## Reviews:
- Izquierdo & Beer 2016, "The whole worm: brain-body-environment models of C. elegans", https://pubmed.ncbi.nlm.nih.gov/27336738/
> nice commented reference list at the end, with focus on their (Beer) work on modeling klinotaxix
- Gjorgjieva et al 2014, "Neurobiology of C. elegans Locomotion: where do we stand?", https://academic.oup.com/bioscience/article/64/6/476/289633
> discusses open questions, e.g. about the competing hypotheses for the production of locomotor patterns (Figure 3)
- Zhen & Samuel, "C. elegans locomotion: small circuits, complex functions", https://www.sciencedirect.com/science/article/pii/S0959438815000641
> current biological knowledge on neuromuscular signalling and biomechanics of movement, by our collaborator Mei Zhen
- Cohen & Sanders (2014), "Nematode locomotion: dissecting the neuronal-environmental loop", https://pubmed.ncbi.nlm.nih.gov/24709607/
> review by Netta Cohen, with focus on her group's work

## Model papers
- Niebur and Erdös (1991), "Theory of the locomotion of nematodes", https://linkinghub.elsevier.com/retrieve/pii/S000634959182149X
> first combined model including biomechanics and forces, using empirical information of worm behaviour
- Bryden & Cohen (2008), "Neural control of Caenorhabditis elegans forward locomotion: the role of sensory feedback", https://link.springer.com/content/pdf/10.1007/s00422-008-0212-6.pdf
> minimal circuit model of forward locomotion, including feedback by stretch receptors, but without a central pattern generator as in Niebur & Erdös.
- Sakamoto et al (2021), "Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation", https://www.nature.com/articles/s41598-021-92690-2
> Learning the weights in the connectome model by machine learning (backpropagation) to explain observed behaviour. Interesting approach, but it is not

https://arxiv.org/pdf/2006.10122.pdf
* > different approach from 2021, but no model for worm-environment interactions

## OpenWorm Papers
- Palyanov et al. (2018), "Three-dimensional simulation of the Caenorhabditis elegans body and muscle cells in liquid and gel environments for behavioural analysis", https://royalsocietypublishing.org/doi/full/10.1098/rstb.2017.0376
> biomechanics model, code available
- Gleeson et al. (2018), "C302: A multiscale framework for modelling the nervous system of Caenorhabditis elegans", https://doi.org/10.1098/rstb.2017.0379
> neural network model framework, code available
- and others...

## Connectome simulations / theory
- Kim et al. (2019), "Neural Interactome: Interactive Simulation of a Neuronal System", https://www.frontiersin.org/articles/10.3389/fncom.2019.00008/full#h6
> real-time simulation of the C. elegans connectome, stimuli can be applied (e.g. touch) and the network modified, but no biomechanical model. Python code available.
- Yan et al. (2017), "Network control principles predict neuron function in the Caenorhabditis elegans connectome", https://www.nature.com/articles/nature24056
> Apply network control theory to predict neuronal groups involved in locomotion. Example of application of network model based on the connectome. No biomechanics. More detailed FAQ: https://doi.org/10.1098/rstb.2017.0372

## Implementations
- Boyle et al. (2012), "Adaptive Undulatory Locomotion of a C. elegans Inspired Robot", https://ieeexplore.ieee.org/abstract/document/6272363
> robot implementation of the model by Cohen, based on proprioceptive feedback
- Petrushin et al (2016): "The Si elegans project at the interface of
experimental and computational C. elegans neurobiology and
behavior",
https://iopscience.iop.org/article/10.1088/1741-2560/13/6/065001/pdf
> Overview of the Si elegans project, aiming to build a FPGA implementation of the C.elegans nervous system, contains a brief overview of past work on page 3/4.
- Coggan et al. (2018), "A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble", https://www.frontiersin.org/articles/10.3389/fnins.2018.00664/full
> Outline of a possible workflow from image segmentation of EM data, connectome extraction, interactive visualization and simulation of network dynamics