Crayfish hybrid simulation model (Bacque-Cazenave et al. 2014)

 Download zip file 
Help downloading and running models
Accession:150698
A neuromechanical model of the crayfish leg and thorax and the postural and locomotor circuitry built and run in AnimatLab v1. The model simulates experiments run with the BCI preparation model in which the model was linked in real time to the in vivo crayfish thoracic nerve cord. The model shows that current understanding of the neural circuitry can account for the increase in locomotor frequency when the sensori-motor feedback loop is intact.
Reference:
1 . Bacqué-Cazenave J, Chung B, Cofer DW, Cattaert D, Edwards DH (2015) The effect of sensory feedback on crayfish posture and locomotion: II. Neuromechanical simulation of closing the loop. J Neurophysiol 113:1772-83 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Crayfish motor neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Acetylcholine;
Simulation Environment: AnimatLab v1;
Model Concept(s): Posture and locomotion;
Implementer(s): Cofer, David [dcofer at neurorobotictech.com];
Search NeuronDB for information about:  Acetylcholine;
/
BacqueCazenaveEtAlsubmitted
Crayfish Body Parts
Angle and Length.atvf
ARINs.atvf
Circuit.atvf
Command.atvf *
Crayfish hybrid simulation model.aproj
Crayfish hybrid simulation model.asim
Crayfish.abef
Crayfish.abpe
Crayfish.absys
Crayfish.arnn
Crayfish.astl
DataTool_11.atvf
DataTool_2.atvf
DataTool_3.atvf
DataTool_4.atvf
DataTool_8.atvf
Depressor Set Up.atvf *
Distal Telson Rec Neurons.atvf *
Experiment Data.atvf
Fig 2.atvf
Fig 4.atvf
grass11.bmp *
gravel.bmp *
Ground.astl
Leg CB Angles.dat *
Leg5 Angles and Tensions.dat *
Movement.atvf
MSIs.atvf *
Open & Closed Loop.atvf
Organism_1.abef *
Organism_1.abpe *
Organism_1.absys *
Organism_1.astl *
Post Lev Set Up.atvf *
Prox Telson Rec Neurons.atvf *
thorax.ase *
thorax.mtl *
thorax.obj *
thorax_collision.mtl *
thorax_collision.obj *
thorax_flipped.bmp *
trial 1 CL.dtv
trial 2 CL.dtv
trial 3 CL.dtv
trial 4 CL.dtv
trial 5 CL.dtv
trial 6 CL.dtv
Water.astl
                            
<NervousSystem>
<NeuralModules>
<NeuralModule>
<ModuleName>RealisticNeuralNet</ModuleName>
<ModuleFileName>RealisticNeuralNet_vc7.dll</ModuleFileName>
<Type>RealisticNeuralModule</Type>
<NeuralNetFile>Crayfish.arnn</NeuralNetFile>
</NeuralModule>
</NeuralModules>
<Adapters>
<Adapter>
<Type>PhysicalToNode</Type>
<SourceBodyType>RigidBody</SourceBodyType>
<SourceBodyID>f209e845-c598-45b3-a963-56b3eb979213</SourceBodyID>
<SourceDataType>MuscleLength</SourceDataType>
<TargetModule>RealisticNeuralNet</TargetModule>
<TargetNodeID>18</TargetNodeID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>True</UseLimits>
<LowerLimit>0.004</LowerLimit>
<UpperLimit>0.006</UpperLimit>
<LowerOutput>0</LowerOutput>
<UpperOutput>6e-08</UpperOutput>
<A>0</A>
<B>0</B>
<C>3e-05</C>
<D>-1.2e-07</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>12</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>3df28b50-0060-4da4-9a37-d2af9326eca0</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>2</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>18ce8858-4e89-4413-bc37-4f9a67adf22c</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>11</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>07ded226-3cad-4002-a7a5-a81e0d0247f3</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>PhysicalToNode</Type>
<SourceBodyType>RigidBody</SourceBodyType>
<SourceBodyID>f209e845-c598-45b3-a963-56b3eb979213</SourceBodyID>
<SourceDataType>Vmuscle</SourceDataType>
<TargetModule>RealisticNeuralNet</TargetModule>
<TargetNodeID>15</TargetNodeID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>True</UseLimits>
<LowerLimit>-0.02</LowerLimit>
<UpperLimit>0</UpperLimit>
<LowerOutput>2e-08</LowerOutput>
<UpperOutput>0</UpperOutput>
<A>0</A>
<B>0</B>
<C>-1e-06</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>2</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>58185988-05fa-4c7c-b7e3-a76c9d5a56af</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>11</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>1cd6a46f-e2f0-4d4d-b61b-562fdeb56a56</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>PhysicalToNode</Type>
<SourceBodyType>RigidBody</SourceBodyType>
<SourceBodyID>f209e845-c598-45b3-a963-56b3eb979213</SourceBodyID>
<SourceDataType>Vmuscle</SourceDataType>
<TargetModule>RealisticNeuralNet</TargetModule>
<TargetNodeID>26</TargetNodeID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>True</UseLimits>
<LowerLimit>-0.02</LowerLimit>
<UpperLimit>0</UpperLimit>
<LowerOutput>2e-08</LowerOutput>
<UpperOutput>0</UpperOutput>
<A>0</A>
<B>0</B>
<C>-1e-06</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>PhysicalToNode</Type>
<SourceBodyType>RigidBody</SourceBodyType>
<SourceBodyID>f209e845-c598-45b3-a963-56b3eb979213</SourceBodyID>
<SourceDataType>Vmuscle</SourceDataType>
<TargetModule>RealisticNeuralNet</TargetModule>
<TargetNodeID>20</TargetNodeID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>True</UseLimits>
<LowerLimit>0</LowerLimit>
<UpperLimit>0.02</UpperLimit>
<LowerOutput>0</LowerOutput>
<UpperOutput>2e-08</UpperOutput>
<A>0</A>
<B>0</B>
<C>1e-06</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>PhysicalToNode</Type>
<SourceBodyType>RigidBody</SourceBodyType>
<SourceBodyID>f209e845-c598-45b3-a963-56b3eb979213</SourceBodyID>
<SourceDataType>Vmuscle</SourceDataType>
<TargetModule>RealisticNeuralNet</TargetModule>
<TargetNodeID>19</TargetNodeID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>True</UseLimits>
<LowerLimit>0</LowerLimit>
<UpperLimit>0.02</UpperLimit>
<LowerOutput>0</LowerOutput>
<UpperOutput>2e-08</UpperOutput>
<A>0</A>
<B>0</B>
<C>1e-06</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>17</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>d7b1e060-c25b-4883-8110-755571fd8312</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>12</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>70c6fd45-667c-4067-9eb7-3e946432a35e</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>PhysicalToNode</Type>
<SourceBodyType>RigidBody</SourceBodyType>
<SourceBodyID>f209e845-c598-45b3-a963-56b3eb979213</SourceBodyID>
<SourceDataType>MuscleLength</SourceDataType>
<TargetModule>RealisticNeuralNet</TargetModule>
<TargetNodeID>24</TargetNodeID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>True</UseLimits>
<LowerLimit>0.004</LowerLimit>
<UpperLimit>0.006</UpperLimit>
<LowerOutput>6e-08</LowerOutput>
<UpperOutput>0</UpperOutput>
<A>0</A>
<B>0</B>
<C>-3e-05</C>
<D>1.8e-07</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>2</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>5c6a61ea-75f5-464a-b513-65bda492d5a1</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>2</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>a158b676-1b91-42d5-92e5-314141587fa9</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>12</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>fb05d8b0-87e7-43e1-8ea7-2bf683913321</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>11</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>4aa84a92-74cf-4f45-81b5-98ede2cea4f5</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>17</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>c832acde-40da-411b-ab7a-557281310be2</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>11</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>23ee1704-6002-4e10-a47b-81a6d488e21d</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>12</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>30641b72-ea06-429f-9c5c-96fc58fb2506</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
<Adapter>
<Type>NodeToPhysical</Type>
<SourceModule>RealisticNeuralNet</SourceModule>
<SourceNodeID>12</SourceNodeID>
<SourceDataType>MembraneVoltage</SourceDataType>
<TargetBodyType>RigidBody</TargetBodyType>
<TargetBodyID>8a428047-52e6-4d99-907f-3cd51c3b9ef8</TargetBodyID>
<Gain>
<Type>Polynomial</Type>
<UseLimits>False</UseLimits>
<A>0</A>
<B>0</B>
<C>1</C>
<D>0</D>
</Gain>
</Adapter>
</Adapters>
</NervousSystem>

Loading data, please wait...