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Model of the Xenopus tadpole swimming spinal network (Roberts et al. 2014)

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Accession:238332
This is a NEURON-python and MATLAB simulation code for generating anatomical or probabilistic connectivity and simulating the neuronal dynamics of the neuronal network controlling swimming in Xenopus tadpoles. For more details about this model, see Ferrario et al, 2018, eLife and Roberts et al, 2014, J of Neurosci
References:
1 . Ferrario A, Merrison-Hort R, Soffe SR, Borisyuk R (2018) Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network. Elife [PubMed]
2 . Roberts A, Conte D, Hull M, Merrison-Hort R, al Azad AK, Buhl E, Borisyuk R, Soffe SR (2014) Can simple rules control development of a pioneer vertebrate neuronal network generating behavior? J Neurosci 34:608-21 [PubMed]
3 . Borisyuk R, Al Azad AK, Conte D, Roberts A, Soffe SR (2014) A developmental approach to predicting neuronal connectivity from small biological datasets: a gradient-based neuron growth model. PLoS One 9:e89461 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Axon;
Brain Region(s)/Organism:
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s): I_Ks; I Potassium; I Calcium;
Gap Junctions: Gap junctions;
Receptor(s): AMPA; Gaba; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: MATLAB; NEURON;
Model Concept(s): Activity Patterns; Connectivity matrix; Oscillations;
Implementer(s): Ferrario, Andrea [andrea.ferrario at plymouth.ac.uk]; Borisyuk, Roman [rborisyuk at plymouth.ac.uk]; Merrison-Hort, Robert ;
Search NeuronDB for information about:  AMPA; NMDA; Gaba; I Calcium; I Potassium; I_Ks; Gaba; Glutamate; 
# tadpole-spinal-cord-models

This is the source code for three compuational models used for generating connectivity and swimming functionality of spinal cord neurons in the Xenopus 
tadpoles using biological data. 

1. The first model uses biological data to reconstruct a complete map of synaptic interactions between spinal neuron (anatomical 
connectome), in the folder "anatomical model". 
2. The second model generalizes the anatomical model by averaging a number of anatomical connectomes and thus generating a matrix 
of connection probabilities between the neurons, in the folder "probabilistic model".
3. The third model uses the previous two to generate connectivity between neurons and simulates the dynamics of this circuit 
using Neuron (tested with version 7.3), in the folder "functional model".

A README file contained in the folder of each piece of the model explains how to run the simulations and give a description of the models. 
For a more complete description of these models have a look at (please cite, if you are using this model):

- Borisyuk et al., 2014, PLoS One
- Roberts et al., 2014, Journal of Neuroscience

For any problem contact me. 

Author: Andrea Ferrario, University of Plymouth
Last Update: 21/1/2018
Email: andrea.ferrario@plymouth.ac.uk

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