@book{mitra2007observed,
  title={Observed brain dynamics},
  author={Mitra, Partha},
  year={2007},
  publisher={Oxford university press}
}

@Article{raschNeuronsCircuitsLinear2009,
  author   = {Rasch, M. and Logothetis, N. K. and Kreiman, G.},
  title    = {From Neurons to Circuits: Linear Estimation of Local Field Potentials},
  doi      = {10.1523/JNEUROSCI.2390-09.2009},
  issn     = {1529-2401 (Electronic) 0270-6474 (Linking)},
  pages    = {13785-96},
  volume   = {29},
  abstract = {Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs) (a circuit property) and spiking multiunit activity (MUA). Recently, there has been increased interest in LFPs because of their correlation with functional magnetic resonance imaging blood oxygenation level-dependent measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same electrode or nearby electrodes. We used "signal estimation theory" to show that a linear filter operation on the activity of one or a few neurons can explain a significant fraction of the LFP time course in the macaque monkey primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positive time lags. The filter was similar across different neocortical regions and behavioral conditions, including spontaneous activity and visual stimulation. The estimations had a spatial resolution of approximately 1 mm and a temporal resolution of approximately 200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than the negative time lags. Additionally, we showed that spikes occurring within approximately 10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In summary, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons.},
  journal  = {J Neurosci},
  keywords = {Nerve Net/*physiology,Action Potentials/*physiology,Animals,Neurons/*physiology,Macaca mulatta,Visual Pathways/physiology,Linear Models,Visual Fields/*physiology},
  month    = nov,
  year     = {2009},
}
                  
@Article{rasch2008InferringSpikeTrains,
  author   = {Rasch, M. J. and Gretton, A. and Murayama, Y. and Maass, W. and Logothetis, N. K.},
  title    = {Inferring Spike Trains from Local Field Potentials},
  doi      = {10.1152/jn.00919.2007},
  issn     = {0022-3077 (Print) 0022-3077 (Linking)},
  language = {en},
  number   = {3},
  pages    = {1461-76},
  volume   = {99},
  abstract = {We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation across electrodes, the low-frequency structure in spike trains (in the 100-ms range) can be inferred with reasonable accuracy, whereas exact spike positions are not reliably predicted. Two kinds of features of the LFP are exploited for prediction: the frequency power of bands in the high gamma-range (40-90 Hz) and information contained in low-frequency oscillations ({$<$}10 Hz), where both phase and power modulations are informative. Information analysis revealed that both features code (mainly) independent aspects of the spike-to-LFP relationship, with the low-frequency LFP phase coding for temporally clustered spiking activity. Although both features and prediction quality are similar during seminatural movie stimuli and spontaneous activity, prediction performance during spontaneous activity degrades much more slowly with increasing electrode distance. The general trend of data obtained with anesthetized animals is qualitatively mirrored in that of a more limited data set recorded in V1 of non-anesthetized monkeys. In contrast to the cortical field potentials, thalamic LFPs (e.g., LFPs derived from recordings in the dorsal lateral geniculate nucleus) hold no useful information for predicting spiking activity.},
  journal  = {J Neurophysiol},
  keywords = {Action Potentials/*physiology,Animals,Neurons/*physiology,Electroencephalography/methods,Predictive Value of Tests,Macaca mulatta,Photic Stimulation/methods,Evoked Potentials; Visual/*physiology,Spectrum Analysis,Visual Fields/physiology,Visual Perception/physiology,Reference Values,Visual Cortex/*cytology/*physiology,Wakefulness},
  month    = mar,
  year     = {2008},
}
                  
@article{ay2008information,
  title={Information flows in causal networks},
  author={Ay, Nihat and Polani, Daniel},
  journal={Advances in complex systems},
  volume={11},
  number={01},
  pages={17--41},
  year={2008},
  publisher={World Scientific}
}

@article{vicente2011transfer,
  title={Transfer entropy—a model-free measure of effective connectivity for the neurosciences},
  author={Vicente, Raul and Wibral, Michael and Lindner, Michael and Pipa, Gordon},
  journal={Journal of computational neuroscience},
  volume={30},
  number={1},
  pages={45--67},
  year={2011},
  publisher={Springer}
}

@article{barnett2009granger,
  title={Granger causality and transfer entropy are equivalent for Gaussian variables},
  author={Barnett, Lionel and Barrett, Adam B and Seth, Anil K},
  journal={Physical review letters},
  volume={103},
  number={23},
  pages={238701},
  year={2009},
  publisher={APS}
}

@inproceedings{moneta2011causal,
  title={Causal search in structural vector autoregressive models},
  author={Moneta, Alessio and Chla{\ss}, Nadine and Entner, Doris and Hoyer, Patrik},
  booktitle={NIPS Mini-Symposium on Causality in Time Series},
  pages={95--114},
  year={2011},
  organization={PMLR}
}

@article{shao2023transient,
    title={Information theoretic measures of causal influences during transient neural events},
    author={Shao, Yujia and Logothetis, Nikos K and Besserve, Michel},
    journal={Frontiers in Network Physiology},
    volume={3},
    pages={1085347},
    year={2023},
    publisher={Frontiers}
}

@article{granger1969investigating,
  title={Investigating causal relations by econometric models and cross-spectral methods},
  author={Granger, Clive WJ},
  journal={Econometrica: journal of the Econometric Society},
  pages={424--438},
  year={1969},
  publisher={JSTOR}
}

@article{Schreiber2000,
  author = {Schreiber, Thomas},
  title = {Measuring Information Transfer},
  journal = {Physical Review Letters},
  year = {2000},
  volume = {85},
  number = {2},
  pages = {461--464},
  doi = {10.1103/PhysRevLett.85.461}
}

@article{Seth2015,
  author = {Seth, Anil K. and Barrett, Adam B. and Barnett, Lionel},
  title = {Granger Causality Analysis in Neuroscience and Neuroimaging},
  journal = {Journal of Neuroscience},
  year = {2015},
  volume = {35},
  number = {8},
  pages = {3293--3297},
  doi = {10.1523/JNEUROSCI.4399-14.2015}
}

@article{harris2020array,
  title={Array programming with NumPy},
  author={Harris, Charles R and Millman, K Jarrod and Van Der Walt, St{\'e}fan J and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J and others},
  journal={Nature},
  volume={585},
  number={7825},
  pages={357--362},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@article{virtanen2020fundamental,
  title={Fundamental algorithms for scientific computing in python and SciPy 1.0 contributors. SciPy 1.0},
  author={Virtanen, P and Gommers, R and Oliphant, TE and Haberland, M and Reddy, T and Cournapeau, D and Burovski, E and Peterson, P and Weckesser, W and Bright, J and others},
  journal={Nat. Methods},
  volume={17},
  pages={261--272},
  year={2020}
}

@article{hunter2007matplotlib,
  title={Matplotlib: A 2D graphics environment},
  author={Hunter, John D},
  journal={Computing in science \& engineering},
  volume={9},
  number={03},
  pages={90--95},
  year={2007},
  publisher={IEEE Computer Society}
}

@article{MNEPython2013,
  author = {Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis A. and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and H{\"a}m{\"a}l{\"a}inen, Matti S.},
  title = {{MEG} and {EEG} data analysis with {MNE-Python}},
  journal = {Frontiers in Neuroscience},
  year = {2013},
  volume = {7},
  pages = {267},
  doi = {10.3389/fnins.2013.00267}
}

@article{Brainstorm2011,
  author = {Tadel, Fran{\c{c}}ois and Baillet, Sylvain and Mosher, John C. and Pantazis, Dimitrios and Leahy, Richard M.},
  title = {Brainstorm: {A} User-Friendly Application for {MEG/EEG} Analysis},
  journal = {Computational Intelligence and Neuroscience},
  year = {2011},
  volume = {2011},
  pages = {879716},
  doi = {10.1155/2011/879716}
}

@article{zheng2024causal,
  title={Causal-learn: Causal discovery in python},
  author={Zheng, Yujia and Huang, Biwei and Chen, Wei and Ramsey, Joseph and Gong, Mingming and Cai, Ruichu and Shimizu, Shohei and Spirtes, Peter and Zhang, Kun},
  journal={Journal of Machine Learning Research},
  volume={25},
  number={60},
  pages={1--8},
  year={2024}
}

@article{weichwald2021causality,
  title={Causality in cognitive neuroscience: concepts, challenges, and distributional robustness},
  author={Weichwald, Sebastian and Peters, Jonas},
  journal={Journal of Cognitive Neuroscience},
  volume={33},
  number={2},
  pages={226--247},
  year={2021},
  publisher={MIT Press}
}

@article{runge2022jakobrunge,
  title={jakobrunge/tigramite: Tigramite 5.0},
  author={Runge, J},
  year={2022}
}

@article{janzing2013quantifying,
  title={Quantifying causal influences},
  author={Janzing, Dominik and Balduzzi, David and Grosse-Wentrup, Moritz and Sch{\"o}lkopf, Bernhard},
  year={2013}
}

@article{jooHippocampalSharpWave2018,
  title = {The Hippocampal Sharp Wave--Ripple in Memory Retrieval for Immediate Use and Consolidation},
  author = {Joo, Hannah R. and Frank, Loren M.},
  year = {2018},
  month = dec,
  journal = {Nature Reviews Neuroscience},
  volume = {19},
  number = {12},
  pages = {744--757},
  publisher = {Nature Publishing Group},
  issn = {1471-0048},
  doi = {10.1038/s41583-018-0077-1},
  url = {https://www.nature.com/articles/s41583-018-0077-1},
  urldate = {2022-01-20},
  abstract = {Various cognitive functions have long been known to require the hippocampus. Recently, progress has been made in identifying the hippocampal neural activity patterns that implement these functions. One such pattern is the sharp wave--ripple (SWR), an event associated with highly synchronous neural firing in the hippocampus and modulation of neural activity in distributed brain regions. Hippocampal spiking during SWRs can represent past or potential future experience, and SWR-related interventions can alter subsequent memory performance. These findings and others suggest that SWRs support both memory consolidation and memory retrieval for processes such as decision-making. In addition, studies have identified distinct types of SWR based on representational content, behavioural state and physiological features. These various findings regarding SWRs suggest that different SWR types correspond to different cognitive functions, such as retrieval and consolidation. Here, we introduce another possibility --- that a single SWR may support more than one cognitive function. Taking into account classic psychological theories and recent molecular results that suggest that retrieval and consolidation share mechanisms, we propose that the SWR mediates the retrieval of stored representations that can be utilized immediately by downstream circuits in decision-making, planning, recollection and/or imagination while simultaneously initiating memory consolidation processes.},
  copyright = {2018 The Authors},
  langid = {english},
  keywords = {read},
  annotation = {Bandiera\_abtest: a\\
Cg\_type: Nature Research Journals\\
Primary\_atype: Reviews\\
Subject\_term: Consolidation;Hippocampus;Spatial memory\\
Subject\_term\_id: consolidation;hippocampus;spatial-memory},
  groups = {computation},
  timestamp = {2022-07-01T18:22:17Z},
  file = {joo2018the.pdf:/home/ssafavi/Nextcloud/libraries/zoteroLib/neuroscience/states/events/computation/joo2018the.pdf:application/pdf}
}
% == BibTeX quality report for jooHippocampalSharpWave2018:
% ? unused Journal abbreviation ("Nat Rev Neurosci")
% ? unused Library catalog ("www.nature.com")

@article{womelsdorfBurstFiringSynchronizes2014,
  title = {Burst Firing Synchronizes Prefrontal and Anterior Cingulate Cortex during Attentional Control},
  author = {Womelsdorf, T. and Ardid, S. and Everling, S. and Valiante, T. A.},
  year = {2014},
  month = nov,
  journal = {Curr Biol},
  volume = {24},
  pages = {2613--21},
  issn = {1879-0445 (Electronic) 0960-9822 (Linking)},
  doi = {10.1016/j.cub.2014.09.046},
  abstract = {BACKGROUND: It is widely held that single cells in anterior cingulate and lateral prefrontal cortex (ACC/PFC) coordinate their activity during attentional processes, although cellular activity that may underlie such coordination across ACC/PFC has not been identified. We thus recorded cells in five ACC/PFC subfields of macaques engaged in a selective attention task, characterized those spiking events that indexed attention, and identified how spiking of distinct cell populations synchronized between brain areas. RESULTS: We found that cells in ACC/PFC increased the firing of brief 200 Hz spike bursts when subjects shifted attention and engaged in selective visual processing. In contrast to nonburst spikes, burst spikes synchronized over large distances to local field potentials at narrow beta (12-20 Hz) and at gamma (50-75 Hz) frequencies. Long-range burst synchronization was anatomically specific, functionally connecting those subfields in area 24 (ACC) and area 46 (PFC) that are key players of attentional control. By splitting cells into putative excitatory (pE) and inhibitory (pI) cells by their broad and narrow spikes, we identified that bursts of pI cells preceded and that bursts of pE cells followed in time periods of maximal beta coherent network activity. In contrast, gamma bursts were transient impulses with equal timing across cell classes. CONCLUSIONS: These findings suggest that processes underlying burst firing and burst synchronization are candidate mechanisms to coordinate attention information across brain areas. We speculate that distinct burst-firing motifs realize beta and gamma synchrony to trigger versus maintain functional network states during goal-directed behavior.},
  keywords = {*Action Potentials,*Attention,Animals,Brain Mapping,Gyrus Cinguli/*physiology,Macaca/*physiology,Photic Stimulation,Prefrontal Cortex/physiology},
  groups = {unfiled},
  timestamp = {2018-02-04T12:42:51Z}
}

@article{lundqvistBetaBurstsCognition2024,
  title = {Beta: Bursts of Cognition},
  shorttitle = {Beta},
  author = {Lundqvist, Mikael and Miller, Earl K. and Nordmark, Jonatan and Liljefors, Johan and Herman, Pawel},
  year = {2024},
  month = apr,
  journal = {Trends in Cognitive Sciences},
  issn = {1364-6613},
  doi = {10.1016/j.tics.2024.03.010},
  url = {https://www.sciencedirect.com/science/article/pii/S1364661324000779},
  urldate = {2024-04-23},
  abstract = {Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.},
  keywords = {read},
  groups = {@collection},
  timestamp = {2024-04-23T16:59:29Z}
}

@article{safaviUncoveringOrganizationNeural2023,
  title = {Uncovering the Organization of Neural Circuits with {{Generalized Phase Locking Analysis}}},
  author = {Safavi, Shervin and Panagiotaropoulos, Theofanis I. and Kapoor, Vishal and {Ramirez-Villegas}, Juan F. and Logothetis, Nikos K. and Besserve, Michel},
  year = {2023},
  month = apr,
  journal = {PLOS Computational Biology},
  volume = {19},
  number = {4},
  pages = {e1010983},
  publisher = {Public Library of Science},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1010983},
  url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010983},
  urldate = {2023-05-03},
  abstract = {Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic ``field'' signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.},
  langid = {english},
  groups = {papers},
  timestamp = {2023-05-03T22:51:35Z},
  file = {safavi2023unc.pdf:/home/ssafavi/Nextcloud/libraries/zoteroLib/sspub/papers/safavi2023unc2.pdf:application/pdf}
}

@article{logothetisHippocampalCorticalInteraction2012,
  title = {Hippocampal--Cortical Interaction during Periods of Subcortical Silence},
  author = {Logothetis, N. K. and Eschenko, O. and Murayama, Y. and Augath, M. and Steudel, T. and Evrard, H. C. and Besserve, M. and Oeltermann, A.},
  year = {2012},
  month = nov,
  journal = {Nature},
  volume = {491},
  number = {7425},
  pages = {547--553},
  publisher = {Nature Publishing Group},
  issn = {1476-4687},
  doi = {10.1038/nature11618},
  url = {https://www.nature.com/articles/nature11618},
  urldate = {2020-06-05},
  abstract = {Hippocampal ripples, episodic high-frequency field-potential oscillations primarily occurring during sleep and calmness, have been described in mice, rats, rabbits, monkeys and humans, and so far they have been associated with retention of previously acquired awake experience. Although hippocampal ripples have been studied in detail using neurophysiological methods, the global effects of ripples on the entire brain remain elusive, primarily owing to a lack of methodologies permitting concurrent hippocampal recordings and whole-brain activity mapping. By combining electrophysiological recordings in hippocampus with ripple-triggered functional magnetic resonance imaging, here we show that most of the cerebral cortex is selectively activated during the ripples, whereas most diencephalic, midbrain and brainstem regions are strongly and consistently inhibited. Analysis of regional temporal response patterns indicates that thalamic activity suppression precedes the hippocampal population burst, which itself is temporally bounded by massive activations of association and primary cortical areas. These findings suggest that during off-line memory consolidation, synergistic thalamocortical activity may be orchestrating a privileged interaction state between hippocampus and cortex by silencing the output of subcortical centres involved in sensory processing or potentially mediating procedural learning. Such a mechanism would cause minimal interference, enabling consolidation of hippocampus-dependent memory.},
  copyright = {2012 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
  langid = {english},
  groups = {learning_memory_sleep},
  timestamp = {2020-06-05T10:56:27Z}
}

@article{nitzanBrainwideInteractionsHippocampal2022,
  title = {Brain-Wide Interactions during Hippocampal Sharp Wave Ripples},
  author = {Nitzan, Noam and Swanson, Rachel and Schmitz, Dietmar and Buzs{\'a}ki, Gy{\"o}rgy},
  year = {2022},
  month = may,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {119},
  number = {20},
  pages = {e2200931119},
  publisher = {Proceedings of the National Academy of Sciences},
  doi = {10.1073/pnas.2200931119},
  url = {https://www.pnas.org/doi/10.1073/pnas.2200931119},
  urldate = {2023-11-21},
  abstract = {During periods of disengagement from the environment, transient population bursts, known as sharp wave ripples (SPW-Rs), occur sporadically. While numerous experiments have characterized the bidirectional relationship between SPW-Rs and activity in chosen brain areas, the topographic relationship between different segments of the hippocampus and brain-wide target areas has not been studied at high temporal and spatial resolution. Yet, such knowledge is necessary to infer the direction of communication. We analyzed two publicly available datasets with simultaneous high-density silicon probe recordings from across the mouse forebrain. We found that SPW-Rs coincide with a transient brain-wide increase in functional connectivity. In addition, we show that the diversity in SPW-R features, such as their incidence, magnitude, and intrahippocampal topography in the septotemporal axis, are correlated with slower excitability fluctuations in cortical and subcortical areas. Further, variations in SPW-R features correlated with the timing, sign, and magnitude of downstream responses with large-amplitude SPW-Rs followed by transient silence in extrahippocampal structures. Our findings expand on previous results and demonstrate that the activity patterns in extrahippocampal structures depend both on the intrahippocampal topographic origin and magnitude of hippocampal SPW-Rs.},
  groups = {events},
  timestamp = {2023-11-21T20:07:27Z}
}
% == BibTeX quality report for nitzanBrainwideInteractionsHippocampal2022:
% ? unused Library catalog ("pnas.org (Atypon)")

@article{safaviUnivariateMultivariateCoupling2021,
  title = {From {{Univariate}} to {{Multivariate Coupling}} between {{Continuous Signals}} and {{Point Processes}}: {{A Mathematical Framework}}},
  shorttitle = {From {{Univariate}} to {{Multivariate Coupling}} between {{Continuous Signals}} and {{Point Processes}}},
  author = {Safavi, Shervin and Logothetis, Nikos K. and Besserve, Michel},
  year = {2021},
  month = apr,
  journal = {Neural Computation},
  pages = {1--67},
  issn = {0899-7667},
  doi = {10.1162/neco_a_01389},
  url = {https://doi.org/10.1162/neco_a_01389},
  urldate = {2021-05-07},
  abstract = {Time series data sets often contain heterogeneous signals, composed of both continuously changing quantities and discretely occurring events. The coupling between these measurements may provide insights into key underlying mechanisms of the systems under study. To better extract this information, we investigate the asymptotic statistical properties of coupling measures between continuous signals and point processes. We first introduce martingale stochastic integration theory as a mathematical model for a family of statistical quantities that include the phase locking value, a classical coupling measure to characterize complex dynamics. Based on the martingale central limit theorem, we can then derive the asymptotic gaussian distribution of estimates of such coupling measure that can be exploited for statistical testing. Second, based on multivariate extensions of this result and random matrix theory, we establish a principled way to analyze the low-rank coupling between a large number of point processes and continuous signals. For a null hypothesis of no coupling, we establish sufficient conditions for the empirical distribution of squared singular values of the matrix to converge, as the number of measured signals increases, to the well-known Marchenko-Pastur (MP) law, and the largest squared singular value converges to the upper end of the MPs support. This justifies a simple thresholding approach to assess the significance of multivariate coupling. Finally, we illustrate with simulations the relevance of our univariate and mul-tivariate results in the context of neural time series, addressing how to reliably quantify the interplay between multichannel local field potential signals and the spiking activity of a large population of neurons.},
  copyright = {All rights reserved},
  groups = {methods,papers},
  timestamp = {2021-05-19T13:10:08Z},
  file = {safavi2021fro.pdf:/home/ssafavi/Nextcloud/libraries/zoteroLib/sspub/papers/safavi2021fro.pdf:application/pdf}
}
% == BibTeX quality report for safaviUnivariateMultivariateCoupling2021:
% ? Title looks like it was stored in title-case in Zotero
% ? unused Library catalog ("Silverchair")

@article{liuECannulaRevealsAnatomical2022b,
  title = {E-{{Cannula}} Reveals Anatomical Diversity in Sharp-Wave Ripples as a Driver for the Recruitment of Distinct Hippocampal Assemblies},
  author = {Liu, Xin and Terada, Satoshi and Ramezani, Mehrdad and Kim, Jeong-Hoon and Lu, Yichen and Grosmark, Andres and Losonczy, Attila and Kuzum, Duygu},
  year = {2022},
  month = oct,
  journal = {Cell Reports},
  volume = {41},
  number = {1},
  issn = {2211-1247},
  doi = {10.1016/j.celrep.2022.111453},
  url = {https://www.cell.com/cell-reports/abstract/S2211-1247(22)01294-3},
  urldate = {2023-05-21},
  langid = {english},
  pmid = {36198271},
  keywords = {read},
  groups = {@collection},
  timestamp = {2023-05-21T22:57:15Z}
}
% == BibTeX quality report for liuECannulaRevealsAnatomical2022b:
% ? unused Library catalog ("www.cell.com")

@article{weichwaldDistributionalRobustnessGuiding2020,
  title = {Distributional Robustness as a Guiding Principle for Causality in Cognitive Neuroscience},
  author = {Weichwald, Sebastian and Peters, Jonas},
  year = {2020},
  month = feb,
  journal = {arXiv:2002.06060 [q-bio, stat]},
  eprint = {2002.06060},
  primaryclass = {q-bio, stat},
  url = {http://arxiv.org/abs/2002.06060},
  urldate = {2020-05-09},
  abstract = {While probabilistic models describe the dependence structure between observed variables, causal models go one step further: they predict how cognitive functions are affected by external interventions that perturb neuronal activity. Inferring causal relationships from data is an ambitious task that is particularly challenging in cognitive neuroscience. Here, we discuss two difficulties in more detail: the scarcity of interventional data and the challenge of finding the right variables. We argue for distributional robustness as a guiding principle to tackle these problems. Modelling a target variable using the correct set of causal variables yields a model that generalises across environments or subjects (if these environments leave the causal mechanisms intact). Conversely, if a candidate model does not generalise, then either it includes non-causes of the target variable or the underlying variables are wrongly defined. In this sense, generalisability may serve as a guiding principle when defining relevant variables and can be used to partially compensate for the lack of interventional data.},
  archiveprefix = {arXiv},
  groups = {causality},
  timestamp = {2020-05-09T08:39:47Z}
}


@article{ross2024causation,
  title={Causation in neuroscience: keeping mechanism meaningful},
  author={Ross, Lauren N and Bassett, Dani S},
  journal={Nature Reviews Neuroscience},
  volume={25},
  number={2},
  pages={81--90},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@phdthesis{safaviBrainComplexSystem2022,
  title = {Brain as a {{Complex System}}, Harnessing Systems Neuroscience Tools \& Notions for an Empirical Approach},
  author = {Safavi, Shervin},
  year = {2022},
  month = sep,
  doi = {10.15496/publikation-69434},
  url = {https://publikationen.uni-tuebingen.de/xmlui/handle/10900/128071},
  urldate = {2023-03-23},
  copyright = {http://tobias-lib.uni-tuebingen.de/doku/lic\_mit\_pod.php?la=de},
  langid = {english},
  school = {Universit{\"a}t T{\"u}bingen},
  annotation = {Accepted: 2022-06-17T08:53:54Z},
  groups = {thesis},
  timestamp = {2023-03-23T09:07:45Z}
}
% == BibTeX quality report for safaviBrainComplexSystem2022:
% ? unused Library catalog ("tobias-lib.ub.uni-tuebingen.de")
% ? unused Type ("Dissertation")

@article{einevollModellingAnalysisLocal2013,
  title = {Modelling and Analysis of Local Field Potentials for Studying the Function of Cortical Circuits},
  author = {Einevoll, G. T. and Kayser, C. and Logothetis, N. K. and Panzeri, S.},
  year = {2013},
  month = oct,
  journal = {Nat Rev Neurosci},
  volume = {14},
  number = {11},
  pages = {770--85},
  issn = {1471-0048 (Electronic) 1471-003X (Linking)},
  doi = {10.1038/nrn3599},
  abstract = {The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) - that is, extracellularly recorded potentials with frequencies of up to approximately 500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.},
  langid = {english},
  keywords = {{Computational models, Computational neuroscience, Sensory systems, Electrophysiology, Nature Reviews Neuroscience}},
  groups = {MT,lfp},
  timestamp = {2018-11-17T15:06:14Z},
  file = {einevoll2013mod.pdf:/home/ssafavi/Nextcloud/libraries/zoteroLib/neuroscience/neurophysiology/lfp/einevoll2013mod.pdf:application/pdf}
}
% == BibTeX quality report for einevollModellingAnalysisLocal2013:
% ? unused Journal abbreviation ("Nature reviews. Neuroscience")

@misc{muller2015python,
  title={Python in neuroscience},
  author={Muller, Eilif and Bednar, James A and Diesmann, Markus and Gewaltig, Marc-Oliver and Hines, Michael and Davison, Andrew P},
  journal={Frontiers in neuroinformatics},
  volume={9},
  pages={11},
  year={2015},
  publisher={Frontiers Media SA}
}

@software{nouri_2025_trancit_package,
  author       = {Nouri, Salar and
                  Shao, Kaidi and
                  Safavi, Shervin},
  title        = {TranCIT: Transient Causal Interaction Toolbox},
  month        = aug,
  year         = 2025,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.16998396},
  url          = {https://doi.org/10.5281/zenodo.16998396},
}

@article{nouri2025trancit, 
       title={TranCIT: Transient Causal Interaction Toolbox},  
       author={Nouri, Salar and Shao, Kaidi and Safavi, Shervin}, 
       year={2025}, 
       journal={arXiv preprint arXiv:2509.00602},
       url={https://doi.org/10.48550/arXiv.2509.00602},
 }
