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ORCIDPanayiota Poirazi
Person information
- affiliation: Foundation of Research and Technology-Hellas, Heraklion, Crete, Greece
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2020 – today
- 2024
[j17]Eirini Troullinou
, Grigorios Tsagkatakis, Attila Losonczy, Panayiota Poirazi, Panagiotis Tsakalides:
A Generative Neighborhood-Based Deep Autoencoder for Robust Imbalanced Classification. IEEE Trans. Artif. Intell. 5(1): 80-91 (2024)- [i5]Spyridon Chavlis, Panayiota Poirazi:
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning. CoRR abs/2404.03708 (2024) - 2023
- [c10]Nikos Malakasis, Spyridon Chavlis, Panayiota Poirazi:
Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks. ACSSC 2023: 942-949 - [c9]Eirini Troullinou, Grigorios Tsagkatakis, Attila Losonczy, Panayiota Poirazi, Panagiotis Tsakalides:
A Generative Neighborhood-Based Deep Autoencoder with an Extended Loss Function for Robust Imbalanced Classification. ACSSC 2023: 1015-1019 - [c8]Stefan Iacob, Spyridon Chavlis, Panayiota Poirazi, Joni Dambre:
Delay-Sensitive Local Plasticity in Echo State Networks. IJCNN 2023: 1-8 - [i4]Michalis Pagkalos, Roman Makarov, Panayiota Poirazi:
Leveraging dendritic properties to advance machine learning and neuro-inspired computing. CoRR abs/2306.08007 (2023) - 2021
- [j16]Eirini Troullinou, Grigorios Tsagkatakis, Spyridon Chavlis, Gergely F. Turi, Wen-Ke Li, Attila Losonczy, Panagiotis Tsakalides, Panayiota Poirazi:
Artificial Neural Networks in Action for an Automated Cell-Type Classification of Biological Neural Networks. IEEE Trans. Emerg. Top. Comput. Intell. 5(5): 755-767 (2021) - [i3]Kosmas Pinitas, Spyridon Chavlis, Panayiota Poirazi:
Dendritic Self-Organizing Maps for Continual Learning. CoRR abs/2110.13611 (2021)
2010 – 2019
- 2019
- [i2]Eirini Troullinou, Grigorios Tsagkatakis, Spyridon Chavlis, Gergely F. Turi, Wen-Ke Li, Attila Losonczy, Panagiotis Tsakalides, Panayiota Poirazi:
Artificial neural networks in action for an automated cell-type classification of biological neural networks. CoRR abs/1911.09977 (2019) - 2017
- [c7]Emmanouil Kousanakis, Apostolos Dollas, Euripides Sotiriades, Ioannis Papaefstathiou, Dionisios N. Pnevmatikatos, Athanasia Papoutsi, Panagiotis C. Petrantonakis, Panayiota Poirazi, Spyridon Chavlis, George Kastellakis:
An Architecture for the Acceleration of a Hybrid Leaky Integrate and Fire SNN on the Convey HC-2ex FPGA-Based Processor. FCCM 2017: 56-63 - 2014
- [j15]Nestoras Karathanasis, Ioannis Tsamardinos, Panayiota Poirazi:
Don't use a cannon to kill the ... miRNA mosquito. Bioinform. 30(7): 1047-1048 (2014) - [j14]Athanasia Papoutsi, Kyriaki Sidiropoulou, Panayiota Poirazi:
Dendritic Nonlinearities Reduce Network Size Requirements and Mediate ON and OFF States of Persistent Activity in a PFC Microcircuit Model. PLoS Comput. Biol. 10(7) (2014) - [r1]Panayiota Poirazi:
Dendritic Computation. Encyclopedia of Computational Neuroscience 2014 - 2013
- [j13]Armando Romani, Cristina Marchetti, Daniela Bianchi, Xavier Leinekugel, Panayiota Poirazi, Michele Migliore, Hélène Marie:
Computational modeling of the effects of amyloid-beta on release probability at hippocampal synapses. Frontiers Comput. Neurosci. 7: 1 (2013) - [c6]Nestoras Karathanasis, Ioannis Tsamardinos, Panayiota Poirazi:
A bioinformatics approach for investigating the determinants of Drosha processing. BIBE 2013: 1-4 - [c5]Panagiotis C. Petrantonakis, Athanasia Papoutsi, Panayiota Poirazi:
Towards predicting persistent activity of neurons by statistical and fractal dimension-based features. IJCNN 2013: 1-6 - 2012
- [j12]Kyriaki Sidiropoulou, Panayiota Poirazi:
Predictive Features of Persistent Activity Emergence in Regular Spiking and Intrinsic Bursting Model Neurons. PLoS Comput. Biol. 8(4) (2012) - [c4]Nestoras Karathanasis, Ioannis Tsamardinos, Angelos P. Armen, Panayiota Poirazi:
SVM-based miRNA: MiRNA∗ duplex prediction. BIBE 2012: 181-186 - 2011
- [j11]Eleftheria Tzamali, Panayiota Poirazi, Ioannis G. Tollis, Martin Reczko:
A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities. BMC Syst. Biol. 5: 167 (2011) - [j10]José Francisco Gómez González, Bartlett W. Mel, Panayiota Poirazi:
Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It's about Time. Frontiers Comput. Neurosci. 5: 44 (2011) - 2010
- [j9]Eleftheria Kyriaki Pissadaki, Kyriaki Sidiropoulou, Martin Reczko, Panayiota Poirazi:
Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model. PLoS Comput. Biol. 6(12) (2010) - [c3]Maria Manioudaki, Panayiota Poirazi:
Modeling of Stress-induced Regulatory Cascades Involving Transcription Factor Dimers. CISIS 2010: 667-671
2000 – 2009
- 2009
- [j8]Anastasis Oulas, Martin Reczko, Panayiota Poirazi:
MicroRNAs and Cancer - The Search Begins! IEEE Trans. Inf. Technol. Biomed. 13(1): 67-77 (2009) - 2008
- [c2]Katerina Gkirtzou, Panagiotis Tsakalides, Panayiota Poirazi:
Mature miRNA identification via the use of a Naive Bayes classifier. BIBE 2008: 1-5 - 2007
- [j7]Kyriaki Sidiropoulou, Marian Joëls, Panayiota Poirazi:
Modeling stress-induced adaptations in Ca2+ dynamics. Neurocomputing 70(10-12): 1640-1644 (2007) - [j6]Eleftheria Kyriaki Pissadaki, Panayiota Poirazi:
Modulation of excitability in CA1 pyramidal neurons via the interplay of entorhinal cortex and CA3 inputs. Neurocomputing 70(10-12): 1735-1740 (2007) - [i1]Martin Reczko, Panayiota Poirazi, Anastasis Oulas, Eleftheria Tzamali, Maria Manioudaki, Vassilis Tsiaras, Ioannis G. Tollis:
The Impact of Systems Biology on the Digital Patient. ERCIM News 2007(69) (2007) - 2006
- [j5]Pavlos Pavlidis, Panayiota Poirazi:
Individualized markers optimize class prediction of microarray data. BMC Bioinform. 7: 345 (2006) - 2005
- [j4]Maria E. Markaki, Stelios C. Orphanoudakis, Panayiota Poirazi:
Modelling reduced excitability in aged CA1 neurons as a calcium-dependent process. Neurocomputing 65-66: 305-314 (2005) - 2004
- [j3]Panayiota Poirazi, Costas Neocleous, Constantinos S. Pattichis, Christos N. Schizas:
Classification capacity of a modular neural network implementing neurally inspired architecture and training rules. IEEE Trans. Neural Networks 15(3): 597-612 (2004) - 2000
- [j2]Panayiota Poirazi, Bartlett W. Mel:
Choice and Value Flexibility Jointly Contribute to the Capacity of a Subsampled Quadratic Classifier. Neural Comput. 12(5): 1189-1205 (2000)
1990 – 1999
- 1999
- [j1]Panayiota Poirazi, Bartlett W. Mel:
Towards the memory capacity of neurons with active dendrites. Neurocomputing 26-27: 237-245 (1999) - [c1]Panayiota Poirazi, Bartlett W. Mel:
Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration. NIPS 1999: 157-163
Coauthor Index
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