement Learning in the Iterated Prisoner's Dilemma: Fast Cooperation through Evolved Payoffs. Proc. of the International Joint Conference on Neural Networks (IJCNN'10), part of the World Congress on Computational Intelligence (WCCI'10), Barcelona, Spain, pp. 2828-2835.

Krambia Kapardis, M., Christodoulou, C. and Agathocleous, M. (2010). Usage of Neural Networks as a tool in fraud detection. Proceedings of the 33rd European Accounting Association Annual Congress (EAA2010), Constantinople, Turkey, May 2010 (AU.PS. 20; ID: 7373) (19 pages).

Vassiliades, V., Cleanthous, A. and Christodoulou, C. (2009). Multiagent Reinforcement Learning with Spiking and Non Spiking Agents in the Iterated Prisoner's Dilemma. Artificial Neural Networks - ICANN 2009, Lecture Notes in Computer Science, ed. by C. Alippi, M. Polycarpou, C. Panayiotou, G. Ellinas, Springer, 5768, 737-746.

Cleanthous, A. and Christodoulou, C. (2009). On the Psychology and Modelling of Self Control. In: Connectionist Models of Behaviour and Cognition II, Progress in Neural Processing, ed. by J. Mayor, N. Ruh, K. Blunkett, World Scientific, 18, 229-240.

Draganova, C., Lanitis, A. and Christodoulou, C. (2009). Isolating Stock Prices Variation with Neural Networks. In: Engineering Applications of Neural Networks, Communications in Computer and Information Science, D. Palmer Brown, C. Draganova, E. Pimenidis, H. Mouratidis (eds.), Springer, 43, 401-408.

Draganova, C., Lanitis, A. and Christodoulou, C. (2005). Restoration of Partially Occluded Shapes of Faces Using Neural Networks. In: Computer Recognition Systems, Advances in Soft Computing, ed. by M. Kurzynski, E. Puchala, M. Wozniak, A. Zolnierek, Springer, 30, 767-774.

Banfield, G. and Christodoulou, C. (2005). Can Self-Control be Explained through Games? In: Modelling Language, Cognition and Action, Progress in Neural Processing, ed. by A. Cangelosi, G. Bugmann, R. Borisyuk, World Scientific, 16, 321-330.

Christodoulou, C., Clarkson, T.G., Bugmann, G. and Taylor, J.G. (2000). Analysis of Fluctuation-Induced firing in the presence of inhibition. Proc of the Int Joint Conf on Neural Networks 2000 (IJCNN'2000), Como, Italy, IEEE Computer Society Press, Vol. III, 115-120.

Bugmann, G., Christodoulou, C. & Taylor, J. G. (1999). Role of temporal integration and fluctuation detection in the highly irregular firing of a Leaky Integrator neuron with partial reset. In: Neural Codes and Distributed Representations: Foundations of Neural Computation. L. Abbott and T. J. Sejnowski (eds.), MIT Press (ISBN 0-262- 51100-2), 171-186. (Note: This book "collects, by topic, the most significant papers that have appeared in the journal Neural Computation over the past nine years" - this paper is the same as the 1997 Neural Computation paper above).

Christodoulou, C., Clarkson T. G. and Taylor, J. G. (1996). Speaker Identification using pRAM Neural Networks. Solving Engineering Problems with Neural Networks, Proc. of the Int. Conf. on Engineering Applications of Neural Networks 1996 (EANN '96), ed. by A. B. Bulsari, S. Kallio and D. Tsapsinos, London, UK, 265-268.

Christodoulou, C., Clarkson, T. G. and Taylor, J. G. (1995). Temporal pattern detection and recognition using the Temporal Noisy-Leaky Integrator neuron model with the postsynaptic delays trained using Hebbian Learning. Proc. of the World Congress on Neural Networks (WCNN '95) , Washington, DC, USA, Vol. 3, 34-37.

Christodoulou, C. and Clarkson, T. G. (1995). A review on the stochastic firing behaviour of real neurons and how it can be modelled. From Natural to Artificial Neural Computation, Lecture Notes in Computer Science, ed. by J. Mira and F. Sandoval, Springer-Verlag, 930, 223-230.

Christodoulou, C. and Clarkson, T. G. (1995). Postsynaptic delay training for temporal pattern detection and recognition using Hebbian learning. Proc of the Int Conf on Digital Signal Processing (DSP'95) , Limassol, Cyprus, Vol 1, 415-420.

Christodoulou, C., Clarkson, T. G., Bugmann, G. and Taylor, J. G. (1994). Modelling of the high firing variability of real cortical neurons with the Temporal Noisy-Leaky Integrator neuron model. Proc. of the IEEE Int. Conf. on Neural Networks (ICNN '94), part of the IEEE World Congress on Computational Intelligence (WCCI '94), Orlando, Florida, USA, Vol. IV, 2239-2244.

Christodoulou, C., Bugmann, G., Clarkson, T. G. and Taylor, J. G. (1993). The Temporal Noisy-Leaky Integrator neuron model. In: Recent Advances in Neural Networks, ed. by R. Beale and M. D. Plumbley, Prentice Hall.

Christodoulou, C., Bugmann, G., Clarkson, T. G. and Taylor, J. G. (1993). The Temporal Noisy-Leaky Integrator neuron with additional inhibitory inputs. New Trends in Neural Computation, Lecture Notes in Computer Science, ed. by J. Mira, J. Cabestany and A. Prieto, Springer-Verlag, 686, 465-470.

Christodoulou, C. and Bugmann, G. (1993). The use of pRAMs for modelling the quantal neurotransmitter release process in the Temporal Noisy-Leaky Integrator neuron model. Proc. of the WNNW '93 (Weightless Neural Network Workshop), Computing with Logical Neurons, ed. by N. M. Allinson, York, 117-122.

Christodoulou, C., Bugmann, G., Taylor, J. G. and Clarkson, T. G. (1992). An extension of the Temporal Noisy-Leaky Integrator neuron and its potential applications. Proc. of the Int. Joint Conf. on Neural Networks, Beijing, III, 165-170.

Christodoulou, C., Taylor, J. G., Clarkson, T. G. and Bugmann, G. (1992). A Temporal Noisy-Leaky Integrator Neuron constructed using pRAMs. Artificial Neural Networks II, ed. by I. Aleksander and J. G. Taylor, Elsevier, Vol. 2, 1475-1478.

Christodoulou, C., Taylor, J. G., Clarkson, T. G. and Gorse, D. (1992). The Noisy-Leaky Integrator model implemented using pRAMs. Proc. of the Int. Joint Conf. on Neural Networks, Baltimore, I, 178-183.

Refereed Short Papers and Abstracts

Pafitis, M., Constantinou, M. and Christodoulou, C. (2022). Hessian-Free Optimization for Accelerating the Training of Convolutional Neural Network Classifiers of Brain Magnetic Resonance Images. Proc. of the 14th Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2022, p. 14.

Kanev, J., Koutsou, A., Christodoulou, C. and Obermayer, K. (2021). The Difference Neuron: A versatile abstract spiking neuron model. Proceedings of the 14th International Workshop on Neural Coding, Seattle, Washington, USA (online), July 2021, Paper 26.

Hadjiantonis, G., Bugmann, G. and Christodoulou, C. (2021). Characterization of inputs from filtered intracellular recordings. Proceedings of the 14th International Workshop on Neural Coding, Seattle, Washington, USA (online), July 2021, Paper 25.

Kanev, J., Koutsou, A., Christodoulou, C. and Obermayer, K. (2018). The Difference Neuron - a new spiking Neuron Model that learns. Proceedings of the Bernstein Conference 2018, Berlin, Sept. 2018, W39. doi: 10.12751/nncn.bc2018.0057

Christodoulou, C. (2018). Modelling the relationship between self-control and consciousness. Proceedings of the 13th International Workshop on Neural Coding, Turin, Italy, Sept. 2018, pp. 11-13.

Dimitriou, P., Agathocleous, M., Promponas, V. J. and Christodoulou, C. (2018). Fast and accurate protein secondary structure prediction using Clockwork Recurrent Neural Networks. Proceedings of the 17th European Conference on Computational Biology, Athens, Greece, Sept. 2018, Abstract for Poster #P_Pr043.

Dionysiou, A., Agathocleous, M., Christodoulou, C. and Promponas, V. J. (2018). A Hybrid Machine Learning Algorithm for Complex Sequential Data Classification, Using a Novel Data Representation Method. Proc. of the 11th Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2018, p. 8.

Dimitriou, P., Agathocleous, M., Promponas, V. J. and Christodoulou, C. (2018). Clockwork Recurrent Neural Networks for fast and accurate protein secondary structure prediction. Proc. of the 11th Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2018, p. 16.

Maslioukova, M. I., Agathocleous, M., Christodoulou, C. and Promponas, V. (2017). A novel Bidirectional Echo State Network for Protein Secondary Structure Prediction. Proc. of the 10th Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2017, p. 9.

Zavou, C., Koutsou, A. and Christodoulou, C. (2016). Correlating pre-synaptic synchrony with experimentally recorded intracellular membrane potential. Proc. of the 12th Int. Workshop on Neural Coding, Cologne, Germany, Aug./Sept. 2016, pp. 104-105.

Kkoushi, A., Koutsou, A. and Christodoulou, C. (2016). Synchrony measure for a neuron driven by excitatory and inhibitory inputs. Proc. of the 12th Int. Workshop on Neural Coding, Cologne, Germany, Aug./Sept. 2016, pp. 29-30.

Agathocleous, M., Christodoulou, C.,Promponas, V., Kountouris, P. and Vassiliades, V. (2016). Using Second-order learning algorithms to train Bidirectional Recurrent Neural Networks. Proceedings of the 9th Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, June 2016, p. 19.

Koutsou, A., Bugmann, G. and Christodoulou, C. (2014). Learning temporal correlations in input spike trains. Proc. of the 11th Int. Workshop on Neural Coding , Versailles, France, Oct. 2014, pp. 98-99.

Koutsou, A., Kanev, J. Economidou, M. and Christodoulou, C. (2014). Comparison of synchrony measures and implications for inter-network neural connectivity. Proc. of the 11th Int. Workshop on Neural Coding , Versailles, France, Oct. 2014, p. 60.

Agathocleous, M. and Christodoulou, C. (2014). Decoding EEG motion signals with Echo State Networks: A Brain-Computer Interface approach. Proceedings of the 7th Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2014, p. 20.

Zachariou, M., Coombes, S. and Christodoulou, C. (2013). The modulating role of cannabinoids in hippocampal networks: A computational modeling study. Society for Neuroscience meeting 2013, San Diego, CA, USA, November 2013, Poster #: 678.21/MMM32.

Koutsou, A., Christodoulou, C., Bugmann, G. & Kanev, J. (2012). Understanding the Neural Code through Exploration of the Causes of Firing. Book of Abstracts of the Research Work of Postgraduate Students, Faculty of Pure and Applied Sciences, University of Cyprus , Nicosia, Cyprus, Nov. 2012, pp. 21 (Abstract for Poster P-30).

Vassiliades, V., Christodoulou, C., Cleanthous, A. and Lambrou, I. (2012). Explorations in Reinforcement Learning. Book of Abstracts of the Research Work of Postgraduate Students, Faculty of Pure and Applied Sciences, University of Cyprus , Nicosia, Cyprus, Nov. 2012, p. 21 (Abstract for Poster P-28).

Koutsou, A., Lansky, P., Kanev, J. and Christodoulou, C. (2012). Input synchrony estimation in the Ornstein-Uhlenbeck model through the slope of depolarisation at threshold crossing. Proc. of the 10th Int. Workshop on Neural Coding, Prague, Czech Republic, Sept. 2012, pp. 65-66.

Kanev, J., Koutsou, A. and Christodoulou, C. (2012). Can discrete Response-Stimulus Correlation distinguish Integration from Coincidence Detection? Proc. of the 10th Int. Workshop on Neural Coding, Prague, Czech Republic, Sept. 2012, pp. 55-56.

Zachariou, M., Alexander, S., Coombes, S. and Christodoulou, C. (2012). A biophysical model of endocannabinoid-mediated plasticity in hippocampus. Proceedings of the FENS (Federation of European Neuroscience Societies) Forum of Neuroscience, Barcelona, Spain, July 2012, Abstract Number 5118.

Zachariou, M., Alexander, S., Coombes, S. and Christodoulou, C. (2012). A biophysical model of endocannabinoid-mediated plasticity in hippocampus. Neurodynamics: a workshop on heterogeneity, noise, delays, and plasticity in neural systems, Edinburgh, UK, March 2012.

Zachariou, M., Alexander, S., Coombes, S. and Christodoulou, C. (2011). A biophysical model of Endocannabinoid-mediated plasticity in hippocampus. Workshop on Learning and Plasticity, Marseille, France, November 2011.

Agathocleous, M., Hadjicostas, S., Kountouris, P., Promponas, V., Vassiliades, V. and Christodoulou, C. (2011). Improving protein secondary structure prediction using evolutionary strategies and RBF networks.Proceedings of the 6th conference of the Hellenic Society for Computational Biology & Bioinformatics - HSCBB11, Patras, Greece, October 2011, p.34.

Kountouris, P., Agathocleous, M., Promponas, V., Christodoulou, G., Hadjicostas, S., Vassiliades, V. and Christodoulou, C. (2011). A comparative study on filtering protein secondary structure prediction. 19th Annual International Conference on Intelligent Systems for Molecular Biology and 10th European Conference on Computational Biology (ISMB/ECCB), Vienna, Austria, July 2011, Abstract for Poster W39.

Agathocleous, M., Kountouris, P., Promponas, V., Christodoulou, G., Vassiliades, V. and Christodoulou, C. (2011). Training bidirectional recurrent neural networks with Conjugate gradient-type algorithms for protein secondary structure prediction. 19th Annual International Conference on Intelligent Systems for Molecular Biology and 10th European Conference on Computational Biology (ISMB/ECCB), Vienna, Austria, July 2011, Abstract for Poster W67.

Kountouris, P., Agathocleous, M., Promponas, V., Christodoulou, G., Hadjicostas, S., Vassiliades, V. and Christodoulou, C. (2011). A comparative study on filtering protein secondary structure prediction. Proceedings of the 4th Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2011, p. 13.

Christodoulou, C. and Cleanthous, A. (2010). High firing irregularity enhances learning. Proc. of the 9th Int. Workshop on Neural Coding, Limassol, Cyprus, Oct./Nov. 2010, pp. 19-20.

Koutsou, C., Christodoulou, C., Bugmann, G. and Kanev, J. (2010). Distinguishing the causes of firing with the membrane potential slope. Proc. of the 9th Int. Workshop on Neural Coding, Limassol, Cyprus, Oct./Nov. 2010, pp. 57-58.

Koutsou, A. and Christodoulou, C. (2010). Measuring single neuron operational modes using a metric based on the membrane potential slope. Proceedings of the 3rd Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2010, p. 21.

Vassiliades, V. and Christodoulou, C. (2010). Evolving internal rewards for effective multiagent learning in game theoretical situations. Proceedings of the 3rd Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2010, p. 22.

Agathocleous, M., Christodoulou, G., Promponas, V., Christodoulou, C., Vassiliades, V. and Antoniou, A. (2010). Per residue weight updating procedure for Protein Secondary Structure Prediction with Bidirectional Recurrent Neural Networks. Proceedings of the 3rd Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2010, p. 23.

Koutsou, A., Christodoulou, C., C. and Kanev, J. (2010). Causes of firing in cortical neurons revisited: Temporal integration vs. coincidence detection. Proceedings of the Conference on Research in Encoding And Decoding of Neural Ensembles (AREADNE), Santorini, Greece, June 2010, p. 71.

Koutsou, A., Christodoulou, C., Kanev, J. and Bugmann, G. (2010). Quantification of the contribution of temporal integration and coincidence detection to the irregularity of cortical neurons at high rates. Proceedings of the Workshop on Spike Train Measures and Their Applications to Neural Coding, Plymouth, United Kingdom, June 2010; available at: http://helen.pion.ac.uk/stm2010/poster-abstracts.html

Cleanthous, C. and Christodoulou, C. (2010). How dynamical changes in the payoff matrix of the Iterated Prisoner's Dilemma enhance the understanding of how to attain self-control behaviour. Proceedings of the 14th International Conference on Cognitive and Neural Systems, Boston, USA, May 2010, p. 67.

Christodoulou, C. and Cleanthous, A. (2009). Modelling and Resolving Conscious Conflict through Learned Self Control Behaviour. Proc of the Conference Consciousness and it Measures, November December 2009, Limassol, Cyprus, pp. 27-28.

Christodoulou, C. and Cleanthous, A. (2009). Spiking Neural Networks with Different Reinforcement Learning Schemes in a Multiagent Setting. Proceedings of the 8th International Workshop on Neuronal Coding, Tainan, Taiwan, May 2009, pp. 57-59.

Vassiliades, V., Cleanthous, A. and Christodoulou, C. (2009). Multiagent Reinforcement Learning: Spiking and Non spiking Neural Network Agents. Proceedings of the 2nd Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2009, p. 16.

Agathocleous, M. Antoniou, A., Christodoulou, C. and Promponas, V. (2009). Genetic Algorithm Optimisation of a Bidirectional Recurrent Neural Network for Protein Secondary Structure Prediction. Proceedings of the 2nd Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2009, p. 17.

Christodoulou, C. and Cleanthous, A. (2009). Modelling Self control behaviour with Spiking Neural Networks in a Multiagent Reinforcement Learning Framework. Proceedings of the 2nd Cyprus Workshop on Signal Processing and Informatics, Nicosia, Cyprus, July 2009, p. 18.

Cleanthous, A. and Christodoulou, C. (2008). Can Networks of Leaky Integrate and Fire Neurons with Spike based Reinforcement Learning Play Games? Proceedings of the Computational and Systems Neuroscience 2008 Workshop for Spiking Networks and Reinforcement Learning, Snow Bird, Utah, USA, March 2008; available at: http://cosyne.org/wiki/Workshop_speaker_Aristodemos_Cleanthous

Christodoulou, C. and Cleanthous, A. (2008). On the psychology and modelling of self control. Proceedings of the 11th Neural Computation and Psychology Workshop, Oxford, UK, July 2008.

Banfield, G. and Christodoulou, C. (2007). Precommiting to an uncertain future. Proceedings of the 10th Neural Computation and Psychology Workshop, Dijon, France, April 2007; available at: http://leadserv.u bourgogne.fr/ncpw10/abstract_Banfield.php

Christodoulou, C. and Banfield, G. (2007). Self Control with Spiking Neural Networks Playing Games. Proceedings of the 7th International Workshop on Neuronal Coding, Montevideo, Uruguay, November 2007, p. 97.

Banfield, G. and Christodoulou, C. (2006). Can Self Control be Explained by Evolutionary Game Theory? Proceedings of the Workshop for Mathematical and Computational Neuroscience 2006, Brisbane, Australia, August 2006.

Banfield, G. and Christodoulou, C. (2004). Can Self-Control be Explained through Games? Proc of the 9th Neural Computation and Psychology Workshop: Modelling Language, Cognition and Action, (Book of Abstracts), Plymouth, UK, September 2004, 10.

Banfield, G. and Christodoulou, C. (2003). On reinforcement learning in two player "real-world" games. Proc of the Joint Int Conference on Cognitive Science, Sydney, Australia, July 2003, 22.

Draganova, C., Lanitis, A. and Christodoulou, C. (2003). Isolating sources of variation in multivariate distributions using Neural Networks. Proc of the Int Workshop on Computational Management Science, Economics, Finance and Engineering, Limassol, Cyprus, March 2003, 50.

Bugmann, G. and Christodoulou, C. (2001). Learning temporal correlation between input neurons by using dendritic propagation delays and stochastic synapses. Proceedings of the Int. Workshop on Neural Coding , Plymouth, UK, Sept. 2001, 131-132.

Christodoulou, C. (2001). On the variability of the Integrate-and-Fire neurons with partial reset in the presence of inhibition. Proc of the Computational Neuroscience Meeting 2001 (CNS '01) , San Franscisco & Pacific Grove, California, USA, June/July 2001, 26.

Christodoulou, C. and Bugmann, G. (2000). Coefficient of Variation (CV) vs Mean Interspike Interval (ISI) curves: what do they tell us about the brain? Proc of the Computational Neuroscience Meeting 2000 (CNS'2000) , Belgium, July 2000, 29.

Christodoulou, C. and Bugmann, G. (1999). Poisson-Type Firing Produced by Concurrent Excitation and Inhibition. Proc. of the Int. Workshop on Neuronal Coding 1999 (NCWS '99), Osaka, Japan, October 1999, 41-44.

Technical Reports

Agathocleous, M., Kountouris, P., Promponas, V. and Christodoulou, C. (2011). A general Neural Network Library for the Protein Secondary Structure Prediction. Technical Report Number TR-11-10, November 2011, Department of Computer Science, University of Cyprus (25 pages).

Christodoulou, G., Christodoulou, C. and Promponas, V. (2010). Investigation of learning methods for bidirectional recurrent neural networks as applied to protein secondary structure prediction. Technical Report Number TR-10-03, May 2010, Department of Computer Science, University of Cyprus (in Greek, 162 pages).

Agathocleous, M., Christodoulou, C. and Promponas, V. (2009). Protein secondary structure prediction with bidirectional recurrent neural networks. Technical Report Number TR-09-01, December 2009, Department of Computer Science, University of Cyprus (in Greek, 114 pages).

Christodoulou, C. and Clarkson, T. G. (1996). The Temporal Noisy-Leaky Integrator neuron model. Internal Research Report Number 115/SCS/96 ISBN 1-898-783-06-03, May 1996, Signals Circuits and Systems Research Group, Dept. of Electronic and Electrical Eng., King's College, University of London, London WC2R 2LS, UK.

PhD Thesis

Christodoulou, C. (1997). The Temporal Noisy-Leaky Integrator Artificial Neuron Model and its Applications. Ph.D Thesis, King's College, University of London.

Revised on 4 December 2024