According to our database1, Panayiota Poirazi authored at least 25 papers between 1999 and 2019.
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Artificial neural networks in action for an automated cell-type classification of biological neural networks.
An Architecture for the Acceleration of a Hybrid Leaky Integrate and Fire SNN on the Convey HC-2ex FPGA-Based Processor.
Proceedings of the 25th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2017
Proceedings of the Encyclopedia of Computational Neuroscience, 2014
Dendritic Nonlinearities Reduce Network Size Requirements and Mediate ON and OFF States of Persistent Activity in a PFC Microcircuit Model.
PLoS Comput. Biol., 2014
Computational modeling of the effects of amyloid-beta on release probability at hippocampal synapses.
Frontiers Comput. Neurosci., 2013
Towards predicting persistent activity of neurons by statistical and fractal dimension-based features.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013
Proceedings of the 13th IEEE International Conference on BioInformatics and BioEngineering, 2013
Predictive Features of Persistent Activity Emergence in Regular Spiking and Intrinsic Bursting Model Neurons.
PLoS Comput. Biol., 2012
Proceedings of the 12th IEEE International Conference on Bioinformatics & Bioengineering, 2012
Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It's about Time.
Frontiers Comput. Neurosci., 2011
A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities.
BMC Syst. Biol., 2011
PLoS Comput. Biol., 2010
Modeling of Stress-induced Regulatory Cascades Involving Transcription Factor Dimers.
Proceedings of the CISIS 2010, 2010
Proceedings of the 8th IEEE International Conference on Bioinformatics and Bioengineering, 2008
Modulation of excitability in CA1 pyramidal neurons via the interplay of entorhinal cortex and CA3 inputs.
BMC Bioinform., 2006
Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.
IEEE Trans. Neural Networks, 2004
Choice and Value Flexibility Jointly Contribute to the Capacity of a Subsampled Quadratic Classifier.
Neural Comput., 2000
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999