Muscle spindle feedback circuit (Moraud et al, 2016)

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Accession:189786
Here, we developed a computational model of the muscle spindle feedback circuits of the rat ankle that predicts the interactions between Epidural Stimulation and spinal circuit dynamics during gait.
Reference:
1 . Moraud EM, Capogrosso M, Formento E, Wenger N, DiGiovanna J, Courtine G, Micera S (2016) Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury. Neuron 89:814-28 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Spinal motoneuron;
Cell Type(s): Spinal cord motor neuron slow twitch;
Channel(s): I K; I K,Ca; I Na,p; I Sodium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Sensory processing; Neuromodulation;
Implementer(s): Capogrosso, Marco ; Formento, Emanuele ;
Search NeuronDB for information about:  I Na,p; I K; I K,Ca; I Sodium;
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neuralnetwork
python_files
main.py
NeuralNetwork.py
NeuralNetwork.pyc
                            
## MUSCLE SPINDLE FEEDBACK CIRCUIT MODEL by 

## Marco Capogrosso and Emanuele Formento

## Published in:
## Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury.
## Moraud EM, Capogrosso M, Formento E, Wenger N, DiGiovanna J, Courtine G, Micera S.
## Neuron. 2016 Feb 17;89(4):814-28. doi: 10.1016/j.neuron.2016.01.009. Epub 2016 Feb 4.


import sys
sys.path.append('../python_files')
from NeuralNetwork import *
from mpi4py import MPI
comm = MPI.COMM_WORLD
sizeComm = comm.Get_size()
rank = comm.Get_rank()

def main():

	simulationType = None
	if rank==0:
		simulationType = input("\nWhich Experiment would you like to perform? \nEnter 1 to compute recruitment curves \nEnter 2 to perform a dynamic stepping simulation\n ")
	comm.Barrier()
	simulationType = comm.bcast(simulationType,root=0)
	if simulationType != 1 and simulationType !=2:
		if rank==0:print "Invalid input!"
		sys.exit(-1)

	#Setting the simulation parameters
	if simulationType==1:
		network=None
		if rank==0:
			ans = raw_input("Do you want to simulate the Extensor or Flexors recruitment curve (e/f)?\n")
			if ans=="e":
				network="extensor"
				print "Extensor network set"
			elif ans=="f":
				network="flexor"
				print "Flexor network set"
			else:
				print "Invalid input, Extensor network set"
				network="extensor"
			print "Starting the recrutiment curve simulation..."
		comm.Barrier()
		network = comm.bcast(network,root=0)

	elif simulationType==2:
		amplitude=None
		frequency=None
		if rank==0:
			ans = raw_input("Do you want to modify the predefined parameters of stimulation (40Hz EES and optimal amplitude) (y/n)?\n")
			if ans=="y":
				frequency = input("Please insert the frequency of stimulation (0-200):\t")
				if frequency>=0 and frequency<=200:print "Frequency of stimulation set to: "+str(frequency)+"Hz\n"
				else:
					print "Invalid frequency value - EES frequency set to 40 Hz\n"
					frequency = 40
				amplitude = input("Please insert the amplitude of stimulation\nInsert -1 to chose the 'optimal' amplitude of stimulation (amplitude that leads to the largest recruitment of afferent fibers without recruiting efferent fibers)\nOr insert a current from 0 to 600 uA:\n\t")
				if amplitude==-1:
					amplitude="optimal"
					print "Amplitude of stimulation set to: "+str(amplitude)+"\n"
				elif amplitude>=0 and amplitude<=600: print "Amplitude of stimulation set to: "+str(amplitude)+" uA\n"
				else:
					print "Invalid amplitude value - amplitude set to optimal\n"
					amplitude="optimal"
			else:
				amplitude="optimal"
				frequency=40
			print "Starting dynamic simulation..."
		comm.Barrier()
		amplitude = comm.bcast(amplitude,root=0)
		frequency = comm.bcast(frequency,root=0)

	#Starting simulations
	sim = NeuralNetwork()
	if simulationType==1:sim.computeRecruitCurve(network)
	elif simulationType==2:sim.runSimulation(frequency,"",amplitude)

	del sim

if __name__ == '__main__':
	main()