@article{bergmann2025energy,

title = {How energy determines spatial localisation and copy number of molecules in neurons},

author = {Cornelius Bergmann and Kanaan Mousaei and Silvio O Rizzoli and Tatjana Tchumatchenko},

year  = {2025},

date = {2025-01-01},

journal = {Nature Communications},

volume = {16},

number = {1},

pages = {1424},

publisher = {Nature Publishing Group UK London},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bergmann2025_energy,

title = {How energy determines spatial localisation and copy number of molecules in neurons},

author = {Cornelius Bergmann and Kanaan Mousaei and Silvio O. Rizzoli and Tatjana Tchumatchenko},

doi = {10.1038/s41467-025-56640-0},

year  = {2025},

date = {2025-01-01},

journal = {Nature Communications},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/bioinformatics/btaf339,

title = {SpyDen: simplifying molecular and structural analysis across spines and dendrites},

author = {Maximilian F Eggl and Surbhit Wagle and Jean P Filling and Thomas E Chater and Yukiko Goda and Tatjana Tchumatchenko},

url = {https://doi.org/10.1093/bioinformatics/btaf339},

doi = {10.1093/bioinformatics/btaf339},

issn = {1367-4811},

year  = {2025},

date = {2025-01-01},

journal = {Bioinformatics},

volume = {41},

number = {7},

pages = {btaf339},

abstract = {Investigating the molecular composition of different neural compartments such as axons, dendrites, or synapses is critical for understanding learning and memory. State-of-the-art microscopy techniques now resolve individual molecules and pinpoint their position with a micrometer or nanometre resolution across hundreds of micrometres, allowing the labelling of multiple structures of interest simultaneously. Algorithmically, tracking individual molecules across hundreds of micrometres and determining whether they are inside a particular cellular compartment can be challenging. Historically, microscopy images are annotated manually, often using multiple software packages to detect fluorescence puncta and quantify cellular compartments of interest. Advanced ANN-based automated tools, while powerful, often can only help with selected parts of the data analysis, may be optimized for specific spatial resolutions, cell preparations, and may not be fully open source and open access to be sufficiently customizable.Thus, we developed SpyDen, a Python package based upon three principles: (i) ease of use for multi-task scenarios, (ii) open-source accessibility and data export to a standard, open data format, (iii) the ability to edit any software-generated annotation and generalize across spatial resolutions. SpyDen operates on 2D microscopy time-series data, offering robust temporal tracking and spatial analysis capabilities. Equipped with a graphical user interface and accompanied by video tutorials, SpyDen provides a collection of powerful algorithms that can be used for neurite and synapse detection, fluorescent puncta, and intensity analysis. We validated SpyDen using expert annotation across numerous use cases to prove a powerful, integrated platform for efficient and reproducible molecular imaging analysis.SpyDen is available on https://github.com/meggl23/SpyDen while the compiled executables can be found at https://gin.g-node.org/CompNeuroNetworks/SpyDenTrainedNetwork.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chater2024_competitive,

title = {Competitive processes shape multi-synapse plasticity along dendritic segments},

author = {Thomas E. Chater and Maximilian F. Eggl and Yukiko Goda and Tatjana Tchumatchenko},

doi = {10.1038/s41467-024-51919-0},

year  = {2024},

date = {2024-01-01},

journal = {Nature Communications},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Squadrani2024_astrocytes,

title = {Astrocytes enhance plasticity response during reversal learning},

author = {Lorenzo Squadrani and Carlos Wert-Carvajal and Daniel Mueller-Komorowska and Kirsten Bohmbach and Christian Henneberger and Pietro Verzelli and Tatjana Tchumatchenko},

doi = {10.1038/s42003-024-06540-8},

year  = {2024},

date = {2024-01-01},

journal = {Communications Biology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nikbakht2024_aversive,

title = {Efficient encoding of aversive location by CA3 long-range projections},

author = {N Nikbakht and M Pofahl and A Miguel-Lopez and F Kamali and Tatjana Tchumatchenko and H Beck},

doi = {10.1016/j.celrep.2024.113957},

year  = {2024},

date = {2024-01-01},

journal = {Cell Reports},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Verzelli2024_editorial,

title = {Editorial overview: Computational neuroscience as a bridge between artificial intelligence, modeling and data},

author = {Pietro Verzelli and Tatjana Tchumatchenko and J Hellgren Kotaleski},

year  = {2024},

date = {2024-01-01},

journal = {Current Opinion in Neurobiology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Eggl2023_linking,

title = {Linking spontaneous and stimulated spine dynamics},

author = {Maximilian F. Eggl and Janko Petkovic and Thomas E. Chater and Yukiko Goda and Tatjana Tchumatchenko},

doi = {10.1038/s42003-023-05303-1},

year  = {2023},

date = {2023-01-01},

journal = {Communications Biology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wagle2023_computational,

title = {Computational insights into mRNA and protein dynamics underlying synaptic plasticity rules},

author = {Surbhit Wagle and Nataliya Kraynyukova and Anne-Sophie Hafner and Tatjana Tchumatchenko},

doi = {10.1016/j.mcn.2023.103846},

year  = {2023},

date = {2023-01-01},

journal = {Molecular and Cellular Neuroscience},

volume = {125},

pages = {103846},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kraynyukova2022_V1,

title = {In vivo extracellular recordings of thalamic and cortical visual responses reveal V1 connectivity rules},

author = {N Kraynyukova and S Renner and G Born and Y Bauer and M. A. Spacek and G Tushev and L Busse and Tatjana Tchumatchenko},

year  = {2022},

date = {2022-01-01},

journal = {Proceedings of the National Academy of Sciences (PNAS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BernaezTimon2022_population,

title = {Synaptic plasticity controls the emergence of population-wide invariant representations in balanced network models},

author = {L Bernaez Timon and P Ekelmans and N Kraynyukova and S Konrad and A Nold and Tatjana Tchumatchenko},

year  = {2022},

date = {2022-01-01},

journal = {Physical Review Research},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{WertCarvajal2022_dopamine,

title = {Dopamine and serotonin interplay for valence-based spatial learning},

author = {C Wert-Carvajal and Melissa Reneaux and Tatjana Tchumatchenko and Claudia Clopath},

year  = {2022},

date = {2022-01-01},

journal = {Cell Reports},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tchumatchenko2020_statistical,

title = {Statistical Laws of Protein Motion in Neuronal Dendritic Trees},

author = {F Sartori and Anne-Sophie Hafner and A Karimi and A Nold and Y Fonkeu and E. M. Schuman and Tatjana Tchumatchenko},

year  = {2020},

date = {2020-01-01},

journal = {Cell Reports},

volume = {33},

number = {7},

pages = {108391},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fonkeu2019_mRNA,

title = {How mRNA Localization and Protein Synthesis Sites Influence Dendritic Protein Distribution and Dynamics},

author = {Yombe Fonkeu and Nataliya Kraynyukova and Anne-Sophie Hafner and F Sartori and E. M. Schuman and Tatjana Tchumatchenko},

year  = {2019},

date = {2019-01-01},

journal = {Neuron},

volume = {103},

number = {5},

pages = {1109–1122},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Herfurth2019_info,

title = {Information transmission of mean and variance coding in integrate-and-fire neurons},

author = {T Herfurth and Tatjana Tchumatchenko},

year  = {2019},

date = {2019-01-01},

journal = {Physical Review E},

volume = {99},

pages = {032420},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kraynyukova2018_ssn,

title = {Stabilized supralinear network can give rise to bistable, oscillatory, and persistent activity},

author = {N Kraynyukova and Tatjana Tchumatchenko},

year  = {2018},

date = {2018-01-01},

journal = {Proceedings of the National Academy of Sciences (PNAS)},

volume = {115},

number = {14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Herfurth2017_linear,

title = {How linear response shaped models of neural circuits and the quest for alternatives},

author = {T Herfurth and Tatjana Tchumatchenko},

year  = {2017},

date = {2017-01-01},

journal = {Current Opinion in Neurobiology},

volume = {46},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dettner2016_spike,

title = {Temporal pairwise spike correlations fully capture single-neuron information},

author = {A Dettner and S Münzberg and Tatjana Tchumatchenko},

year  = {2016},

date = {2016-01-01},

journal = {Nature Communications},

volume = {7},

pages = {13805},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tchumatchenko2014_oscillations,

title = {Oscillations emerging from noise-driven steady state in networks with electrical synapses and subthreshold resonance},

author = {Tatjana Tchumatchenko and Claudia Clopath},

year  = {2014},

date = {2014-01-01},

journal = {Nature Communications},

volume = {5},

pages = {5512},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tchumatchenko2013_energy,

title = {Energy-efficient encoding by shifting spikes in neocortical neurons},

author = {A Malyshev and Tatjana Tchumatchenko and S Volgushev and M Volgushev},

year  = {2013},

date = {2013-01-01},

journal = {European Journal of Neuroscience},

volume = {38},

number = {8},

pages = {1456–1464},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tchumatchenko2011_ultrafast,

title = {Ultrafast population encoding by cortical neurons},

author = {Tatjana Tchumatchenko and A Malyshev and F Wolf and M Volgushev},

year  = {2011},

date = {2011-01-01},

journal = {Journal of Neuroscience},

volume = {31},

number = {34},

pages = {12171–12179},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tchumatchenko2011_representation,

title = {Representation of dynamical stimuli in populations of threshold neurons},

author = {Tatjana Tchumatchenko and F Wolf and T Geisel and M Volgushev},

year  = {2011},

date = {2011-01-01},

journal = {PLoS Computational Biology},

volume = {7},

number = {2},

pages = {e1001059},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tchumatchenko2010_correlations,

title = {Correlations and synchrony in threshold neuron models},

author = {Tatjana Tchumatchenko and T Geisel and M Volgushev and F Wolf},

year  = {2010},

date = {2010-01-01},

journal = {Physical Review Letters},

volume = {104},

number = {5},

pages = {058102},

keywords = {},

pubstate = {published},

tppubtype = {article}

}