Pith. sign in

REVIEW 2 major objections 1 minor 86 references

The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging

T0 review · 2 major / 1 minor · reviewed 2026-05-24 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A whole-connectome stochastic simulator of C. elegans imputes single-cell membrane potentials from partial calcium imaging data.

desk verdict The paper builds a connectome-derived stochastic simulator for C. elegans and uses SMC to impute membrane potentials from calcium data, but only demonstrates this on synthetic trajectories generated from the same model. read the letter →

arxiv 1907.11075 v1 pith:FMHRXANK submitted 2019-07-24 q-bio.NC cs.AIcs.LGstat.ML

Andrew Warrington , Arthur Spencer , Frank Wood This is my paper · ORCID
classification q-bio.NCcs.AIcs.LGstat.ML
keywords C.eleganscalciumimagingmembranepotentialsequentialMonteCarlowhole-brainsimulationconnectomestateimputation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper constructs a stochastic simulator of the entire C. elegans nervous system and body that incorporates the known anatomical connectome. It shows that this simulator supplies enough regularization for sequential Monte Carlo methods to recover hidden membrane-potential time series from incomplete calcium-fluorescence observations. The same SMC machinery is also used to optimize simulator parameters by maximizing an approximation to the marginal likelihood. Experiments are performed on synthetic data whose statistics match current laboratory recordings. The work thereby links measurable fluorescence traces to latent voltage states at cellular resolution inside an anatomically grounded model.

What carries the argument

Stochastic whole-brain and body simulator built from the C. elegans connectome, combined with sequential Monte Carlo (SMC) for state imputation and evidence approximation.

What would settle it

Direct comparison of SMC-imputed membrane potentials against simultaneous electrophysiological recordings in the same animal, checking whether the imputed traces lie within the measurement noise of the true voltages.

Watch

Extended reading notes

Core claim

The anatomically grounded whole-connectome simulator is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations via sequential Monte Carlo, and the same procedure yields a variational route to parameter estimation.

Load-bearing premise

The custom stochastic simulator supplies enough biological regularization that SMC can accurately recover membrane potentials from partial calcium observations on data representative of real experiments.

Editorial extensions

If this is right

  • Imputation yields time-varying brain-state estimates at single-cell fidelity from covariates that are already measurable in the lab.
  • Simulator parameters can be learned by variational optimization of the noisy model-evidence approximation supplied by SMC.
  • The approach operates on synthetic data whose dimension and noise statistics match current calcium-imaging experiments.
  • The loop from connectome to simulator to imputed voltages constitutes the first reported use of a full anatomical model for this inference task.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the simulator remains accurate on real rather than synthetic data, the method could supply voltage estimates in any preparation where only calcium imaging is feasible.
  • Parameter learning inside the simulator could identify which synaptic or cellular properties are most constrained by population calcium recordings.
  • The framework might be tested by withholding subsets of cells from the imputation step and checking whether held-out cells are still recovered at usable accuracy.
  • Extending the same SMC machinery to multi-animal or longitudinal datasets could reveal how circuit parameters change across individuals or over development.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper develops a stochastic whole-brain and body simulator of C. elegans grounded in the connectome and uses sequential Monte Carlo (SMC) to impute latent membrane potentials from partial calcium fluorescence observations. It further proposes variational optimization of simulator parameters via the SMC evidence estimate. All reported experiments and validation are performed exclusively on synthetic trajectories generated from the same model (with parameters either known or optimized), using data dimensions representative of current laboratory measurements.

Significance. If the connectome-derived simulator supplies biologically faithful regularization that enables accurate recovery of membrane potentials under realistic noise and partial observations, the method would provide a novel route to infer unobservable neural states at cellular resolution from standard calcium imaging. The SMC-based parameter learning is a secondary contribution. However, the exclusive use of in-model synthetic data leaves open whether the regularization holds when the true dynamics deviate from the assumed rules, limiting immediate impact on experimental neuroscience.

major comments (2)
  1. [Abstract / Experiments] Abstract and experiments section: The central claim that the simulator is 'sufficiently regularizing' to allow imputation rests on recovery performance for trajectories drawn from the identical generative model. This design cannot distinguish biological fidelity from successful inversion of the forward process; no experiments under model misspecification (altered ion-channel kinetics, noise statistics, or connectome rules) or on real calcium traces are reported, leaving the regularization claim untested for the intended use case.
  2. [Methods / Results] Methods / Results: The SMC imputation and variational parameter estimation are demonstrated only when the data-generating parameters are either known or recovered from the same simulator; no cross-validation against held-out real or perturbed data is shown to establish that the inferred potentials reflect observations rather than simulator priors.
minor comments (1)
  1. [Abstract] Abstract: 'data of dimension and type representative of that which are measured' contains a subject-verb agreement error ('which are' should be 'which is').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comments point by point below, clarifying the intended scope of the work as a synthetic proof-of-principle.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and experiments section: The central claim that the simulator is 'sufficiently regularizing' to allow imputation rests on recovery performance for trajectories drawn from the identical generative model. This design cannot distinguish biological fidelity from successful inversion of the forward process; no experiments under model misspecification (altered ion-channel kinetics, noise statistics, or connectome rules) or on real calcium traces are reported, leaving the regularization claim untested for the intended use case.

    Authors: The manuscript explicitly states that all experiments use synthetic trajectories generated from the simulator itself, with data dimensions representative of current laboratory measurements. The central claim is scoped to this controlled setting: that the connectome-derived dynamics provide regularization sufficient for SMC-based imputation when observations are consistent with the model. This is a necessary first validation step before real-data application. We do not claim to have tested biological fidelity or robustness to misspecification, as those would require either real traces or deliberate model perturbations outside the current scope. The abstract and methods already qualify the synthetic nature of the results. revision: no

  2. Referee: [Methods / Results] Methods / Results: The SMC imputation and variational parameter estimation are demonstrated only when the data-generating parameters are either known or recovered from the same simulator; no cross-validation against held-out real or perturbed data is shown to establish that the inferred potentials reflect observations rather than simulator priors.

    Authors: When parameters are known, imputation performance is evaluated on held-out synthetic trajectories. When parameters are variationally optimized, the evidence estimate is maximized on training trajectories and imputation is assessed on separate held-out trajectories from the same model. This demonstrates that the procedure recovers both parameters and latents when the generative assumptions hold. The design isolates the contribution of the observations within the model; pure prior sampling would not match the specific observed calcium dynamics. Cross-validation on real or perturbed data is not included because the work is positioned as synthetic validation of the method. revision: no

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; simulator and inference are independently grounded

full rationale

The paper constructs a stochastic simulator directly from the known C. elegans connectome anatomy and applies standard sequential Monte Carlo to impute latent membrane potentials from calcium observations. Experiments on synthetic trajectories drawn from this model constitute ordinary validation of an inference procedure rather than a reduction of the claim to its own inputs. No self-definitional equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the derivation; the central regularization claim rests on the anatomical grounding and SMC properties, which remain externally verifiable.

Assumptions & free parameters 1 free parameters · 1 assumptions · 1 invented entities

The central claim rests on the fidelity of a newly constructed stochastic simulator and the suitability of SMC for the imputation task. Because only the abstract is available, specific free parameters, additional axioms, and invented entities cannot be enumerated in detail.

free parameters (1)
  • Simulator parameters
    The abstract states that simulator parameters are learned via variational optimization of the SMC evidence approximation, implying the existence of free parameters whose values are not specified here.
assumptions (1)
  • domain assumption The C. elegans connectome supplies a sufficient anatomical and dynamical basis for a stochastic whole-brain simulator that can regularize latent membrane potential imputation.
    This premise is invoked when the authors state that the simulator is 'anatomically grounded' and 'sufficiently regularizing'.
invented entities (1)
  • Stochastic whole-brain and body simulator of C. elegans
    purpose: To provide regularization for imputing latent membrane potentials from calcium imaging observations.
    The simulator is introduced as a new construct in the paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging." pith.science (2026). https://pith.science/paper/FMHRXANK

@misc{pith2026190711075,
  author       = {Pith},
  title        = {Pith review of: The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FMHRXANK}},
  note         = {Machine review of arXiv:1907.11075}
}
read the original abstract

We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to "complete the circle," where an anatomically grounded whole-connectome simulator is used to impute a time-varying "brain" state at single-cell fidelity from covariates that are measurable in practice. The sequential Monte Carlo (SMC) method we employ not only enables imputation of said latent states but also presents a strategy for learning simulator parameters via variational optimization of the noisy model evidence approximation provided by SMC. Our imputation and parameter estimation experiments were conducted on distributed systems using novel implementations of the aforementioned techniques applied to synthetic data of dimension and type representative of that which are measured in laboratories currently.

Figures

Figures reproduced from arXiv: 1907.11075 by the authors.

Figure 1
Figure 1. (a): The C. elegans roundworm, reproduced with permission from anon. (b): Typical in vivo fluorescence data on which we propose to condition (from Kato et al. [11]). (c): Graphical model of our C. elegans simulator; variables defined in Section 2. The dashed box denotes the 994 dimensional latent state of the worm at each time step. (d): Diagram adapted from Sarma et al. [4] reflecting the community planned developm… view at source ↗
Figure 2
Figure 2. Comparison of PMVO, PMMH and PT methods for parameter estimation in the autore [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figures 3(a) and 3(c) show the imputed voltage traces and body poses when using the true [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 3
Figure 3. Figure 3: Estimating C. elegans simulator parameters as described in Section 4.2. (a) and (c) show the filtering distributions of SMC reconstructions of the membrane potentials of 15 cells given the true generative parameter (blue), optimization algorithm initial parameters (red…
Figure 4
Figure 4. Figure 4: Experiment showing the accumulated error when using and ODE solver ( [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: Experiment showing the success of stimulating the whole connectome with motor feedback [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: Unaligned WormSim reconstructions. The shape of the reconstructed worm is correct at [PITH_FULL_IMAGE:figures/full_fig_p022_6.png]
Figure 7
Figure 7. Figure 7: Experiment showing the results of the initialization. Failed initializations are shown as red [PITH_FULL_IMAGE:figures/full_fig_p025_7.png]
Figure 3
Figure 3. Figure 3: Estimating C. elegans simulator parameters when observing all 302 neurons. (a) and (c) show the filtering distributions of SMC reconstructions of the membrane potentials of 15 cells given the true generative parameter (blue), two different optimization algorithm initia…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages

  1. [1]

    H. Seung. Neuroscience: towards functional connectomics. Nature, 471(7337):170–172, 2011

    work page 2011

  2. [2]

    W. B. Kristan and P. Katz. Form and function in systems neuroscience.Current biology, 16(19):R828–R831, 2006

    work page 2006

  3. [3]

    R. W. Doty. Consciousness from neurons. Acta neurobiologiae experimentalis, 35(5-6):791–804, 1975

    work page 1975

  4. [4]

    G. P. Sarma, C. W. Lee, T. Portegys, V . Ghayoomie, T. Jacobs, B. Alicea, M. Cantarelli, M. Currie, R. C. Gerkin, S. Gingell, et al. Openworm: overview and recent advances in integrative biological simulation of caenorhabditis elegans. Philosophical Transactions of the Royal Society B: Biological Sciences, 373 (1758):20170382, 2018

    work page 2018

  5. [5]

    Palyanov, S

    A. Palyanov, S. Khayrulin, and S. D. Larson. Application of smoothed particle hydrodynamics to modeling mechanisms of biological tissue. Advances in Engineering Software, 98:1–11, 2016. ISSN 18735339. doi: 10.1016/j.advengsoft.2016.03.002

    work page doi:10.1016/j.advengsoft.2016.03.002 2016

  6. [6]

    J. H. Boyle, S. Berri, and N. Cohen. Gait modulation in c. elegans: an integrated neuromechanical model. Frontiers in computational neuroscience, 6:10, 2012

    work page 2012

  7. [7]

    Gleeson, D

    P. Gleeson, D. Lung, R. Grosu, R. Hasani, and S. D. Larson. c302: a multiscale framework for modelling the nervous system of caenorhabditis elegans.Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1758):20170379, 2018

    work page 2018

  8. [8]

    Kunert, E

    J. Kunert, E. Shlizerman, and J. N. Kutz. Low-dimensional functionality of complex network dynamics: Neurosensory integration in the caenorhabditis elegans connectome. Physical Review E, 89(5):052805, 2014

    work page 2014

Show all 86 references
  1. [9]

    Marblestone

    A. Marblestone. Simple c. elegans. https://https://github.com/adammarblestone/ simple-C-elegans, 2016

    2016

  2. [10]

    Rahmati, K

    V . Rahmati, K. Kirmse, D. Markovi´c, K. Holthoff, and S. J. Kiebel. Inferring neuronal dynamics from calcium imaging data using biophysical models and bayesian inference. PLoS computational biology, 12 (2):e1004736, 2016

    2016

  3. [11]

    S. Kato, H. S. Kaplan, T. Schrödel, S. Skora, T. H. Lindsay, E. Yemini, S. Lockery, and M. Zimmer. Global brain dynamics embed the motor command sequence of caenorhabditis elegans. Cell, 163(3):656–669, 2015

    2015

  4. [12]

    J. P. Nguyen, F. B. Shipley, A. N. Linder, G. S. Plummer, M. Liu, S. U. Setru, J. W. Shaevitz, and A. M. Leifer. Whole-brain calcium imaging with cellular resolution in freely behaving caenorhabditis elegans. Proceedings of the National Academy of Sciences, 113(8):E1074–E1081, 2016

    2016

  5. [13]

    Szigeti, P

    B. Szigeti, P. Gleeson, M. Vella, S. Khayrulin, A. Palyanov, J. Hokanson, M. Currie, M. Cantarelli, G. Idili, and S. Larson. OpenWorm: an open-science approach to modeling Caenorhabditis elegans. Frontiers in Computational Neuroscience, 8(November):1–7, 2014. ISSN 1662-5188. d...

    2014 doi

  6. [14]

    M. E. Spira and A. Hai. Multi-electrode array technologies for neuroscience and cardiology. Nature nanotechnology, 8(2):83, 2013

    2013

  7. [15]

    T. W. Margrie, M. Brecht, and B. Sakmann. In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain. Pflügers Archiv, 444(4):491–498, 2002

    2002

  8. [16]

    Stosiek, O

    C. Stosiek, O. Garaschuk, K. Holthoff, and A. Konnerth. In vivo two-photon calcium imaging of neuronal networks. Proceedings of the National Academy of Sciences, 100(12):7319–7324, 2003

    2003

  9. [17]

    S. H. Chung, L. Sun, and C. V . Gabel. In vivo neuronal calcium imaging in C. elegans.J Vis Exp, (74), Apr 2013

    2013

  10. [18]

    E. L. Ardiel and C. H. Rankin. Behavioral plasticity in the c. elegans mechanosensory circuit. Journal of neurogenetics, 22(3):239–255, 2008

    2008

  11. [19]

    S. R. Wicks, C. J. Roehrig, and C. H. Rankin. A dynamic network simulation of the nematode tap with- drawal circuit: predictions concerning synaptic function using behavioral criteria. Journal of Neuroscience, 16(12):4017–4031, 1996

    1996

  12. [20]

    E. L. Ardiel and C. H. Rankin. An elegant mind: learning and memory in caenorhabditis elegans. Learning & memory, 17(4):191–201, 2010

    2010

  13. [21]

    L. R. Varshney, B. L. Chen, E. Paniagua, D. H. Hall, and D. B. Chklovskii. Structural properties of the caenorhabditis elegans neuronal network. PLOS Computational Biology, 7(2):1–21, 02 2011. doi: 10.1371/journal.pcbi.1001066

    2011 doi

  14. [22]

    J. G. White, E. Southgate, J. N. Thomson, and S. Brenner. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos. Trans. R. Soc. Lond., B, Biol. Sci., 314(1165):1–340, Nov 1986

    1986

  15. [23]

    R. M. Hasani, V . Beneder, M. Fuchs, D. Lung, and R. Grosu. Sim-ce: An advanced simulink platform for studying the brain of caenorhabditis elegans. arXiv preprint arXiv:1703.06270, 2017

    2017 arXiv

  16. [24]

    Hines and N

    M. Hines and N. Carnevale. The neuron simulation environment. NEURON, 9(6), 2006

    2006

  17. [25]

    Gewaltig and M

    M.-O. Gewaltig and M. Diesmann. Nest (neural simulation tool). Scholarpedia, 2(4):1430, 2007

    2007

  18. [26]

    Kuramochi and Y

    M. Kuramochi and Y . Iwasaki. Quantitative modeling of neuronal dynamics in c. elegans. InInternational Conference on Neural Information Processing, pages 17–24. Springer, 2010

    2010

  19. [27]

    A. Y . Palyanov and S. S. Khayrulin. Sibernetic: A software complex based on the pci sph algorithm aimed at simulation problems in biomechanics. Russian Journal of Genetics: Applied Research, 5(6):635–641, Nov 2015. doi: 10.1134/S2079059715060052

    2015 doi

  20. [28]

    Q. Wen, M. D. Po, E. Hulme, S. Chen, X. Liu, S. W. Kwok, M. Gershow, A. M. Leifer, V . Butler, C. Fang-Yen, et al. Proprioceptive coupling within motor neurons drives c. elegans forward locomotion. Neuron, 76(4):750–761, 2012

    2012

  21. [29]

    A. V . Hill. The heat of shortening and the dynamic constants of muscle.Proc. R. Soc. Lond. B, 126(843): 136–195, 1938

    1938

  22. [30]

    Grienberger and A

    C. Grienberger and A. Konnerth. Imaging calcium in neurons. Neuron, 73(5):862–885, 2012

    2012

  23. [31]

    Yasuda, E

    R. Yasuda, E. A. Nimchinsky, V . Scheuss, T. A. Pologruto, T. G. Oertner, B. L. Sabatini, and K. Svoboda. Imaging calcium concentration dynamics in small neuronal compartments. Sci. STKE, 2004(219):pl5–pl5, 2004

    2004

  24. [32]

    Doucet and A

    A. Doucet and A. Johansen. A tutorial on particle filtering and smoothing: fifteen years later, 2010

    2010

  25. [33]

    Staines and D

    J. Staines and D. Barber. Variational optimization. arXiv preprint arXiv:1212.4507, 2012

    2012 arXiv

  26. [34]

    R. J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3-4):229–256, 1992

    1992

  27. [35]

    D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  28. [36]

    Weaver and N

    L. Weaver and N. Tao. The optimal reward baseline for gradient-based reinforcement learning. In Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence, pages 538–545. Morgan Kaufmann Publishers Inc., 2001. 10

    2001

  29. [37]

    J. T. V ogelstein, B. O. Watson, A. M. Packer, R. Yuste, B. Jedynak, and L. Paninski. Spike inference from calcium imaging using sequential monte carlo methods. Biophysical journal, 97(2):636–655, 2009

    2009

  30. [38]

    J. T. V ogelstein, A. M. Packer, T. A. Machado, T. Sippy, B. Babadi, R. Yuste, and L. Paninski. Fast nonneg- ative deconvolution for spike train inference from population calcium imaging.Journal of neurophysiology, 104(6):3691–3704, 2010

    2010

  31. [39]

    Friedrich, P

    J. Friedrich, P. Zhou, and L. Paninski. Fast online deconvolution of calcium imaging data. PLOS Computational Biology, 13(3):1–26, 03 2017. doi: 10.1371/journal.pcbi.1005423

    2017 doi

  32. [40]

    Aitchison, L

    L. Aitchison, L. Russell, A. M. Packer, J. Yan, P. Castonguay, M. Hausser, and S. C. Turaga. Model-based bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit. In Advances in Neural Information Processing Systems, pages 3486–...

    2017

  33. [41]

    Speiser, J

    A. Speiser, J. Yan, E. W. Archer, L. Buesing, S. C. Turaga, and J. H. Macke. Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. In Advances in Neural Information Processing Systems, pages 4024–4034, 2017

    2017

  34. [42]

    R. C. Gerkin, R. J. Jarvis, and S. M. Crook. Towards systematic, data-driven validation of a collaborative, multi-scale model of caenorhabditis elegans. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1758):20170381, 2018

    2018

  35. [43]

    Kuramochi and M

    M. Kuramochi and M. Doi. A computational model based on multi-regional calcium imaging represents the spatio-temporal dynamics in a caenorhabditis elegans sensory neuron. PLOS ONE, 12(1):1–19, 01

  36. [44]

    doi: 10.1371/journal.pone.0168415

    doi

  37. [45]

    E. J. Izquierdo and R. D. Beer. Connecting a Connectome to Behavior: An Ensemble of Neuroanatomical Models of C. elegans Klinotaxis. PLoS Computational Biology , 9(2), 2013. ISSN 1553734X. doi: 10.1371/journal.pcbi.1002890

    2013 doi

  38. [46]

    Q. Liu, P. B. Kidd, M. Dobosiewicz, and C. I. Bargmann. C. elegans awa olfactory neurons fire calcium- mediated all-or-none action potentials. Cell, 175(1):57 – 70.e17, 2018. ISSN 0092-8674. doi: https: //doi.org/10.1016/j.cell.2018.08.018

    2018 doi

  39. [47]

    M. B. Goodman, D. H. Hall, L. Avery, and S. R. Lockery. Active currents regulate sensitivity and dynamic range in c. elegans neurons. Neuron, 20(4):763–772, 1998

    1998

  40. [48]

    K. M. Stiefel and D. S. Brooks. Why is there no successful whole brain simulation (yet)? Biological Theory, Mar 2019. ISSN 1555-5550. doi: 10.1007/s13752-019-00319-5

    2019 doi

  41. [49]

    S. D. Larson, P. Gleeson, and A. E. Brown. Connectome to behaviour: modelling caenorhabditis elegans at cellular resolution, 2018

    2018

  42. [50]

    J. M. Kunert, J. L. Proctor, S. L. Brunton, and J. N. Kutz. Spatiotemporal feedback and network structure drive and encode caenorhabditis elegans locomotion. PLoS computational biology, 13(1):e1005303, 2017

    2017

  43. [51]

    Cohen and T

    N. Cohen and T. Sanders. Nematode locomotion: dissecting the neuronal–environmental loop. Current opinion in neurobiology, 25:99–106, 2014

    2014

  44. [52]

    Yemini, T

    E. Yemini, T. Jucikas, L. Grundy, A. Brown, and W. Schafer. A database of c. elegans behavioral phenotypes. Nature Methods, 10(9):877–879, 2013. doi: 10.1038/nmeth.2560.A

    2013 doi

  45. [53]

    Mujika, P

    A. Mujika, P. Leškovsk`y, R. Álvarez, M. A. Otaduy, and G. Epelde. Modeling behavioral experiment interaction and environmental stimuli for a synthetic c. elegans. Frontiers in neuroinformatics, 11:71, 2017

    2017

  46. [54]

    Abbott and T

    L. Abbott and T. B. Kepler. Model neurons: from hodgkin-huxley to hopfield. In Statistical mechanics of neural networks, pages 5–18. Springer, 1990

    1990

  47. [55]

    L. F. Abbott. Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain research bulletin, 50(5-6):303–304, 1999

    1907

  48. [56]

    A. L. Hodgkin and A. F. Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology, 117(4):500–544, 1952

    1952

  49. [57]

    J. A. Hertz. Introduction to the theory of neural computation. CRC Press, 2018

    2018

  50. [58]

    Saarinen, M.-L

    A. Saarinen, M.-L. Linne, and O. Yli-Harja. Modeling single neuron behavior using stochastic differential equations. Neurocomputing, 69(10):1091 – 1096, 2006. ISSN 0925-2312. doi: https://doi.org/10.1016/j. neucom.2005.12.052. Computational Neuroscience: Trends in Research 2006. 11

    2006 doi

  51. [59]

    Vidal-Gadea, S

    A. Vidal-Gadea, S. Topper, L. Young, A. Crisp, L. Kressin, E. Elbel, T. Maples, M. Brauner, K. Erbguth, A. Axelrod, et al. Caenorhabditis elegans selects distinct crawling and swimming gaits via dopamine and serotonin. Proceedings of the National Academy of Sciences, 108(42):1...

    2011

  52. [60]

    Lebois, P

    F. Lebois, P. Sauvage, C. Py, O. Cardoso, B. Ladoux, P. Hersen, and J.-M. Di Meglio. Locomotion control of caenorhabditis elegans through confinement. Biophysical journal, 102(12):2791–2798, 2012

    2012

  53. [61]

    http://www.wormatlas.org

    WormAtlas. http://www.wormatlas.org. Accessed: 2018-05-15

    2018

  54. [62]

    S. W. Linderman, G. E. Mena, H. Cooper, L. Paninski, and J. P. Cunningham. Reparameterizing the birkhoff polytope for variational permutation inference. arXiv preprint arXiv:1710.09508, 2017

    2017 arXiv

  55. [63]

    G. Mena, D. Belanger, S. Linderman, and J. Snoek. Learning latent permutations with gumbel-sinkhorn networks. arXiv preprint arXiv:1802.08665, 2018

    2018 arXiv

  56. [64]

    A. J. Bretscher, E. Kodama-Namba, K. E. Busch, R. J. Murphy, Z. Soltesz, P. Laurent, and M. Bono. Temperature, oxygen, and salt-sensing neurons in c. elegans are carbon dioxide sensors that control avoidance behavior. Neuron, 69(6):1099–1113, 2011

    2011

  57. [65]

    Metaxakis, D

    A. Metaxakis, D. Petratou, and N. Tavernarakis. Multimodal sensory processing in caenorhabditis elegans. Open biology, 8(6):180049, 2018

    2018

  58. [66]

    Cohen and J

    N. Cohen and J. E. Denham. Whole animal modeling: piecing together nematode locomotion. Current Opinion in Systems Biology, 2018

    2018

  59. [67]

    E. J. Izquierdo and R. D. Beer. Connecting a connectome to behavior: an ensemble of neuroanatomical models of c. elegans klinotaxis. PLoS computational biology, 9(2):e1002890, 2013

    2013

  60. [68]

    E. O. Olivares, E. J. Izquierdo, and R. D. Beer. Potential role of a ventral nerve cord central pattern generator in forward and backward locomotion in caenorhabditis elegans. Network Neuroscience, 2(3): 323–343, 2018

    2018

  61. [69]

    A. D. Fouad, S. Teng, J. R. Mark, A. Liu, P. Alvarez-Illera, H. Ji, A. Du, P. D. Bhirgoo, E. Cornblath, S. A. Guan, et al. Distributed rhythm generators underlie caenorhabditis elegans forward locomotion. Elife, 7: e29913, 2018

    2018

  62. [70]

    S. Gao, S. A. Guan, A. D. Fouad, J. Meng, Y .-C. Huang, Y . Li, S. Alcaire, W. Hung, T. Kawano, Y . Lu, et al. Excitatory motor neurons are local central pattern generators in an anatomically compressed motor circuit for reverse locomotion. bioRxiv, page 135418, 2017

    2017

  63. [71]

    Teng and F

    M. Teng and F. Wood. Bayesian distributed stochastic gradient descent. InAdvances in Neural Information Processing Systems, pages 6378–6388, 2018

    2018

  64. [72]

    J. Chen, X. Pan, R. Monga, S. Bengio, and R. Jozefowicz. Revisiting distributed synchronous sgd. arXiv preprint arXiv:1604.00981, 2016

    2016 arXiv

  65. [73]

    W. K. Hastings. Monte carlo sampling methods using markov chains and their applications. Biometrika, 57(1):97–109, 1970

    1970

  66. [74]

    Chib and E

    S. Chib and E. Greenberg. Understanding the metropolis-hastings algorithm. The american statistician, 49 (4):327–335, 1995

    1995

  67. [75]

    Andrieu, A

    C. Andrieu, A. Doucet, and R. Holenstein. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3):269–342, 2010

    2010

  68. [76]

    Kantas, A

    N. Kantas, A. Doucet, S. S. Singh, J. Maciejowski, N. Chopin, et al. On particle methods for parameter estimation in state-space models. Statistical science, 30(3):328–351, 2015

    2015

  69. [77]

    Haario, E

    H. Haario, E. Saksman, and J. Tamminen. Componentwise adaptation for high dimensional mcmc. Computational Statistics, 20(2):265–273, 2005

    2005

  70. [78]

    Meent, B

    J.-W. Meent, B. Paige, and F. Wood. Tempering by subsampling. arXiv preprint arXiv:1401.7145, 2014

    2014 arXiv

  71. [79]

    Maclaurin and R

    D. Maclaurin and R. P. Adams. Firefly monte carlo: Exact mcmc with subsets of data. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

    2015

  72. [80]

    Angelopoulos and J

    N. Angelopoulos and J. Cussens. Bayesian learning of bayesian networks with informative priors. Annals of Mathematics and Artificial Intelligence, 54(1-3):53–98, 2008. 12

    2008

  73. [81]

    Gupta, L

    S. Gupta, L. Hainsworth, J. Hogg, R. Lee, and J. Faeder. Evaluation of parallel tempering to accelerate bayesian parameter estimation in systems biology. In 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pages 690–697....

    2018

  74. [82]

    R. H. Swendsen and J.-S. Wang. Replica monte carlo simulation of spin-glasses. Physical review letters, 57(21):2607, 1986

    1986

  75. [83]

    Altekar, S

    G. Altekar, S. Dwarkadas, J. P. Huelsenbeck, and F. Ronquist. Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics, 20(3):407–415, 01 2004. ISSN 1367-4803. doi: 10.1093/bioinformatics/btg427

    2004 doi

  76. [84]

    Miasojedow, E

    B. Miasojedow, E. Moulines, and M. Vihola. An adaptive parallel tempering algorithm. Journal of Computational and Graphical Statistics, 22(3):649–664, 2013

    2013

  77. [85]

    tutorial,

    M. K. Ł ˛ acki and B. Miasojedow. State-dependent swap strategies and automatic reduction of number of temperatures in adaptive parallel tempering algorithm. Statistics and Computing, 26(5):951–964, 2016. 13 A Supplementary Materials In this supplement, we offer additional pro...

    2016

  78. [86]

    standard

    If the sample is rejected, the state of the Markov chain is set to the value at the previous step, with value θ1 ← θ0. The current state of the Markov chain, whether the step was accepted or rejected, is appended to the set of samples, {θt′}t′∈{0,1}. This process iterated prop...

Pith tools

Reviewed May 24, 2026 · model on record in the stance chip above.