Dr Vassilis Cutsuridis

Profiles

Dr Vassilis Cutsuridis

Associate Professor in Computer Science

School of Engineering, Computing and Mathematics (Faculty of Science and Engineering)

Vassilis Cutsuridis is an Associate Professor of Computer Science at the University of Plymouth. He is a Visiting Professor at Tokyo University of Agriculture and Technology, Japan and Visiting Professor at the National and Kapodistrian University of Athens, Greece. His research interests reside in the area of neuromorphic AI and its applications in health, medicine and environment. He has published over 100 journal papers, conference papers, books, edited books, and book chapters.

Qualifications

PhD in Computational Neuroscience, University of Athens, Greece
M.A. in Cognitive and Neural Systems, Boston University, USA
MSc in Theoretical Physics, Wichita State University, USA
BSc in Physics and Maths, Wichita State University, USA

Professional membership

UK's Machine Vision Society (2017 - now)
British Oculomotor Society (2018 - now)
British Neuroscience Association (2017 - now)
Organization of Computational Neuroscience (2007 - now)
Convergent Science Network of Bio-mimetic and Bio-hybrid Systems (2010 - now)

Roles on external bodies

External Research Council Grant Reviewing:

  • Engineering and Physical Sciences Research Council (EPSRC), UK
  • Medical Research Council (MRC), UK
  • Biotechnology and Biological Sciences Research Council (BBSRC), UK
  • Netherlands Organization for Scientific Research (NOW)
  • European Research Council (ERC)
  • Swiss National Science Foundation
  • Research and Innovation Foundation of Cyprus
  • General Secretariat for Research and Technology (Greece)
  • Human Brain Project
  • Horizon 2020

Teaching

Teaching interests

COMP1003 - Algorithms, Data Structures and Maths
COMP3000 - Computing Project

Staff serving as external examiners

MSc in Computational Neuroscience, University of Nottingham, UK

Research

Research interests

My lab’s research interests fall under the general category of Natural and Artificial Intelligence. We are broadly interested to reverse engineer how the brain and mind work in order to extract the neural algorithms for the design and development of more efficient intelligent methods for complex data analysis in Healthcare, Drug Discovery, Medical Imaging, Biomedical Signal Processing and Robotics.
But why is necessary to understand how the brain works in order to investigate intelligent systems?
Because artificial intelligence driven by the new deep learning techniques and large data volumes, although has demonstrated huge potential and attracted huge investments globally, it has encountered big problems – it not only needs collect huge datasets and spend enormous time and resources to be trained on them, but also the trained system cannot deal effectively with any never encountered before (novel) data. In reality, this means a well-trained autonomous vehicle cannot cope with unfamiliar roads, or a well-trained robot cannot work again if the environment slightly modified.
On the other hand, human and animal brains are unparalleled in their ability to rapidly, and on their own, adapt and learn from changing and unexpected environmental contingencies with very limited resources. Brains are designed to achieve autonomous adaptation to a constantly changing world. How did the brain design evolve to such recognition mastery even with limited resources is one of the fundamental questions my lab addresses. The challenge of understanding how brains work is, on a technical level, the challenge of understanding how an intelligent system can so rapidly and stably self-organize its successful behaviours in response to an unpredictably changing, or non-stationary, world. Scientific advances currently face challenges of dealing with uncertainty and change. The brain is a paradigmatic example of an advanced natural system that is unparalleled in realizing such a property. AI inspired by human/animal brains will open the road to new computing technologies with the potential to revolutionize the industry, economy and society. Such investigation will inevitably have significant impact to autonomous real-time learning systems to achieve human-like intelligence capabilities.

Publications
Journals
  1. Cutsuridis V. (2024). Neuromorphic cognitive learning systems: the future of AI? Cognitive Computation (invited), 16: 1433–1435
  2. Sun Z, Cutsuridis V, Caiafa CF, Sole-Casals J. (2023). Brain Simulations and Spiking Neural Networks. Cogn Comput 1-3, https://doi.org/10.1007/s12559-023-10156-1
  3. Andreakos N, Yue S, Cutsuridis V*. Systematic Evaluation of Associative Memory Retrieval in a Brain Microcircuit Model. Submitted
  4. Gong I, Yu M, Cutsuridis V, Kollias S, Pearson S. (2023). A Novel Model Fusion Approach for Greenhouse Crop Yield Prediction. Horticulturae 9(1), 5. https://doi.org/10.3390/horticulturae9010005 (IF: 3.03)
  5. Lei F, Peng Z, Liu M, Zhang Y, Cutsuridis V, Yue S. (2022). A Robust Visual System for Looming Cue Detection Against Translation Motion. IEEE Transactions on Neural Networks and Learning Systems, 34 (11), 8362-8376. doi: 10.1109/TNNLS.2022.3149832 (IF: 10.451)
  6. Gong I, Yu M, Jiang S, Cutsuridis V, Kollias S, Pearson S. (2021). Studies of evolutionary algorithms for the reduced Tomgro model calibration for modelling tomato yields. Smart Agricultural Technology, 1: 100011
  7. Gong I, Yu M, Jiang S, Cutsuridis V, Pearson S. (2021). Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors, 21(13), 4537(IF: 3.275)
  8. Andreakos N, Yue S, Cutsuridis V*. (2021). Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus. Brain Informatics (invited), 8:9. https://doi.org/10.1186/s40708-021-00131-7 (IF: 3.53)
  9. Cutsuridis V*, Jiang S, Dunn M, Rosser A, Brauwn J, Erichsen J. (2021). Neural modeling of antisaccade performance of healthy controls and early-stage Huntington’s disease patients. Chaos, 31: 013121. doi: 10.1063/5.0021584 (IF: 2.79)
  10. Cutsuridis V*. (2019). Improving the recall performance of a brain mimetic microcircuit model. Cognitive Computation, 11(5): 644-55. https://doi.org/10.1007/s12559-019-09658-8 (IF: 5.47)
  11. Cutsuridis V*. (2019). Memory prosthesis: Is it time for a deep neuromimetic computing approach. Frontiers in Neuroscience, 13: 667.https://doi.org/10.3389/fnins.2019.00667 (IF: 3.566)
  12. Moustafa, A. A., Hassan, M., Hewedi, D., Garami, J., Alashwal, H., Zaki, N., Seo, S.Y., Cutsuridis, V. , Angulo, S. L., Hewedi, E., Natesh, J. Y. , Herzallah, M. M., Frydecka, D., Misiak, B., Salama. M, Mohamed, W., El Haj, M., Hornbeger, M. (2018). Genetic Underpinnings in Alzheimer’s disease – a review. Reviews in the Neurosciences, 28 (1): 21-38. (IF: 3.390)
  13. Cutsuridis, V* (2017). Behavioural and computational varieties of response inhibition in eye movements. Philosophical Transactions of the Royal Society B: Biological Sciences, 372 (1718): 20160196 (IF: 6.004)
  14. Cutsuridis V*, Yoshida M. (2017). Memory Processes in Medial Temporal Lobe: Experimental, Theoretical and Computational Approaches. Frontiers in Systems Neuroscience, 1-5 (IF: 2.207)
  15. Cutsuridis V*, Moustafa A. (2016). Multiscale Models of Pharmacological, Immunological and Neurostimulation Treatments in Alzheimer’s Disease. Drug Discovery Today: Disease Models (Elsevier), 19: 85-91 (IF: 6.4)
  16. Cutsuridis V*. (2015). Neural Competition via Lateral Inhibition between Decision Processes and Not a STOP Signal Accounts for the Antisaccade Performance in Healthy and Schizophrenia Subjects. Front. Neurosci. 9:5. doi: 10.3389/fnins.2015.00005 (IF: 2.041)
  17. Saravanan V, Cui A, Gootjes-Dreesbach L, Cutsuridis V*, Yoshida M*. (2015). Transition between encoding and consolidation/replay dynamics via cholinergic modulation of CAN current: A modelling study. Hippocampus 25(9): 1052-70. doi: 10.1002/hipo.22429 (IF: 4.343)
  18. Cutsuridis V*, Poirazi P. (2015). A computational study on how theta modulated inhibition can account for the long temporal delays in the entorhinal-hippocampal loop. Neurobiology of Learning and Memory, 120: 69-83 (IF: 3.923)
  19. Psarou M, Stefanou S, Papoutsi A, Tzilivaki A, Cutsuridis V, Poirazi P. (2014). A simulation study on the effects of dendritic morphology on layer V PFC pyramidal cell firing behaviour. Frontiers in Cellular Neuroscience, 8: 287 (IF: 4.662)
  20. Cutsuridis V*, Kumari V, Ettinger, U. (2014). Antisaccade performance in schizophrenia: A Neural Model of Decision Making in the Superior Colliculus. Front. Neurosci. 8:13. doi: 10.3389/fnins.2014.00013 (IF: 2.079)
  21. Papoutsi A, Sidiropoulou K, Cutsuridis V, Poirazi P. (2013). Induction and modulation of persistent activity in a layer V PFC microcircuit model. Frontiers in Neural Circuits, doi: 10.3389/fncir.2013.00161 (IF: 1.948)
  22. Cutsuridis V*, Taxidis V. (2013). Deciphering the CA1 inhibitory circuits in sharp wave ripple complexes. Frontiers in Systems Neuroscience, 7:13, doi: 10.3389/fnsys.2013.00013 (IF: 3.06)
  23. Cutsuridis V*, Taylor JG. (2013). A Cognitive Control Architecture for the Perception-Action Cycle in Robots and Agents. Cognitive Computation, 5: 383-95 (IF: 1.873)
  24. Cutsuridis V*. (2013). Interaction of Inhibition and Triplets of Excitatory Spikes Modulates the NMDA-R Mediated Synaptic Plasticity in a Computational Model of Spike Timing Dependent Plasticity. Hippocampus, 23(1): 75-86 (IF: 4.782)
  25. Taylor JG, Cutsuridis V*, Hartley M, Althoefer K, Nanayakara T. (2013). Observational Learning: Basis, Experimental Results, Models and Implications to Robotics. Cognitive Computation, 5: 340-354. (IF: 1.873)
  26. Cutsuridis V*, Hussain A. (2013). In memory of John G Taylor: A polymath scholar, Cognitive Computation, 5(3): 279-280. (IF: 1.873)
  27. Cutsuridis V*. (2012). The Perception-…-Action Cycle Cognitive Architecture and Autonomy: A View from the Brain. Journal of Artificial General Intelligence, 3(2):36-38
  28. Cutsuridis V*. (2012). Bursts shape the NMDA-R mediated spike timing dependent plasticity curve: Role of burst interspike interval and GABA inhibition. Cognitive Neurodynamics, 6(5): 421-441 (IF: 0.572)
  29. Cutsuridis V*. (2012). Deciphering the mechanisms of episodic memory from a computational modeler’s point of view. Hippocampus, 22: 1645 (IF: 5.39)
  30. Cutsuridis V*, Hasselmo M. (2012). GABAergic modulation of gating, timing and theta phase precession of hippocampal neuronal activity during theta oscillations. Hippocampus, 22: 1597-1621. (IF: 5.39)
  31. Cutsuridis V*, Hasselmo M. (2011). Spatial memory sequence encoding and replay during modeled theta and ripple oscillations, Cognitive Computation, 3: 554-74. (IF: 2.032)
  32. Cutsuridis V*. (2011). Origins of a repetitive and co-contractive pattern of muscle activation in Parkinson’s disease. Neural Networks, 24(6): 592-601 (IF: 3.294)
  33. Cutsuridis V*. (2011). GABA inhibition modulates NMDA-R mediated spike timing dependent plasticity (STDP) in a biophysical model. Neural Networks, 24(1): 29-42. (IF: 3.294)
  34. Cutsuridis V, Heida C, Duch W, Doya K. (2011). Neurocomputational models of brain disorders. Neural Networks, 24(6): 513-514. (IF: 3.294)
  35. Taylor JG, Cutsuridis V. (2011). Saliency, attention, active visual search and picture scanning. Cognitive Computation, 3: 1-3. (IF: 2.032)
  36. Cutsuridis V*, Graham BP, Cobb S. (2010). Encoding and retrieval in the hippocampal CA1 microcircuit model. Hippocampus, 20(3): 423-446. (IF: 4.591)
  37. Cutsuridis V*. (2009). A cognitive model of saliency, overt attention and picture scanning. Cognitive computation, 1: 292-299
  38. Cutsuridis V*, Wenneckers T. (2009). Hippocampus, microcircuits and associative memory. Neural Networks, 22(8): 1120-8. (IF: 3.118)
  39. Cutsuridis V*, Cobb S, Graham BP. (2009). Modelling the STDP symmetry-to-asymmetry transition in the presence of GABAergic inhibition. Neural Network World, 19(5): 471-81 (IF: 0.6)
  40. Cutsuridis V, Wennekers T, Graham BP, Vida I, Taylor JG. (2009). Microcircuits – Their structure, dynamics and their role for brain function. Neural Networks, 22(8): 1037-1200 (IF: 3.118)
  41. Cutsuridis V*, Kahol P. (2008). Derivation and Evaluation of the Fourth Moment of NMR Lineshape in Zero-Field. Solid State NMR, 34: 191-195. (IF: 1.763)
  42. CutsuridisV*. (2007). Does Abnormal Reciprocal Inhibition Lead to Co-contraction of Antagonist Muscles? A Modeling Study. International Journal of Neural Systems, 17(4): 319-327 (IF: 0.913)
  43. Cutsuridis V*, Smyrnis N, Evdokimidis I, Perantonis S. (2007). A Neural Network Model of Decision Making in an Antisaccade Task by the Superior Colliculus. Neural Networks, 20(6): 690-704. (IF: 3.170)
  44. Cutsuridis V*, Kahramanoglou I, Smyrnis N, Evdokimidis I, Perantonis S. (2007). A Neural Variable Integrator Model of Decision Making in an Antisaccade Task. Neurocomputing, 70(7-9): 1390-1402. (IF: 1.301)
  45. Cutsuridis V*, Perantonis S. (2006). A Neural Model of Parkinson's Disease Bradykinesia. Neural Networks, 19(4): 354-374. (IF: 2.927)

Books
  1. Cutsuridis V. (2019). Multiscale models of brain disorders, Springer Nature, USA
  2. Cutsuridis V, Graham BP, Cobb S, Vida I. (2018). Hippocampal Microcircuits: A Computational Modeler's Resource Book, 2nd edition, Springer, USA
  3. Cutsuridis V, Hussain A, Taylor JG. (2011). Perception-action cycle: Models, architectures and hardware, Springer, USA
  4. Cutsuridis V, Graham BP, Cobb S, Vida I. (2010). Hippocampal Microcircuits: A Computational Modeler's Resource Book, 1st edition, Springer, USA
  5. Hussain A, Aleksander I, Smith L, Chrisley R, Barros AK, Cutsuridis V. (2008). Brain Inspired Cognitive Systems, Springer, USA
Chapters
  1. Cutsuridis V. (2022). Antisaccade Models. In: Encyclopedia of Computational Neuroscience. Springer, USA
  2. Cutsuridis V. (2022). Countermanding models. In: Encyclopedia of Computational Neuroscience. Springer, USA
  3. Cutsuridis V. (2019). Basal ganglio-thalamo-cortico-spino-muscular model of Parkinson’s disease bradykinesia. In: Multiscale models of brain disorders, Springer-Nature, USA
  4. Cutsuridis V. (2019). Modelling cognitive processing of healthy controls and obsessive compulsive disorder subjects in the antisaccade task. In: Multiscale models of brain disorders, Springer-Nature, USA
  5. Cutsuridis V*. (2019). Simplified compartmental models of CA1 pyramidal cells of theta-modulated inhibition effects on spike timing-dependent plasticity. In: Hippocampal Microcircuits: A computational modeller’s resource book, 2nd edition, Springer, USA
  6. Cutsuridis V*. (2019). Models of Rate and Phase Coding of Place Cells in Hippocampal Microcircuits. In: Hippocampal Microcircuits: A computational modeller’s resource book, 2nd edition, Springer, USA
  7. Cutsuridis V. (2018). Bradykinesia Models. In: Encyclopedia of Computational Neuroscience. Springer, USA
  8. Cutsuridis V, Moustafa AA. (2017). Neurocomputational models of Alzheimer’s disease. Scholarpedia, 12(1):32144
  9. Cutsuridis V*, Moustafa A. (2016). Computational model of pharmacological and immunological treatments of Alzheimer’s disease. In: Computational models of brain and behavior. Wiley, U.K.
  10. Cutsuridis V*. (2016). Computational Microcircuit Models of Associative Memory In Healthy and Diseased Hippocampus. In: Computational models of brain and behavior. Wiley, U.K.
  11. Cutsuridis V*. (2016). Foreword. In From Human Attention to Computational Attention. Springer, USA
  12. Cutsuridis V*. (2013). Bradykinesia Models of Parkinson’s Disease. Scholarpedia, 8(9):30937
  13. Cutsuridis V*. (2013). Bradykinesia Models. In: Encyclopedia of Computational Neuroscience. Springer, USA
  14. Cutsuridis V*. (2010). Neural network modeling of voluntary single joint movement organization. I. Normal conditions. In: Computational neuroscience, Springer-Verlag, 181-192
  15. Cutsuridis V*. (2010). Neural network modeling of voluntary single joint movement organization. II. Parkinson’s disease. In: Computational neuroscience, Springer-Verlag, 193-212
  16. Graham BP, Cutsuridis V, Hunter R. (2010). Associative Memory Models of Hippocampal Areas CA1 and CA3. In: Hippocampal Microcircuits: A Computational Modeller’s Resource Book. Springer, USA, 459-494
  17. Graham BP, Cutsuridis V. (2009). Dynamical Information Processing in the CA1 Microcircuit of the Hippocampus. In: Computational Modeling in behavioral neuroscience: Closing the gap between neurophysiology and behavior. London: Psychology Press, Taylor and Francis Group
  18. Cutsuridis V*. (2008). Voluntary single joint movement organization (in greek). In: Βιοπληροφορική: Δυνατότητες και Προοπτικές. Ίδρυμα Ιατροβιολογικών Ερευνών της Ακαδημίας Αθηνών.
Conference Papers
  1. Tanner T, Cutsuridis V. (2024). Generative Adversarial Network for image reconstruction from brain activity. In: 18th International Joint Conference on Biomedical Engineering Systems and Technologies – Biosignals, in print
  2. Reddy NKS, Cutsuridis V*. (2023). Deep convolutional neural networks with transfer learning for bone fracture recognition with small exemplar image datasets. In 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), Satellite workshop on “AI Enabled Medical Image Analysis”, Rhodes, Greece, June 10, 2023
  3. Golfidis A, Vinos M, Vassilopoulos N, Papadaki E, Skaliora, I, Cutsuridis V*. (2023). Machine Learning Algorithms for Mouse LFP Data Classification in Epilepsy. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-631-6, ISSN 2184-4305, pages 36-47
  4. Reed T, Cutsuridis V*. (2020). Demonstration of a Literature Based Discovery System based on Ontologies, Semantic Filters and Word Embeddings for the Raynaud Disease-Fish Oil Rediscovery. In: ICON 2020 - 17th International Conference on Natural Language Processing, Patna, India, Dec 18-20, 2020
  5. Lei F, Peng Z, Cutsuridis V, Liu M, Zhang Y, Yue S. (2020). Competition between ON and OFF Neural Pathways Enhancing Collision Selectivity. In: IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020, 19 – 24th July, Glasgow (UK)
  6. Andreakos N, Yue S, Cutsuridis V*. (2020). Recall performance improvement in a bio-inspired model of the mammalian hippocampus. In: 13th International Conference in Brain Informatics Proceedings, Mufti M et al. (eds), BI 2020, LNAI 12241, pp. 1–10, 2020, Springer Nature Switzerland AG 2020
  7. Andreakos N, Yue S, Cutsuridis V*. (2020). Improving recall in an associative neural network model of the hippocampus. In: 9th International Conference on Biomimetic and Biohybrid Systems ("Living Machines 2020") Proceedings, Vouloussi V et al. (eds), Springer-Nature, USA
  8. Cutsuridis V*. (2017). A Neural Accumulator Model of Ant saccade Performance of Healthy Controls and Obsessive-Compulsive Disorder Patients. In: M. K. vanVugt, A. P. Banks, & W. G. Kennedy (Eds.), Proceedings of the 15th International Conference on Cognitive Modeling (pp. 85-90). Coventry, United Kingdom: University of Warwick.
  9. Cutsuridis V*, Efstathiou G, Kokkinidis M. (2015). Protein Function Prediction by an ARTMAP Neural Network. Proc MLCB/MLSB worskshop, NIPS 2015, Montreal, Canada, Dec 12-13, 2015
  10. Cutsuridis V*. (2013). Cognitive Models of the Perception-Action Cycle: A View from the Brain. Proc. IJCNN 2013 IEEE, August 4-7, Dallas, TX, USA
  11. CutsuridisV*, Grahan B.P., Cobb S., Hasselmo M.E. (2011). Bio-inspired models of memory capacity, recall performance and theta phase precession. Proc. IJCNN, 2011 IEEE, pp. 3141-48
  12. Cutsuridis V*. (2010). Neural accumulator models of decision making in eye movements. Adv Exp Med Biol, 657: 61-72
  13. Cutsuridis V*, Hasselmo M. (2010). Dynamics and function of a CA1 model of the hippocampus during theta and ripples. Lecture Notes in Computer Science (LNCS) 6352, Springer-Verlag Berlin Heidelberg, pp. 230-240, 2010
  14. Cutsuridis V*. (2010). Action Potential Bursts Modulate the NMDA-R Mediated Spike Timing Dependent Plasticity in a Biophysical Model. Lecture Notes in Computer Science (LNCS) 6352, Springer-Verlag Berlin Heidelberg, pp. 107–116, 2010.
  15. Cutsuridis V*, Cobb S, Graham BP. (2009). How bursts shape the STDP curve in the presence/absence of GABA inhibition. Lecture Notes in Computer Science (LNCS) 5768, Springer-Verlag, 229–238
  16. Cutsuridis V*. (2008). A Bio-Inspired System Architecture of an Active Visual Search Model. Lecture Notes in Computer Science (LNCS) 5164, (Springer-Verlag Berlin Heidelberg 2008), 248-257
  17. Cutsuridis V*, Cobb S, Graham BP. (2008). Encoding and Retrieval in a CA1 Microcircuit Model of the Hippocampus. Lecture Notes in Computer Science (LNCS) 5164, (Springer-Verlag Berlin Heidelberg 2008), 238–247
  18. Kahramanoglou I, Perantonis S, Smyrnis N, Evdokimidis I, Cutsuridis V*. (2008). Modeling the Effects of Dopamine on the Antisaccade Reaction Times (aSRT) of Schizophrenia Patients Lecture Notes in Computer Science (LNCS) 5164, (Springer-Verlag Berlin Heidelberg 2008), 290-299
  19. Cutsuridis V*, Cobb S, Graham BP. (2008). A Ca2+ Dynamics Model of the STDP Symmetry-to-Asymmetry Transition in the CA1 Pyramidal Cell of the Hippocampus. Lecture Notes in Computer Science (LNCS) 5164, (Springer-Verlag Berlin Heidelberg 2008), 627-635
  20. Cutsuridis V*. (2006). Neural Model of Dopaminergic Control of Arm Movements in Parkinson’s Disease Bradykinesia. Lecture Notes in Computer Science (LNCS) 4131, Springer-Verlag, 583-591.
  21. Cutsuridis V*, Kahramanoglou I, Perantonis S, Evdokimidis I, Smyrnis N. (2005). A Biophysical Neural Model of Decision Making in an Antisaccade Task Through Variable Climbing Activity. Lecture Notes in Computer Science (LNCS) 3696, Springer-Verlag, 205-210
  22. Cutsuridis V*. (2003). Computational neural modelling in cognitive neuroscience: advantages and problems. In: Proceedings of the 6th Hellenic-European Conference on Computer Mathematics and its Applications (HERCMA 2003), September 25-27, Athens, Greece
Presentations and posters
  1. Andreakos N, Yue S, Cutsuridis V. Memory retrieval enhancement in a CA1 microcircuit model of the hippocampus. Generative Episodic Memory – GEM 2023, University of Bochum, Germany, June 12-14, 2023
  2. Andreakos N, Yue S, Cutsuridis V. Improving recall in hippocampal neural network models. 16th International Symposium of Cognition, Logic and Language, University of Latvia, Aug 2022
  3. Andreakos N, Yue S, Cutsuridis V. Modelling the Effects of the Perforant Path in the Recall Performance of a CA1 Microcircuit with Excitatory and Inhibitory Neurons. CNS 2021.
  4. Andreakos N, Yue S, Cutsuridis V. Recall performance of a bioinspired model of the mammalian hippocampus. International Conference of Mathematical Neuroscience (online), July 6-7, 2020
  5. Cutsuridis V, Dunn M, Brawn J, Rosser A, Erichsen J. Separate neural systems for antisaccade direction errors in horizontal, but not vertical antisaccades in early Huntington’s disease. British Oculo-Motor Group (BOMG), Cardiff, U.K., Dec 17, 2019
  6. Cutsuridis V, Dunn M, Brawn J, Rosser A, Erichsen J. Neural Modelling of Antisaccade Performance of Healthy Controls and Huntington Disease Patients. Applied Vision Association meeting, Cardiff, U.K., Dec 16, 2019
  7. Cutsuridis V, Dunn M, Brawn J, Rosser A, Erichsen J. Neural Modelling of Antisaccade Performance of Healthy Controls and Huntington Disease Patients. European Conference on Eye Movements, Alicante, Spain, Aug 18 - 22, 2019
  8. Ellison P, Cutsuridis V. Machine learning algorithms for protein function prediction from primary structure. Proc 17th East Midlands Proteomics worskshop, Lincoln, U.K., Oct 24, 2018
  9. Cutsuridis V, Kokkinidis M. Protein Function Prediction by an ARTMAP Neural Network. Proc 17th East Midlands Proteomics worskshop, Lincoln, U.K., Oct 24, 2018
  10. Cutsuridis V, Ettinger U, Kumari V. Neural modelling of antisaccade performance across aging and dysfunction. 2018 Meeting of the European Mathematical Psychology Group, Gevona, Italy, July 30 - August 2, 2018
  11. Cutsuridis V. Two Separate Systems Account for Uncorrected and Corrected Directional Errors of Healthy Controls, Schizophrenia and OCD Patients in the Antisaccade Task. 27th British Oculo-Motor Group (BOMG) Meeting, Cardiff, U.K., January 17, 2018
  12. Cutsuridis V. Neural Modelling of Antisaccade Performance of Healthy Controls, Schizophrenia and Obsessive-Compulsive Disorders Patients. Grenoble Workshop on Models and Analysis of Eye Movements, Grenoble, France, June 6-8, 2018
  13. Cutsuridis V. Neural Modelling of Antisaccade Performance of Healthy Controls, Schizophrenia and Obsessive-Compulsive Disorders Patients. 8th International International Symposium on Biology of Decision Making, Paris, France, May 21-23, 2018
  14. Cutsuridis V. Neural field theory in psychiatric disorders. 4th International Conference in Neural Fields, University of Reading, U.K., July 3-5, 2017
  15. Cutsuridis V. Neurocomputational modeling of decision making in schizophrenia and obsessive-compulsive disorders. Computational Neurology 2017, University of Newcastle, U.K., February 20-21, 2017
  16. Psarrou M, Stefanou S, …, Cutsuridis V, Poirazi P. A simulation study on the effects of dendritic morphology on layer V PFC pyramidal cell firing behaviour. Dendrites Worskop 2014, July 1-4, Heraklion, Crete, Greece.
  17. Saudargiene A, Cutsuridis V. Acetylcholine influence on spike timing dependent plasticity in a hippocampal CA1 pyramidal neuron microcircuit: A computational modelling study. FENS, Milan, July 5-9, 2014
  18. Dainauskas JJ, CutsuridisV, Saudargiene A. Modulatory effects of acetylcholine on spike timing dependent plasticity in hippocampal CA1 pyramidal neuron. LNA Mokslinė Konferencija, Lithuania, 2012
  19. Cutsuridis V, Kumari V, Ettinger U. A Neural Network Model of Antisaccade Performance in Health and in Schizophrenia. Conference in Decision Making, Bristol, Dec 17-18, 2012
  20. Jochems A, Knauer B, Saravanan V, Cutsuridis V, Yoshida M. Intrinsic persistent firing in hippocampal CA1 and CA3 pyramidal cells. SfN, New Orleans, Oct 15-19, 2012
  21. Saravanan V, Cui A, Gootjes-Dreesbach L, Cutsuridis V, Yoshida M. A possible role of the CAN current in switching between real time and time-compressed sequential activity of hippocampal pyramidal cells. FENS, Barcelona, Jul 12-13, 2012
  22. Cutsuridis V. GABAergic contributions to theta phase precession in region CA1 of the hippocampus. Brain Circuits Symposium, Karolinska Institutet, Stockholm, Sweden, Oct 27-28, 2011
  23. Cutsuridis V. Origins of a repetitive and co-contractive pattern of muscle activation in Parkinson’s Disease. Workshop in Computational Neuroscience and Dynamics of Disease States, Leiden, The Netherlands, Aug. 8-12, 2011
  24. Cutsuridis V, Hasselmo M. A Computational Microcircuit Model of Encoding and Retrieval of Spatial Memory Sequences in the CA1 Area of the Hippocampus during Theta and Ripples. Society of Neuroscience, San Diego, USA, Nov 13-17, 2010
  25. Cutsuridis V, Hasselmo M. Network Dynamics of Encoding and Retrieval of Behavioural Spike Sequences During Theta and Ripples in a CA1 Model of the Hippocampus. 19th Annual Computational Neuroscience Meeting CNS*2010, San Antonio, USA, July 24-30, BMC Neuroscience Vol 11, Suppl 1, 2010
  26. Cutsuridis V, Hasselmo M. Encoding and Retrieval of Spatial Memory Sequences During Theta and Ripples in a CA1 Model of the Hippocampus. 14th International Conference on Cognitive and Neural Systems, Boston, USA, May 29-June 1, 2010
  27. Cutsuridis V, Cobb S, Graham BP. Dynamical information processing in the CA1 microcircuit. 4th Computational Cognitive Neuroscience Conference (CCNC), Boston, USA, Nov 18-19, 2009
  28. Cutsuridis V, Graham BP, Cobb S. Modelling the effects of GABA-A inhibition on the spike timing dependent plasticity (STDP) of a CA1 pyramidal cell. Eighteenth Annual Computational Neuroscience Meeting CNS*2009, Berlin, Germany, July 17th – 23rd, BMC Neuroscience Vol 10, Suppl 1, 2009
  29. Cutsuridis V, Cobb S, Graham BP. A CA1 Heteroassociative Microcircuit Model of the Hippocampus. AREADNE: Research in Encoding and Decoding of Neural Ensembles, Santorini, Greece, June 26-29, 2008
  30. Cutsuridis V, Hunter R, Cobb S, Graham BP. Storage and Recall in the CA1 Microcircuit of the Hippocampus: A Biophysical Model. Sixteenth Annual Computational Neuroscience Meeting CNS*2007, Toronto, Canada, July 8th - 12th, 2007. BMC Neuroscience 8(Suppl 2):P33.
  31. Hunter R, Cutsuridis V, Cobb S, Graham BP. Improving Associative Memory in a Model Network of Two-Compartment Spiking Neurons. Fourth Annual Scottish Neuroscience Group Meeting, University of Edinburgh, August 31, 2007
  32. Kahramanoglou I, Cutsuridis V, Smyrnis N, Evdokimidis I, Perantonis S. Dopamine Effect on Climbing Activity of a Cortico-Tectal Model: Simulating the Performance of Patients with DSM-IV Schizophrenia in the Antisaccade Task. 2nd Computational Cognitive Neuroscience Conference, Houston, TX, USA, November 16-19, 2006
  33. Cutsuridis V, Kahramanoglou I, Smyrnis N, Evdokimidis I, Perantonis S. Parametric Analysis of Ionic and Synaptic Current Conductances in a Neural Accumulator Model with Variable Climbing Activity. 19th Conference of Hellenic Society for Neuroscience, Patra, Greece, September 30 - October 2, 2005
  34. Kahramanoglou I, Cutsuridis V, Smyrnis N, Evdokimidis I, Perantonis S. Dopamine Modification of Climbing Activity in a Neural Accumulator Model of the Antisaccade Task. 1st Computational Cognitive Neuroscience Conference, New Orleans, USA, November 11-13, 2005
  35. Cutsuridis V. A neural network model of normal and Parkinsonian EMG activity of fast arm movements. 18th Conference of Hellenic Society for Neuroscience, Athens, Greece, October 17-19, 2003
  36. Cutsuridis V, Evdokimidis I, Kahramanoglou I, Perantonis S, Smyrnis N. Neural network model of eye movement behavior in an antisaccade task. 18th Conference of Hellenic Society for Neuroscience, Athens, Greece, October 17-19, 2003
  37. Cutsuridis V, Smyrnis N, Evdokimidis I, Kahramanoglou I, Perantonis S. Neural network modeling of eye movement behavior in the antisaccade task: validation by comparison with data from 2006 normal individuals. Program No. 72.13. 2003 Abstract Viewer/Itinerary Planner. Washington, DC: Society for Neuroscience, 2003
  38. Cutsuridis V, Bullock D. A Neural Circuit Model of the Effects of Cortical Dopamine Depletion on Task-Related Discharge Patterns of Cells in the Primary Motor Cortex. Poster Session II: Sensory-Motor Control and Robotics, p. xv, Sixth International Neural Network Conference, Boston, MA, May 30 - June 1, 2002
  39. Cutsuridis V, Bullock D. A Neural Circuit Model of the Effects of Cortical Dopamine Depletion on Task-Related Discharge Patterns of Cells in the Primary Motor Cortex. Rethymnon, Crete, 17th Conference of Hellenic Society for Neuroscience, Poster 3, p. 39, October 4-6, 2002