pith. sign in

arxiv: 1907.11075 · v1 · pith:FMHRXANKnew · submitted 2019-07-24 · 🧬 q-bio.NC · cs.AI· cs.LG· stat.ML

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

Andrew Warrington , Arthur Spencer , Frank Wood This is my paper

Pith reviewed 2026-05-24 16:33 UTC · model grok-4.3

classification 🧬 q-bio.NC cs.AIcs.LGstat.ML
keywords C. eleganscalcium imagingmembrane potentialsequential Monte Carlowhole-brain simulationconnectomestate imputation
0
0 comments X

The pith

A whole-connectome stochastic simulator of C. elegans imputes single-cell membrane potentials from partial calcium imaging data.

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

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.

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.

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.

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.

Where Pith is reading between the lines

These are 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.

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.

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.

Figures

Figures reproduced from arXiv: 1907.11075 by Andrew Warrington, Arthur Spencer, Frank Wood.

Figure 1
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 planne… view at source ↗
Figure 2
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. 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 ↗
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), and optimized parameters (green). (b) shows the parameter optimization using PMVO, plotted as the median, upper and lower quartile across… view at source ↗
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] view at source ↗
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] view at source ↗
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] view at source ↗
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] view at source ↗
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 initial parameters (red), and optimized parameters (green). (b) shows the distribution of parameter convergence paths over the course of PMVO opti… view at source ↗
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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

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

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.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 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.
axioms (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 no independent evidence
    purpose: To provide regularization for imputing latent membrane potentials from calcium imaging observations.
    The simulator is introduced as a new construct in the paper.

pith-pipeline@v0.9.0 · 5685 in / 1479 out tokens · 35786 ms · 2026-05-24T16:33:06.796419+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages · 7 internal anchors

  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

  9. [9]

    Marblestone

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

    work page 2016

  10. [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

    work page 2016

  11. [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

    work page 2015

  12. [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

    work page 2016

  13. [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. doi: 10.3389/fncom.2014.00137. 9

    work page doi:10.3389/fncom.2014.00137 2014

  14. [14]

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

    work page 2013

  15. [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

    work page 2002

  16. [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

    work page 2003

  17. [17]

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

    work page 2013

  18. [18]

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

    work page 2008

  19. [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

    work page 1996

  20. [20]

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

    work page 2010

  21. [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

    work page doi:10.1371/journal.pcbi.1001066 2011

  22. [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

    work page 1986

  23. [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

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  24. [24]

    Hines and N

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

    work page 2006

  25. [25]

    Gewaltig and M

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

    work page 2007

  26. [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

    work page 2010

  27. [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

    work page doi:10.1134/s2079059715060052 2015

  28. [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

    work page 2012

  29. [29]

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

    work page 1938

  30. [30]

    Grienberger and A

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

    work page 2012

  31. [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

    work page 2004

  32. [32]

    Doucet and A

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

    work page 2010

  33. [33]

    Variational Optimization

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

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  34. [34]

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

    work page 1992

  35. [35]

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

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  36. [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

    work page 2001

  37. [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

    work page 2009

  38. [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

    work page 2010

  39. [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

    work page doi:10.1371/journal.pcbi.1005423 2017

  40. [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 3486–3495, 2017

    work page 2017

  41. [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

    work page 2017

  42. [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

    work page 2018

  43. [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

    work page

  44. [44]

    doi: 10.1371/journal.pone.0168415

    work page doi:10.1371/journal.pone.0168415

  45. [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

    work page doi:10.1371/journal.pcbi.1002890 2013

  46. [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

    work page doi:10.1016/j.cell.2018.08.018 2018

  47. [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

    work page 1998

  48. [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

    work page doi:10.1007/s13752-019-00319-5 2019

  49. [49]

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

    work page 2018

  50. [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

    work page 2017

  51. [51]

    Cohen and T

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

    work page 2014

  52. [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

    work page doi:10.1038/nmeth.2560.a 2013

  53. [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

    work page 2017

  54. [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

    work page 1990

  55. [55]

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

    work page 1907

  56. [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

    work page 1952

  57. [57]

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

    work page 2018

  58. [58]

    Masset, R

    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

    work page doi:10.1016/j 2006

  59. [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):17504–17509, 2011

    work page 2011

  60. [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

    work page 2012

  61. [61]

    http://www.wormatlas.org

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

    work page 2018

  62. [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

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  63. [63]

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

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  64. [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

    work page 2011

  65. [65]

    Metaxakis, D

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

    work page 2018

  66. [66]

    Cohen and J

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

    work page 2018

  67. [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

    work page 2013

  68. [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

    work page 2018

  69. [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

    work page 2018

  70. [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

    work page 2017

  71. [71]

    Teng and F

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

    work page 2018

  72. [72]

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

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  73. [73]

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

    work page 1970

  74. [74]

    Chib and E

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

    work page 1995

  75. [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

    work page 2010

  76. [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

    work page 2015

  77. [77]

    Haario, E

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

    work page 2005

  78. [78]

    Tempering by Subsampling

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

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  79. [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

    work page 2015

  80. [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

    work page 2008

Showing first 80 references.