| Training deep neural density estimators to identify mechanistic models of neural dynamics PJ Gonēalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... Elife 9, e56261, 2020 | 137 | 2020 |
| Modelling and analysis of electrical potentials recorded in microelectrode arrays (MEAs) TV Ness, C Chintaluri, J Potworowski, S Łęski, H Głąbska, DK Wójcik, ... Neuroinformatics 13, 403-426, 2015 | 81 | 2015 |
| Corrected four-sphere head model for EEG signals S Nęss, C Chintaluri, TV Ness, AM Dale, GT Einevoll, DK Wójcik Frontiers in human neuroscience 11, 490, 2017 | 27 | 2017 |
| 25th annual computational neuroscience meeting: CNS-2016 TO Sharpee, A Destexhe, M Kawato, V Sekulić, FK Skinner, DK Wójcik, ... BMC neuroscience 17, 1-112, 2016 | 17 | 2016 |
| NSDF: neuroscience simulation data format S Ray, C Chintaluri, US Bhalla, DK Wójcik Neuroinformatics 14, 147-167, 2016 | 16 | 2016 |
| Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv PJ Gonēalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... | 13 | 2019 |
| kCSD-python, a tool for reliable Current Source Density estimation C Chintaluri, M Kowalska, W Średniawa, MB Czerwiński, JM Dzik, ... BioRxiv, 708511, 2019 | 7 | 2019 |
| Collection of simulated data from a thalamocortical network model H Głąbska, C Chintaluri, DK Wójcik Neuroinformatics 15, 87-99, 2017 | 5 | 2017 |
| Modelling and analysis of electrical potentials recorded in microelectrode arrays (MEAs) N Torbjųrn, C Chaitanya, P Jan, L Szymon, G Helena, W Daniel, E Gaute Front. Neurosci. 10, 403-426, 2016 | 3 | 2016 |
| Four-sphere head model for EEG signals revisited S Nęss, C Chintaluri, TV Ness, AM Dale, GT Einevoll, DK Wójcik bioRxiv, 124875, 2017 | 2 | 2017 |
| A novel method for spatial source localization using ECoG and SEEG recordings in human epilepsy patients C Chintaluri, DK Wójcik BMC Neuroscience 16 (Suppl 1), P286, 2015 | 2 | 2015 |
| Collection of simulated data for validation of methods of analysis of extracellular potentials HT Głąbska, C Chintaluri, DK Wójcik Neuroinformatics, 2014 | 2 | 2014 |
| Metabolically driven action potentials serve neuronal energy homeostasis and protect from reactive oxygen species C Chintaluri, TP Vogels bioRxiv, 2022 | 1 | 2022 |
| What we can and what we cannot see with extracellular multielectrodes C Chintaluri, M Bejtka, W Średniawa, M Czerwiński, JM Dzik, ... PLOS Computational Biology 17 (5), e1008615, 2021 | 1 | 2021 |
| Amortised inference for mechanistic models of neural dynamics JM Lueckmann, PJ Gonēalves, C Chintaluri, WF Podlaski, G Bassetto, ... Computational and Systems Neuroscience (Cosyne) 2019, 108, 2019 | 1 | 2019 |
| Neuroscience Simulation Data Format (NSDF): HDF-based format for large simulation datasets C Chintaluri, S Ray, US Bhalla, DK Wójcik Frontiers in Neuroinformatics 26, 2014 | 1 | 2014 |
| Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species C Chintaluri, TP Vogels Proceedings of the National Academy of Sciences of the United States of …, 2023 | | 2023 |
| Kernel current source density revisited C Chintaluri, M Kowalska, MB Czerwinski, W Sredniawa, ... Acta Neurobiologiae Experimentalis 79 (Suppl. 1), 2019 | | 2019 |
| Kernel electrical source imaging (kESI) method for reconstruction of sources of brain electric activity in realistic brain geometries M Kowalska, JM Dzik, C Chintaluri, DK Wojcik Acta Neurobiologiae Experimentalis 79 (Suppl. 1), 2019 | | 2019 |
| A standardised formalism for voltage-gated ion channel models C Chintaluri, WF Podlaski, PJ Goncalves, JH Macke 28th Annual Computational Neuroscience Meeting, Barcelona, Spain, 2019 | | 2019 |
