@article{292,
abstract = {Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains.},
author = {Botella Soler, Vicent and Deny, Stephane and Martius, Georg S and Marre, Olivier and Tkacik, Gasper},
journal = {PLoS Computational Biology},
number = {5},
publisher = {Public Library of Science},
title = {{Nonlinear decoding of a complex movie from the mammalian retina}},
doi = {10.1371/journal.pcbi.1006057},
volume = {14},
year = {2018},
}
@misc{5584,
abstract = {This package contains data for the publication "Nonlinear decoding of a complex movie from the mammalian retina" by Deny S. et al, PLOS Comput Biol (2018).
The data consists of
(i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin.
(ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments are exact repeats or unique ball trajectories.
(iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions ("sites") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. },
author = {Deny, Stephane and Marre, Olivier and Botella-Soler, Vicente and Martius, Georg S and Tkacik, Gasper},
keyword = {retina, decoding, regression, neural networks, complex stimulus},
publisher = {IST Austria},
title = {{Nonlinear decoding of a complex movie from the mammalian retina}},
doi = {10.15479/AT:ISTA:98},
year = {2018},
}
@article{306,
abstract = {A cornerstone of statistical inference, the maximum entropy framework is being increasingly applied to construct descriptive and predictive models of biological systems, especially complex biological networks, from large experimental data sets. Both its broad applicability and the success it obtained in different contexts hinge upon its conceptual simplicity and mathematical soundness. Here we try to concisely review the basic elements of the maximum entropy principle, starting from the notion of ‘entropy’, and describe its usefulness for the analysis of biological systems. As examples, we focus specifically on the problem of reconstructing gene interaction networks from expression data and on recent work attempting to expand our system-level understanding of bacterial metabolism. Finally, we highlight some extensions and potential limitations of the maximum entropy approach, and point to more recent developments that are likely to play a key role in the upcoming challenges of extracting structures and information from increasingly rich, high-throughput biological data.},
author = {De Martino, Andrea and De Martino, Daniele},
journal = {Heliyon},
number = {4},
publisher = {Elsevier},
title = {{An introduction to the maximum entropy approach and its application to inference problems in biology}},
doi = {10.1016/j.heliyon.2018.e00596},
volume = {4},
year = {2018},
}
@article{457,
abstract = {Temperate bacteriophages integrate in bacterial genomes as prophages and represent an important source of genetic variation for bacterial evolution, frequently transmitting fitness-augmenting genes such as toxins responsible for virulence of major pathogens. However, only a fraction of bacteriophage infections are lysogenic and lead to prophage acquisition, whereas the majority are lytic and kill the infected bacteria. Unless able to discriminate lytic from lysogenic infections, mechanisms of immunity to bacteriophages are expected to act as a double-edged sword and increase the odds of survival at the cost of depriving bacteria of potentially beneficial prophages. We show that although restriction-modification systems as mechanisms of innate immunity prevent both lytic and lysogenic infections indiscriminately in individual bacteria, they increase the number of prophage-acquiring individuals at the population level. We find that this counterintuitive result is a consequence of phage-host population dynamics, in which restriction-modification systems delay infection onset until bacteria reach densities at which the probability of lysogeny increases. These results underscore the importance of population-level dynamics as a key factor modulating costs and benefits of immunity to temperate bacteriophages},
author = {Pleska, Maros and Lang, Moritz and Refardt, Dominik and Levin, Bruce and Guet, Calin C},
journal = {Nature Ecology and Evolution},
number = {2},
pages = {359 -- 366},
publisher = {Springer Nature},
title = {{Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity}},
doi = {10.1038/s41559-017-0424-z},
volume = {2},
year = {2018},
}
@article{161,
abstract = {Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells.},
author = {De Martino, Daniele and Mc, Andersson Anna and Bergmiller, Tobias and Guet, Calin C and Tkacik, Gasper},
journal = {Nature Communications},
number = {1},
publisher = {Springer Nature},
title = {{Statistical mechanics for metabolic networks during steady state growth}},
doi = {10.1038/s41467-018-05417-9},
volume = {9},
year = {2018},
}
@article{281,
abstract = {Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making.},
author = {Granados, Alejandro and Pietsch, Julian and Cepeda Humerez, Sarah A and Farquhar, Isebail and Tkacik, Gasper and Swain, Peter},
journal = {PNAS},
number = {23},
pages = {6088 -- 6093},
publisher = {National Academy of Sciences},
title = {{Distributed and dynamic intracellular organization of extracellular information}},
doi = {10.1073/pnas.1716659115},
volume = {115},
year = {2018},
}
@misc{5585,
abstract = {Mean repression values and standard error of the mean are given for all operator mutant libraries.},
author = {Igler, Claudia and Lagator, Mato and Tkacik, Gasper and Bollback, Jonathan P and Guet, Calin C},
publisher = {IST Austria},
title = {{Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring}},
doi = {10.15479/AT:ISTA:108},
year = {2018},
}
@misc{5562,
abstract = {This data was collected as part of the study [1]. It consists of preprocessed multi-electrode array recording from 160 salamander retinal ganglion cells responding to 297 repeats of a 19 s natural movie. The data is available in two formats: (1) a .mat file containing an array with dimensions “number of repeats” x “number of neurons” x “time in a repeat”; (2) a zipped .txt file containing the same data represented as an array with dimensions “number of neurons” x “number of samples”, where the number of samples is equal to the product of the number of repeats and timebins within a repeat. The time dimension is divided into 20 ms time windows, and the array is binary indicating whether a given cell elicited at least one spike in a given time window during a particular repeat. See the reference below for details regarding collection and preprocessing:
[1] Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry MJ II. Searching for Collective Behavior in a Large Network of Sensory Neurons. PLoS Comput Biol. 2014;10(1):e1003408.},
author = {Marre, Olivier and Tkacik, Gasper and Amodei, Dario and Schneidman, Elad and Bialek, William and Berry, Michael},
keyword = {multi-electrode recording, retinal ganglion cells},
publisher = {IST Austria},
title = {{Multi-electrode array recording from salamander retinal ganglion cells}},
doi = {10.15479/AT:ISTA:61},
year = {2017},
}
@article{720,
abstract = {Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.},
author = {Humplik, Jan and Tkacik, Gasper},
issn = {1553734X},
journal = {PLoS Computational Biology},
number = {9},
publisher = {Public Library of Science},
title = {{Probabilistic models for neural populations that naturally capture global coupling and criticality}},
doi = {10.1371/journal.pcbi.1005763},
volume = {13},
year = {2017},
}
@article{725,
abstract = {Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here, we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an “active” mode, in which they are sensitive to the swimming patterns of conspecifics, and a “passive” mode, where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors’ behavior. At the group level, switching between active and passive modes is uncorrelated among fish, but correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates as well as to other species.},
author = {Harpaz, Roy and Tkacik, Gasper and Schneidman, Elad},
issn = {00278424},
journal = {PNAS},
number = {38},
pages = {10149 -- 10154},
publisher = {National Academy of Sciences},
title = {{Discrete modes of social information processing predict individual behavior of fish in a group}},
doi = {10.1073/pnas.1703817114},
volume = {114},
year = {2017},
}