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REVIEW 4 major objections 4 minor 51 references

A data-driven biophysical network model reproduces C. elegans premotor neural dynamics

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that a minimal biophysical model of the C. elegans premotor network, driven by real whole-brain calcium imaging, reproduces the worm's stochastic forward-reversal switching and predicts behavioral states like dwelling…

desk verdict A promising driven-network model of the C. elegans premotor circuit whose central 'reproduces/predicts' claim is undermined by in-sample validation; the fitting and validation datasets are the same. read the letter →

arxiv 2501.00278 v1 pith:OCYQPO3D submitted 2024-12-31 q-bio.NC physics.bio-ph

Megan Morrison , Lai-Sang Young This is my paper · ORCID
classification q-bio.NCphysics.bio-ph MSC 92C2037N25
keywords C.eleganspremotornetworkbiophysicalmodelstochasticswitchinggapjunctionswhole-braincalciumimagingdrivendynamicalsystemdwellingversusroaming
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 claims that the seemingly random switching between forward crawling and reversal in C. elegans can be reproduced by a compact, data-driven biophysical model: 15 core premotor neurons, recurrently connected according to the connectome, driven by the recorded activity of 112 presynaptic "signal" neurons. Using whole-brain calcium imaging to fit synaptic weights and biases, and voltage-clamp data for intrinsic dynamics, the model produces forward/reversal cluster activity whose switching, pairwise correlations, activity distributions, and dwell times resemble real recordings. The point is to move beyond phenomenological Markov or limit-cycle descriptions toward a mechanistic account of how identified neurons cooperate. If the claim holds, the model offers an analyzable tool for explaining how gap junctions synchronize neural clusters, how synaptic input drives switches, and how sensory activity shapes behavior such as roaming versus dwelling.

What carries the argument

The central object is the driven dynamical system of Eq. (2): 15 core premotor neurons with bistable cubic intrinsic dynamics (low state at $x=-0.8$, high state at $x=1$), gap-junction coupling $W_{ij}$ read from the connectome, and rectified synaptic coupling $A_{ij}\sigma(x_j)$ with $\sigma=\text{ReLU}$; the forcing comes from 112 signal-neuron activity traces taken directly from whole-brain imaging data. The load-bearing design decision is that the core group is small enough to be analyzed while the unmodeled presynaptic population is treated as external input, which lets the parameters $A_{ij}$ and $d_i$ be fit by linear regression rather than by tuning an entire connectome. The named mechanism is the division of labor: gap junctions equalize and synchronize cluster members, synaptic inputs drive the irregular high/low transitions, and the interaction of the two produces the observed stochastic switching.

What would settle it

Use paired electrophysiology or optogenetic activation to measure the sign of a single identified premotor synapse, for example RIM to AVA. The model's fitted weights require RIM to AVA to be inhibitory, while a published gene-expression-based prediction classifies it as excitatory; observing excitation would show the polarity set is wrong. Separately, optogenetically activating OLQ should lengthen forward runs if the model is right; observing the known head-withdrawal or nose-touch avoidance instead would reject the model's forward-promoting assignment.

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Extended reading notes

Core claim

The central claim is that a driven dynamical system—a recurrent network of the 15 premotor interneurons most closely tied to forward and reversal locomotion, forced by time series of 112 signal neurons taken from whole-brain imaging—reproduces the main statistical features of the worm's premotor activity. Each core neuron's calcium level $x_i$ evolves by $\tau \dot{x}_i = f_i(x_i) + \beta \sum_j W_{ij}(x_j-x_i) + \sum_j A_{ij}\sigma(x_j)$, with $f_i$ a cubic giving low- and high-activity attractors, $W_{ij}$ gap-junction strengths from the connectome, and $A_{ij}$ signed synaptic weights estimated by linear regression over 22 imaging datasets ($\beta=10$, $\tau=0.2$, input magnification 1.4). With these parameters, simulated forward and reversal clusters switch between high and low states much as recorded neurons do, and the model's correlations, activity distributions, and dwell times match data. The paper further reports that gap junctions alone synchronize but do not switch, synapses alone switch but do not synchronize, and both are needed together; and that fitted synaptic signs make ASH and AWC reversal-promoting, ASE mildly forward-promoting, and OLQ strongly forward-promoting, consistent with their known roles in avoidance and chemotaxis.

Load-bearing premise

The model's conclusions depend on the assumption that the synaptic strengths and signs estimated by regression from calcium imaging—which connections excite and which inhibit—are true of the real worm, even though the connectome records no signs and published studies disagree on several of them.

Editorial extensions

If this is right

  • With one parameter set, the model reproduces forward/reversal switching across many imaging datasets, so the same circuit equations can serve as a common substrate for studying different behavioral episodes.
  • Removing either gap junctions or synapses in the model destroys the realistic switching, indicating that both connection types are jointly necessary for the emergent switching dynamics.
  • The model predicts that activating ASH or AWC sensory neurons lengthens and increases reversals, while activating ASE or OLQ lengthens forward runs, giving experimentally testable predictions about single-neuron effects on behavior.
  • Simulated locomotory paths built from the model's switching sequences distinguish dwelling-like from roaming-like datasets, suggesting behavioral state can be read from the forward/reversal cluster dynamics.
  • The driven core-network design can be exported to other circuits or organisms that have connectomic and large-scale imaging data, providing an analyzable middle ground between whole-connectome simulation and phenomenological models.

Reading between the lines

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

  • The regression-derived synaptic polarities are model outputs, not established biology: the connectome does not record signs, and published studies disagree on several connections (e.g., RIM to AVA is inhibitory here but excitatory in a gene-expression-based prediction). If those signs are wrong, the specific mechanistic story may not transfer, even though the paper notes the switching behavior is
  • The model's success suggests that the apparent randomness of C. elegans switching may be deterministic in origin—bistable recurrent dynamics driven by fluctuating inputs—rather than requiring intrinsic noise as in Markov-chain models.
  • The OLQ prediction is likely an oversimplification: real OLQ also mediates head withdrawal and nose-touch avoidance, so a single forward-promoting pathway may fail under optogenetic activation, pointing to parallel pathways the current core network omits.
  • Treating signal neurons as fixed inputs ignores core-to-signal feedback and the omitted PVC/DVA and motor-neuron connections; adding them could change switching statistics and is a natural next test of the driven-system approximation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper presents a data-driven biophysical network model of the C. elegans premotor circuit. A core group of 15 neurons is described by a driven dynamical system (Eq. 2) with intrinsic cubic dynamics fit to voltage-clamp data, gap-junction weights taken from the connectome, and synaptic weights and biases estimated by linear regression on 22 whole-brain calcium imaging datasets. The model is validated by comparing simulated premotor activity and the resulting forward/reversal behavioral sequence to the same type of recordings, and the authors report reproduction of stochastic switching, higher-order statistics, and dwelling versus roaming behavior. The model is then used to propose distinct roles for gap junctions versus synapses and to investigate how activating specific sensory neurons affects simulated behavior.

Significance. If the central claims are upheld, the model would be a notable contribution: a biophysical, minimally parameterized network model that produces complex, non-limit-cycle switching dynamics, with parameters anchored in connectomic and imaging data rather than tuned ad hoc. The paper also offers a clear mechanistic hypothesis about the division of labor between gap junctions and synapses, and it makes its code publicly available, which aids reproducibility. However, the lack of a demonstrable train/test split and the direct dependence of the behavioral 'predictions' on fitted parameters currently prevent the abstract's 'reproduces' and 'correctly predicts' claims from being fully supported. The modeling framework and analyses are promising, but the evidence presented is not yet sufficient to establish that the model captures the underlying dynamics independently of the fitting procedure.

major comments (4)
  1. [Methods: Parameter fit; Premotor network simulation; Results: Model validation] The validation is in-sample. The same datasets used to estimate A_ij, d_i, beta, tau, and the 1.4x magnification factor are used in Figures 3 and 11 to demonstrate agreement with data. The Methods state that beta is chosen by a parameter sweep over the 22 datasets, that A and d are averages of regressions over these same 22 datasets, and that tau and the magnification factor are fit by simulating the datasets shown in Figure 11. Since the signal-neuron inputs in the simulations are also taken from the same recordings, the resemblance in Figures 3 and 11 is expected to some degree by construction. Please report explicitly which datasets (by filename) are used for fitting versus validation, and provide an out-of-sample evaluation (e.g., leave-one-dataset-out or a reserved test set) before claiming that the model reproduces the observed dynamics.
  2. [Results: Manipulation of sensory inputs; Abstract] The sensory-neuron 'predictions' are direct readouts of the fitted synaptic weights, not independent predictions. The paper states that ASH and AWC have excitatory synapses with the reversal cluster (Fig. 2(b)) and then finds, unsurprisingly, that activating them promotes reversal; the ASE and OLQ results likewise follow from fitted weights and the connectome structure. Because A_ij are estimated by regression on the same whole-brain imaging data that are later used for validation, calling these results 'predictions' overstates their evidentiary value. The dwelling versus roaming demonstration uses datasets 2023-01-09-22 and 2023-01-09-15, which are not identified as held out from the fitting procedure; the authors should either show these are held out or qualify the abstract's 'correctly predicts behavior' claim.
  3. [Discussion, Fig. 7; Results: Gap junctions versus synaptic connections] The mechanistic conclusions, including the distinct roles of gap junctions and synaptic connections, rest on the regression-derived signs of the synaptic weights. The Discussion documents that several sign assignments conflict with previous studies (e.g., RIM to AVA is inhibitory here and in Ref. [22] but excitatory in Ref. [5]). The paper asserts that the robustness of switching behavior suggests these discrepancies are not consequential, but no sensitivity analysis is provided. A perturbation analysis (e.g., flipping signs of a subset of fitted weights, or refitting with different sign priors) is needed to establish that the central switching result and the gap-junction/synapse dichotomy are robust to the sign uncertainty acknowledged in the manuscript.
  4. [Results: Model validation, Fig. 3] The quantitative comparisons are informal. While the correlations in Fig. 3(c) look similar, there are no error bars or statistical tests for the correlations, probability distributions (Fig. 3(d)), or dwell-time distributions (Fig. 3(e)). The text notes that simulated dwell times are slightly longer, but no confidence intervals or effect sizes are given. To support the abstract's 'closely resembles' wording, the paper should quantify the agreement over datasets (e.g., summary statistics, permutation or bootstrap tests) rather than relying on visual comparison.
minor comments (4)
  1. [Throughout] There are several typos and grammatical slips, e.g., 'C. elegan' for 'C. elegans' in the Introduction, and 'The whole-brain imaging data [19, 1] connects neuronal activity to behavior' with a subject-verb mismatch. A careful proofreading pass is recommended.
  2. [Figure 10 caption] The caption states 'Because the system is receiving partial signals we also magnify A by 1.4x. Increasing the amount of stimulus to the premotor neurons increases their average activity levels.' This is ambiguous: it should specify that the magnification factor is applied to the synaptic input term only and that this factor was selected to bring the simulated activity to the correct level, as described in the Methods.
  3. [Methods: Parameter fit] The criteria for selecting the 22 datasets are given, but the actual list of dataset filenames is not. To allow reproducibility and to make the train/test distinction transparent, a supplementary table listing the datasets used for fitting, and those used for validation (especially in Figures 3, 5, and 11), should be provided.
  4. [Methods: Whole-brain imaging datasets] The paper uses 'GCamp z-score' without defining the normalization precisely. It would be helpful to state whether the z-score is computed per neuron, per dataset, or globally, since this affects the interpretation of the state variables and the thresholds used for behavioral classification.

Circularity Check

2 steps flagged · score 6.0 of 10

The 'predictions' — ASH/AWC reversal promotion and dwelling-vs-roaming — are readouts of parameters fit to the same datasets they are claimed to predict; the model's reproduction claim is in-sample.

  1. fitted input called prediction [Results, 'Manipulation of sensory inputs' (Figure 6), page 12]
    "Consistent with the experimental literature, our regression results show that ASH and AWC have excitatory synapses with the reversal cluster (Fig. 2(b)), suggesting that they should be reversal-promoting. Unsurprisingly, when we activate either of these two neurons in our simulation, reversals become more frequent and prolonged compared to the unperturbed simulation (Fig. 6(b))."

    The synaptic weights A_ij — including the ASH/AWC-to-reversal signs — are estimated by linear regression on whole-brain imaging data (Methods Eq. 4: 'we perform linear regressions for the 22 datasets to approximate A and d'). The 'prediction' that activating ASH or AWC promotes reversal is therefore a direct readout of the fitted sign of A_ij: a positive fitted weight from a signal neuron to the reversal cluster guarantees additional ReLU input to that cluster when the neuron is pulsed. The simulation result is implied by the fitted parameter, not an independent model prediction; the paper itself labels it 'Unsurprisingly.' Comparing it to the experimental literature is a consistency check, not a test of predictive content.

  2. fitted input called prediction [Abstract; Results, 'Behavior over longer time durations: roaming vs dwelling', page 11; Methods, 'Parameter fit']
    "The model correctly predicts behavior such as dwelling versus roaming as a result of the synaptic inputs received ... We determine β, A, and d simultaneously by performing multiple linear regressions to approximate A and d for different values of β across 22 datasets selected from Ref. [1] ... We fit τ by simulating the activity of the core neurons in the six datasets shown in Figure 11 for variable τ values."

    The dwelling/roaming contrast in Figure 5 is read off forward/reversal switching durations obtained from two Ref. [1] datasets, the same source used to estimate A_ij, d_i, β, τ, and the magnification factor. The Methods state that all parameters are fit on Ref. [1] data ('across 22 datasets selected from Ref. [1]'; τ and magnification by simulating 'the six datasets shown in Figure 11'), and the paper never states that the Figure 5 datasets were reserved for validation. Hence 'predicts dwelling versus roaming' is an in-sample reproduction of switching statistics drawn from the fitting corpus; the behavioral outcome is a relabeling of the fitted forward/reversal durations rather than an independent prediction.

full rationale

The core dynamical system is not circular by definition: the connectome-derived gap junction weights W_ij, the cubic intrinsic dynamics fitted to voltage-clamp data, and the choice of core/signal neurons are external inputs, and the paper's self-citations (Morrison et al. 2021, Morrison & Young 2022) are contextual rather than load-bearing. However, two 'predictions' reduce to the fitted parameters. First, the ASH/AWC reversal-promotion result is a direct readout of the sign of the regression-fitted synaptic weights A_ij; the paper acknowledges this by calling it 'unsurprising.' Second, the claimed prediction of dwelling versus roaming is made on datasets from the same Ref. [1] corpus used to fit A_ij, d_i, β, τ, and the 1.4x magnification factor, with no held-out subset identified; the behavioral distinction is a relabeling of the forward/reversal switching durations that the fitting procedure was designed to reproduce. The 'reproduces' claim for switching dynamics is thus a goodness-of-fit demonstration rather than an independent confirmation, and the abstract's stronger 'correctly predicts' wording overstates the evidence. The model retains independent content in its structural ablations (gap junctions vs synapses), which are not circular, so the overall circularity is partial.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central modeling claim rests on a large set of parameters fitted to the same whole-brain datasets used for validation. The synaptic weight matrix A, biases d, global coupling beta, timescale tau, and input magnification factor are all determined from the data, so the model's output is not an independent prediction of those datasets. The modeling assumptions (cubic intrinsic dynamics, linear gap junction coupling, ReLU synaptic input, neglect of feedback) are stated but not all independently validated.

free parameters (5)
  • beta (global gap junction scaling) = 10
    Chosen by a parameter sweep over 22 datasets minimizing a hybrid regression and derivative error (Fig. 9a). Influences the balance between gap junction and synaptic coupling.
  • tau (timescale) = 0.2
    Fit so that simulated core neurons switch at the same speed as real neurons across the datasets in Fig. 11.
  • Magnification factor for synaptic input = 1.4
    Introduced to compensate for missing signal neuron input; chosen to bring simulated core neurons to the correct activity level.
  • Synaptic weight matrix A_ij = many values (signs and weights shown in Fig. 2b)
    Estimated by linear regression from 22 datasets; entries are averaged across datasets. These weights directly determine the model's synaptic influences and the sensory neuron 'predictions'.
  • Bias terms d_i (15 neurons) = not listed individually
    Estimated by linear regression alongside A_ij; shifts the intrinsic cubic dynamics of each core neuron.
assumptions (6)
  • domain assumption All core neurons share the same cubic intrinsic dynamics f(x) = -2(x+0.8)(x-0.1)(x-1) + d_i, with fixed points at x=-0.8, 0.1, and 1.
    The cubic is inferred from voltage clamp data of RMD and AWCON neurons, not from each core neuron. The fixed points are set from the distribution of premotor activity (Fig. 8). Used in Eq. 2/3.
  • domain assumption Gap junction influence is proportional to voltage difference (x_j - x_i) with weights W from the connectome, scaled by a single global beta.
    Linear electrical coupling is a standard model, but the global scaling beta and the use of connectome weights as relative conductances is an assumption not directly validated.
  • domain assumption Synaptic input is linear in the rectified presynaptic activity: A_ij sigma(x_j) with sigma = ReLU.
    ReLU captures the assumption that hyperpolarized neurons do not release transmitter, but real synapses are nonlinear and sign is ambiguous for complex synapses containing both excitatory and inhibitory receptors.
  • domain assumption Signal neuron dynamics are taken directly from whole-brain imaging data and recurrent core-to-signal feedback is neglected.
    The model treats the premotor network as a driven system. The paper acknowledges omitting core-to-signal feedback and motor neuron gap junctions.
  • domain assumption The 22 selected datasets are representative, and missing neuron traces can be replaced by highly correlated proxies (at least 70% correlation).
    Data replacement and dataset selection may bias parameter estimates but are necessary due to sparse labeling.
  • ad hoc to paper Behavior is inferred by thresholding F(t)-R(t) at plus or minus 0.5.
    The threshold is chosen to define forward, reversal, and pause states; results could depend on this choice.

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Cite this review

Pith. "Pith review of A data-driven biophysical network model reproduces C. elegans premotor neural dynamics." pith.science (2026). https://pith.science/paper/OCYQPO3D

@misc{pith2026250100278,
  author       = {Pith},
  title        = {Pith review of: A data-driven biophysical network model reproduces C. elegans premotor neural dynamics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OCYQPO3D}},
  note         = {Machine review of arXiv:2501.00278}
}
read the original abstract

C. elegans locomotion is composed of switches between forward and reversal states punctuated by turns. This locomotory capability is necessary for the nematode to move towards attractive stimuli, escape noxious chemicals, and explore its environment. Although experimentalists have identified a number of premotor neurons as drivers of forward and reverse motion, how these neurons work together to produce the behaviors observed remains to be understood. Towards a better understanding of C. elegans neurodynamics, we present in this paper a minimally parameterized, biophysical dynamical systems model of the premotor network. Our model consists of a recurrently connected collection of premotor neurons (the core group) driven by over a hundred sensory and interneurons that provide diverse feedforward inputs to the core group. It is data-driven in the sense that the choice of neurons in the core group follows experimental guidance, anatomical structures are dictated by the connectome, and physiological parameters are deduced from whole-brain imaging and voltage clamps data. When simulated with realistic input signals, our model produces premotor activity that closely resembles experimental data: from the seemingly random switching between forward and reversal behaviors to the synchronization of subnetworks to various higher-order statistics. We posit that different roles are played by gap junctions and synaptic connections in switching dynamics. The model correctly predicts behavior such as dwelling versus roaming as a result of the synaptic inputs received, and we demonstrate that it can be used to study how the activity level of certain individual neurons impacts behavior.

Figures

Figures reproduced from arXiv: 2501.00278 by the authors.

Figure 1
Figure 1. (a) Partial connectome from [47] containing 198 sensory neurons, interneurons, and motor neurons (most [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. (a) Relative gap junction weights between core neurons and between signal neurons and core neurons (select [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Comparison of data and model. (a) Core neuron activity in the dataset versus the simulation. (b) The [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Forward and reversal cluster dynamics using connected neurons as signals. (a) Forward neuron activity from [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: (a) Core neuron activity in dataset 2023-01-09-22 [1] and resulting locomotory state time series. (b) Three [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: (a) In each numerical experiment, a Heaviside pulse time series replaces the dataset time series for a sensory [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Polarities obtained from regression compared to polarities determined from previous work [5, 34, 33, 22]. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: (a) Voltage clamp experiments from Ref. [12]. This figure has been reproduced with permission [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: (a) Average hybrid error as a function of [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Dataset compared to simulations with different timescales and magnification factors. Decreasing the [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: time series of core neurons in whole-brain imaging data compared to simulation. Behavior is measured as [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]

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Works this paper leans on

51 extracted references · 51 canonical work pages

  1. [22]

    A recurrent neural network model of C

    Enrico Lanza, Silvia Di Angelantonio, Giorgio Gosti, Giancarlo Ruocco, and Viola Folli. A recurrent neural network model of C. elegans responses to aversive stimuli. Neurocomputing, 430:1–13, March 2021

    work page 2021

  2. [5]

    Fenyves, Gábor S

    Bánk G. Fenyves, Gábor S. Szilágyi, Zsolt Vassy, Csaba S ˝oti, and Peter Csermely. Synaptic polarity and sign- balance prediction using gene expression data in theCaenorhabditis elegans chemical synapse neuronal connec- tome network. PLOS Computational Biology, 16(12):e1007974, December 2020

    work page 2020

  3. [1]

    Atanas, Jungsoo Kim, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Talya S

    Adam A. Atanas, Jungsoo Kim, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Talya S. Kramer, Flossie K. Wan, Saba Baskoylu, Ugur Dag, Elpiniki Kalogeropoulou, Matthew A. Gomes, Cassi Estrem, Netta Cohen, Vikash K. Mansinghka, and Steven W. Flavell. Brain-wide representations of behavior spanning multiple timescales and states in C. elegans. ...

    work page 2023

  4. [2]

    Bargmann

    Cornelia I. Bargmann. Chemosensation in C. elegans. In WormBook: The Online Review of C. Elegans Biology [Internet]. WormBook, October 2006

    work page 2006

  5. [3]

    Bargmann and H

    Cornelia I. Bargmann and H. Robert Horvitz. Chemosensory neurons with overlapping functions direct chemo- taxis to multiple chemicals in C. elegans. Neuron, 7(5):729–742, November 1991

    work page 1991

  6. [4]

    Sulston, John G

    Martin Chalfie, John E. Sulston, John G. White, Eileen Southgate, J. Nichol Thomson, and Sydney Brenner. The neural circuit for touch sensitivity in Caenorhabditis elegans. The Journal of Neuroscience, 5(4):956–964, April 1985

    work page 1985

  7. [6]

    Nathan Kutz

    Charles Fieseler, Manuel Zimmer, and J. Nathan Kutz. Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings. Journal of The Royal Society Interface , 17(173):20200459, December 2020

    work page 2020

  8. [7]

    Impulses and physiological states in theoretical models of nerve membrane

    Richard FitzHugh. Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal, 1(6):445–466, July 1961

    work page 1961

Show all 51 references
  1. [8]

    Flavell, Navin Pokala, Evan Z

    Steven W. Flavell, Navin Pokala, Evan Z. Macosko, Dirk R. Albrecht, Johannes Larsch, and Cornelia I. Bargmann. Serotonin and the neuropeptide pdf initiate and extend opposing behavioral states in C. elegans. Cell, 154(5):1023–1035, August 2013

    2013

  2. [9]

    Flavell, David M

    Steven W. Flavell, David M. Raizen, and Young-Jai You. Behavioral states. Genetics, 216(2):315–332, October 2020

    2020

  3. [10]

    Fouad, Jun Meng, Taizo Kawano, Yung-Chi Huang, Yi Li, Salvador Alcaire, Wesley Hung, Yangning Lu, Yingchuan Billy Qi, Yishi Jin, Mark Alkema, Christopher Fang- Yen, and Mei Zhen

    Shangbang Gao, Sihui Asuka Guan, Anthony D. Fouad, Jun Meng, Taizo Kawano, Yung-Chi Huang, Yi Li, Salvador Alcaire, Wesley Hung, Yangning Lu, Yingchuan Billy Qi, Yishi Jin, Mark Alkema, Christopher Fang- Yen, and Mei Zhen. Excitatory motor neurons are local oscillators for bac...

    2018

  4. [11]

    Padraig Gleeson, David Lung, Radu Grosu, Ramin Hasani, and Stephen D. Larson. C302: A multiscale frame- work for modelling the nervous system of Caenorhabditis elegans. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1758):20170379, September 2018

    2018

  5. [12]

    Goodman, David H

    Miriam B. Goodman, David H. Hall, Leon Avery, and Shawn R. Lockery. Active currents regulate sensitivity and dynamic range in C. elegans neurons. Neuron, 20(4):763–772, April 1998

    1998

  6. [13]

    Goodman and Piali Sengupta

    Miriam B. Goodman and Piali Sengupta. How Caenorhabditis elegans senses mechanical stress, temperature, and other physical stimuli. Genetics, 212(1):25–51, May 2019

    2019

  7. [14]

    Gray, Joseph J

    Jesse M. Gray, Joseph J. Hill, and Cornelia I. Bargmann. A circuit for navigation in Caenorhabditis elegans. Proceedings of the National Academy of Sciences, 102(9):3184–3191, March 2005

    2005

  8. [15]

    J. L. Hindmarsh, R. M. Rose, and Andrew Fielding Huxley. A model of neuronal bursting using three coupled first order differential equations. Proceedings of the Royal Society of London. Series B. Biological Sciences , 221(1222):87–102, January 1997

    1997

  9. [16]

    Concerted pulsatile and graded neural dynamics enables efficient chemotaxis in C

    Eyal Itskovits, Rotem Ruach, Alexander Kazakov, and Alon Zaslaver. Concerted pulsatile and graded neural dynamics enables efficient chemotaxis in C. elegans. Nature Communications, 9(1):2866, July 2018

    2018

  10. [17]

    Kaplan, Annika L.A

    Harris S. Kaplan, Annika L.A. Nichols, and Manuel Zimmer. Sensorimotor integration in Caenorhabditis el- egans: A reappraisal towards dynamic and distributed computations. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1758):20170371, October 2018

    2018

  11. [18]

    Kaplan and H

    Joshua M. Kaplan and H. Robert Horvitz. A dual mechanosensory and chemosensory neuron in Caenorhabditis elegans. Proceedings of the National Academy of Sciences, 90(6):2227–2231, March 1993

    1993

  12. [19]

    Kaplan, Tina Schrödel, Susanne Skora, Theodore H

    Saul Kato, Harris S. Kaplan, Tina Schrödel, Susanne Skora, Theodore H. Lindsay, Eviatar Yemini, Shawn Lock- ery, and Manuel Zimmer. Global brain dynamics embed the motor command sequence of Caenorhabditis ele- gans. Cell, 163(3):656–669, October 2015

    2015

  13. [20]

    Neural interactome: Interactive simulation of a neuronal system

    Jimin Kim, William Leahy, and Eli Shlizerman. Neural interactome: Interactive simulation of a neuronal system. Frontiers in Computational Neuroscience, 13, 2019

    2019

  14. [21]

    Kunert, Joshua L

    James M. Kunert, Joshua L. Proctor, Steven L. Brunton, and J. Nathan Kutz. Spatiotemporal feedback and network structure drive and encode Caenorhabditis elegans locomotion. PLOS Computational Biology , 13(1):e1005303, January 2017

    2017

  15. [23]

    Linderman, Aravinthan D

    Albert Lin, Daniel Witvliet, Luis Hernandez-Nunez, Scott W. Linderman, Aravinthan D. T. Samuel, and Vivek Venkatachalam. Imaging whole-brain activity to understand behaviour. Nature Reviews Physics, 4(5):292–305, May 2022

    2022

  16. [24]

    Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C

    Scott Linderman, Annika Nichols, David Blei, Manuel Zimmer, and Liam Paninski. Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans. Preprint, Neuroscience, April 2019

    2019

  17. [25]

    Lockery and Miriam B

    Shawn R. Lockery and Miriam B. Goodman. The quest for action potentials in C. elegans neurons hits a plateau. Nature neuroscience, 12(4):377–378, April 2009

    2009

  18. [26]

    McCulloch and Walter Pitts

    Warren S. McCulloch and Walter Pitts. A logical calculus of the ideas immanent in nervous activity.The Bulletin of Mathematical Biophysics, 5(4):115–133, 1943

    1943

  19. [27]

    Mellem, Penelope J

    Jerry E. Mellem, Penelope J. Brockie, David M. Madsen, and Andres V . Maricq. Action potentials contribute to neuronal signaling in C. elegans. Nature Neuroscience, 11(8):865–867, August 2008

    2008

  20. [28]

    Nathan Kutz

    Megan Morrison, Charles Fieseler, and J. Nathan Kutz. Nonlinear control in the nematode C. elegans. Frontiers in Computational Neuroscience, 14, 2021. 23

    2021

  21. [29]

    Chaotic heteroclinic networks as models of switching behavior in biolog- ical systems

    Megan Morrison and Lai-Sang Young. Chaotic heteroclinic networks as models of switching behavior in biolog- ical systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(12):123102, December 2022

    2022

  22. [30]

    Nagumo, S

    J. Nagumo, S. Arimoto, and S. Yoshizawa. An active pulse transmission line simulating nerve axon.Proceedings of the IRE, 50(10):2061–2070, October 1962

    1962

  23. [31]

    Annika L. A. Nichols, Tomáš Eichler, Richard Latham, and Manuel Zimmer. A global brain state underlies C. elegans sleep behavior. Science, 356(6344), June 2017

    2017

  24. [32]

    Biophysical modeling of C

    Martina Nicoletti, Alessandro Loppini, Letizia Chiodo, Viola Folli, Giancarlo Ruocco, and Simonetta Filippi. Biophysical modeling of C. elegans neurons: Single ion currents and whole-cell dynamics of AWCon and RMD. PLOS ONE, 14(7):e0218738, July 2019

    2019

  25. [33]

    Piggott, Jie Liu, Zhaoyang Feng, Seth A

    Beverly J. Piggott, Jie Liu, Zhaoyang Feng, Seth A. Wescott, and X. Z. Shawn Xu. The neural circuits and synaptic mechanisms underlying motor initiation in C. elegans. Cell, 147(4):922–933, November 2011

    2011

  26. [34]

    The neuroethology of C

    Jennifer K Pirri and Mark J Alkema. The neuroethology of C. elegans escape. Current Opinion in Neurobiology, 22(2):187–193, April 2012

    2012

  27. [35]

    Optimal synaptic signaling connectome for locomotory behavior in Caenorhabditis elegans: Design minimizing energy cost

    Franciszek Rakowski and Jan Karbowski. Optimal synaptic signaling connectome for locomotory behavior in Caenorhabditis elegans: Design minimizing energy cost. PLoS Computational Biology , 13(11):e1005834, November 2017

    2017

  28. [36]

    Synaptic polarity of the interneuron circuit controlling C

    Franciszek Rakowski, Jagan Srinivasan, Paul Sternberg, and Jan Karbowski. Synaptic polarity of the interneuron circuit controlling C. elegans locomotion. Frontiers in Computational Neuroscience, 7, 2013

    2013

  29. [37]

    Riddle, Thomas Blumenthal, Barbara J

    Donald L. Riddle, Thomas Blumenthal, Barbara J. Meyer, and James R. Priess. Mechanosensory control of locomotion. In C. Elegans II. 2nd Edition. Cold Spring Harbor Laboratory Press, 1997

    1997

  30. [38]

    Roberts, Steven B

    William M. Roberts, Steven B. Augustine, Kristy J. Lawton, Theodore H. Lindsay, Tod R. Thiele, Eduardo J. Izquierdo, Serge Faumont, Rebecca A. Lindsay, Matthew Cale Britton, Navin Pokala, Cornelia I. Bargmann, and Shawn R. Lockery. A stochastic neuronal model predicts random s...

    2016

  31. [39]

    Sabrin, Yongbin Wei, Martijn Pieter van den Heuvel, and Constantine Dovrolis

    Kaeser M. Sabrin, Yongbin Wei, Martijn Pieter van den Heuvel, and Constantine Dovrolis. The hourglass orga- nization of the Caenorhabditis elegans connectome. PLOS Computational Biology, 16(2):e1007526, February 2020

    2020

  32. [40]

    Sensing of cadmium and copper ions by externally exposed ADL, ASE, and ASH neurons elicits avoidance response in Caenorhabditis elegans

    Yoshihiro Sambongi, Takashi Nagae, Yanna Liu, Takao Yoshimizu, Kenji Takeda, Yoh Wada, and Masamitsu Futai. Sensing of cadmium and copper ions by externally exposed ADL, ASE, and ASH neurons elicits avoidance response in Caenorhabditis elegans. NeuroReport, 10(4):753, March 1999

    1999

  33. [41]

    Sattelle and Steven D

    David B. Sattelle and Steven D. Buckingham. Invertebrate studies and their ongoing contributions to neuro- science. Invertebrate Neuroscience, 6(1):1–3, March 2006

    2006

  34. [42]

    Piali Sengupta and Aravinthan D.T. Samuel. Caenorhabditis Elegans: A model system for systems neuroscience. Current Opinion in Neurobiology, 19(6):637–643, December 2009

    2009

  35. [43]

    Thiele, Serge Faumont, Marina Ezcurra, Shawn R

    Hiroshi Suzuki, Tod R. Thiele, Serge Faumont, Marina Ezcurra, Shawn R. Lockery, and William R. Schafer. Functional asymmetry in Caenorhabditis elegans taste neurons and its computational role in chemotaxis.Nature, 454(7200):114–117, July 2008

    2008

  36. [44]

    A set of hub neurons and non-local connectivity features support global brain dynamics in C

    Kerem Uzel, Saul Kato, and Manuel Zimmer. A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Current Biology, 32(16):3443–3459.e8, August 2022

    2022

  37. [45]

    Neurons regulating the duration of forward locomotion in Caenorhabditis elegans

    Tokumitsu Wakabayashi, Izumi Kitagawa, and Ryuzo Shingai. Neurons regulating the duration of forward locomotion in Caenorhabditis elegans. Neuroscience Research, 50(1):103–111, September 2004. 24

    2004

  38. [46]

    Po, Elizabeth Hulme, Sway Chen, Xinyu Liu, Sen Wai Kwok, Marc Gershow, Andrew M

    Quan Wen, Michelle D. Po, Elizabeth Hulme, Sway Chen, Xinyu Liu, Sen Wai Kwok, Marc Gershow, Andrew M. Leifer, Victoria Butler, Christopher Fang-Yen, Taizo Kawano, William R. Schafer, George Whitesides, Matthieu Wyart, Dmitri B. Chklovskii, Mei Zhen, and Aravinthan D. T. Samue...

    2012

  39. [47]

    John Graham White, Eileen Southgate, J. N. Thomson, and Sydney Brenner. The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 314(1165):1–340, November 1986

    1986

  40. [48]

    Wicks and Catharine H

    Stephen R. Wicks and Catharine H. Rankin. Integration of mechanosensory stimuli in Caenorhabditis elegans. The Journal of Neuroscience, 15(3):2434–2444, March 1995

    1995

  41. [49]

    Beibei Zhao, Parul Khare, Lisa Feldman, and Joseph A. Dent. Reversal frequency in Caenorhabditis elegans represents an integrated response to the state of the animal and its environment. The Journal of Neuroscience, 23(12):5319–5328, June 2003

    2003

  42. [50]

    Mei Zhen and Aravinthan D.T. Samuel. C. elegans locomotion: Small circuits, complex functions. Current Opinion in Neurobiology, 33:117–126, August 2015

    2015

  43. [51]

    Brockie, Jerry E

    Yi Zheng, Penelope J. Brockie, Jerry E. Mellem, David M. Madsen, and Andres V . Maricq. Neuronal control of locomotion in C. elegans is modified by a dominant mutation in the GLR-1 ionotropic glutamate receptor. Neuron, 24(2):347–361, October 1999. 25

    1999

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.