Multiscale simulation of the striatal medium spiny neuron (Mattioni & Le Novere 2013)

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Accession:150284
"… We present a new event-driven algorithm to synchronize different neuronal models, which decreases computational time and avoids superfluous synchronizations. The algorithm is implemented in the TimeScales framework. We demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum. The model comprises over a thousand dendritic spines, where the electrical model interacts with the respective instances of a biochemical model. Our results show that a multiscale model is able to exhibit changes of synaptic plasticity as a result of the interaction between electrical and biochemical signaling. …"
Reference:
1 . Mattioni M, Le Novère N (2013) Integration of biochemical and electrical signaling-multiscale model of the medium spiny neuron of the striatum. PLoS One 8:e66811 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell;
Channel(s): I Na,p; I Na,t; I T low threshold; I A; I K,Ca; I CAN; I Calcium; I A, slow; I Krp; I R; I Q;
Gap Junctions:
Receptor(s):
Gene(s): Kv4.2 KCND2; Kv1.2 KCNA2; Cav1.3 CACNA1D; Cav1.2 CACNA1C; Kv2.1 KCNB1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity; Signaling pathways; Calcium dynamics; Multiscale;
Implementer(s): Mattioni, Michele [mattioni at ebi.ac.uk];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; I Na,p; I Na,t; I T low threshold; I A; I K,Ca; I CAN; I Calcium; I A, slow; I Krp; I R; I Q;
# Author Michele Mattioni
# Wed Mar 18 17:51:51 GMT 2009

import os
import logging
FORMAT = '%(levelname)s %(name)s %(lineno)s   %(message)s'
if os.environ.has_key('DEBUG'):
    logging.basicConfig(level=logging.DEBUG, format=FORMAT)
else: 
    logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)

import numpy as np
import math

import sys

from neuron import h

import matplotlib
backend = 'Agg'
matplotlib.use(backend)
import matplotlib.pyplot as plt

from neuronvisio.manager import Manager
from sumatra.external.NeuroTools import parameters

from neuronControl.nrnManager import NeuronManager
from neuronControl.stimul import Stimul

#import tables 
from extref import ExtRef 

    
def iClamptest(delay=10, duration=250, amplititude=0.248):
    """# Quick Iclamp test to check the current dynamics"""
    iclamp = h.IClamp(h.MSP_Cell[0].soma(0.5))
    iclamp.delay = delay
    iclamp.dur = duration
    iclamp.amp = amplititude        


class Runner():
    """Class to run the two simulator together"""
    def __init__(self, param_dict):
        """Read the param and create the manager from neuronvisio"""
        self.param = param_dict
        # Create Neuronvisio Manager
        self.manager = Manager()
        self.vecs = {}
    
    def advance_ecell(self, spine, delta_t):
        """
        Advance ecell simulator in `spine` of `delta_t`.
        
        Paramters:
        ----------
        tmp_tstop: Temporary tstop. It has to be expressed in seconds
        """
        current_time = spine.ecellMan.ses.getCurrentTime()
        len_current_time = len (spine.ecellMan.loggers['ca'].getData()[:,0])
        logger.debug ("Ecell current time: %s in %s. Advancing of: %s seconds.\
        Current time len: %s" %(current_time, spine.id, delta_t, len_current_time))
        spine.ecellMan.ses.run(delta_t)
    
    def advance_quickly(self, tmp_tstop, nrnManager):
        """
        Advance the two simulators quickly in an independent way. Synapse weight 
        is synchronized at the end
        """
        stimulated_spines = self.param['stimulated_spines']
        #Update the weight
        for spine_id in stimulated_spines:
            spine = nrnManager.spines[spine_id]   
            self.update_synape_weight(spine)
        
        delta_ecell = tmp_tstop - h.t
        delta_ecell_seconds = delta_ecell / 1e3
        logger.info ("\nAdvance quickly routine.")
        logger.info ("Current Neuron time: %s, aimed tstop[ms]: %s" %(h.t, tmp_tstop))
        logger.info ("Delta applied on Ecell simulator [s]: %s\n" % delta_ecell_seconds)
        nrnManager.run(tmp_tstop)
        for spine_id in stimulated_spines:
            spine = nrnManager.spines[spine_id]
            self.advance_ecell(spine, delta_ecell_seconds)
            self.update_synape_weight(spine)     
        
    def build_vecs_to_plot(self, var, secs, anyRefs):
        """Create the dictionary of section->vectors to plot"""
        vecs_to_plot = {}
        for secName in secs:
            for ref in anyRefs:
                if secName == ref.sec_name:
                    if ref.vecs.has_key(var):
                        key = secName + '_' + var
                        vecs_to_plot[key] = ref.vecs[var]
        return vecs_to_plot
        
    def create_excitatory_inputs(self, nrnManager):
        """
        Create the excitatory inputs according to the parametes file.
        
        - Create the NEURON inputs on each synapses according to the parameters
        - Record the synaptic vars with a SynVecRef
        - Set and initialize the ecell biochemical simulator in the stimulated
        spine."""
        
        excitatory_stimuli = []
        
        for spine_id in self.param['stimulated_spines']:
            if spine_id in self.param.keys(): # for each spine we list the input
                spine = nrnManager.spines[spine_id]
                for stim_id in self.param[spine.id]:
                    stim_dictionary = self.param[stim_id]
                    
                    if stim_dictionary.has_key('t_stim'):
                        stim = Stimul((stim_dictionary['t_stim']), 
                                      stim_dictionary['numbers'], 
                                      stim_dictionary['delay'], 
                                      stim_dictionary['type'])   
                        if stim.chan_type == 'ampa':
                            for syn in spine.synapses:
                                if syn.chan_type == 'ampa':
                                    syn.stims.append(stim)
                                    
                        elif stim.chan_type == 'nmda':# more than one stim
                            for syn in spine.synapses:
                                if syn.chan_type == 'nmda':
                                    syn.stims.append(stim)
                           
                        stims_time = stim.get_stims_time()
                        excitatory_stimuli.extend(stims_time)
                    else:
                        logger.info("No stim applied to spine: %s" %spine_id)
                    
                spine.deploy_stims(self.param['neuron_time_recording_interval'])
                if self.param['bio_on']:
                    spine.setup_bio_sim() # Initializing ecell
                
        
        excitatory_stimuli = list(set(excitatory_stimuli))
        excitatory_stimuli.sort()
        return excitatory_stimuli
    
    def create_vectors(self):
        "Vectors to store the resutls"
        for sec in h.allsec():
            for var in self.param['var_to_plot']:
                vec = self.manager.create_record_vector(sec, var, None)
                sec_name = sec.name()
                if self.vecs.has_key(sec_name):
                    self.vecs[sec_name].append(vec)
                else:
                    self.vecs[sec_name] = [vec]
    
    def get_calcium_flux(self, spine):
        """
        Retrieving the calcium in the interval. end is always -1 because is
        the last timepoint available, start is when the interval has begun
        """
        delta_calcium_sampling = self.param['delta_calcium_sampling']
        
        ## We use the recorded interval instead of the dt, 'cause we are 
        ## working directly with the time recording 
#        start_index = -int(delta_calcium_sampling/ self.param['dtNeuron'])
        start_index = -int(delta_calcium_sampling/ self.param['neuron_time_recording_interval'])
        # Getting the calcium value
        vec_spine_head_cai = self.manager.get_vector(spine.head, 'cai')
        vec_spine_head_cali = self.manager.get_vector(spine.head, 'cali')
        
        head_cai = vec_spine_head_cai.x[start_index]
        head_cali = vec_spine_head_cali.x[start_index]
        electrical_ca_start = head_cai + head_cali
        
        head_cai = vec_spine_head_cai.x[-1]
        head_cali = vec_spine_head_cali.x[-1]
        electrical_ca_end = head_cai + head_cali
        electrical_diff = electrical_ca_end - electrical_ca_start
#        logger.debug( "Len vecs: %s start_idx: %s" %(len(vec_spine_head_cali), start_index))
#        logger.debug( "Electrical calcium start: %s end: %s difference: %s" %(electrical_ca_start,
#                                                                      electrical_ca_end,
#                                                                      electrical_diff))
        # Calculating the flux
        k_calcium_flux = electrical_diff / delta_calcium_sampling
        
        return k_calcium_flux

    def equilibrium(self, nrnManager):
        """Brings both NEURON and Ecell to equilibrium"""  
        logger.info ("#--#")
        logger.info ("Equilibrium started.")
        nrnManager.run(self.param['t_equilibrium_neuron'])
        for spine_id in self.param['stimulated_spines']:
            spine = nrnManager.spines[spine_id]
            runner.advance_ecell(spine, self.param['t_equilibrium_ecell'])
            spine.set_ampa_equilibrium_baseline()
        logger.info ("Equilibrium run finished. Starting normal simulation.")
        logger.info ("#--#")
    
    def main(self):
        logger.info ("#--#")
        logger.info ("Equilibrium run for the two simulators")
        # Neuron Setup -----------------------------------------------------------
        nrnManager = NeuronManager(self.param['biochemical_filename'],
                                   self.param['big_spine'],
                                   self.param['dtNeuron'],
                                   spines_dist=self.param['spines_dist'],
                                   mod_path='mod', 
                                   hoc_path='hoc')
        # Easier to debug. 
        self.nrnManager = nrnManager
        
        nrnManager.set_kir_gkbar(self.param['kir_gkbar'])

        excitatory_stims = self.create_excitatory_inputs(nrnManager)
        
        logger.info ("This are the time of the stims: %s" %excitatory_stims)
    
        # Recording -----------------------------------------------
        # - Recording and stimul
        # - Set the stimuls to the synapses
        # - Initialize Ecell in each spine
        
        # Threading it!
        nrnManager.enable_threads(self.param['nthreads'], multisplit_on=False)
        
        self.record_vectors(nrnManager)
        
        # Experiment ----------------------------------------------- 
        nrnManager.init() # Initializing neuron
        
        if self.param['bio_on']:
            self.equilibrium(nrnManager)
            self.run_simulation(nrnManager, excitatory_stims)
        else:
            # Only Electrical
            tstop = self.param['t_equilibrium_neuron'] + self.param['tStop']
            self.test_electrical_weight_change()
        # Save the Results ------------------------------------
        saving_dir = self.manager.create_new_dir(root='Data')
        self.save_results(nrnManager, saving_dir)
        self.plot_results(nrnManager, saving_dir)
        
    def plot_results(self, nrnManager, saving_dir):
        for i, var in enumerate(self.param['var_to_plot']):
            secs = self.param['section_to_plot']
            vecs_to_plot = self.build_vecs_to_plot(var, 
                                                   secs, 
                                                   self.manager.refs['VecRef'])
            
            self.manager.plot_vecs(vecs_to_plot, figure_num=i)
            
            if var == 'v':
                plt.ylabel("Voltage [mV]")
                plt.xlabel("Time [ms]")
                plt.ylim(-90) # Setting the minimum limits
                
            elif var == 'cai' or var == 'cali':
                plt.xlabel("Time [ms]")
                plt.ylabel("Concentration [mM]")
            
            elif var == 'ica':
                plt.xlabel("Time [ms]")
                plt.ylabel("Current [nA]")
#                
            fig_file = 'plot_' + var
            plt.savefig(os.path.join(saving_dir, fig_file))
            
        if self.param['bio_on']:  
            from helpers.plotter import EcellPlotter
            ecp = EcellPlotter()
            x_start = self.param['t_equilibrium_ecell']
            x_stop = x_start + self.param['tStop']/1e3

            for stim_spine in self.param['stimulated_spines']:
                spine = nrnManager.spines[stim_spine]
                ecp.plot_timeCourses(spine.ecellMan.timeCourses, save=True, 
                                   dir=saving_dir, name=spine.id, 
                                   x_lims= [x_start, x_stop])
                ecp.plot_weight(spine.ecellMan.timeCourses, dir=saving_dir)
            
    def record_vectors(self, nrnManager):
        """Add a vecRef to record the vectors"""
        t_i_r = self.param['neuron_time_recording_interval']
        
        for spine_id in self.param['stimulated_spines']:
            spine = nrnManager.spines[spine_id]
            for syn in spine.synapses:
                pp = syn.chan     
                self.manager.create_time_record(time_interval_recording=t_i_r,
                                                point_process=pp)
        
        for var in self.param['var_to_plot']:
            for sec_rec in self.param['sec_to_rec']:
                if sec_rec == 'all':
                    self.manager.add_all_vecRef(var, 
                                                t_i_r)
                    break
                else:
                    for sec in h.allsec():
                        if sec.name() in self.param['sec_to_rec']:
                            self.manager.add_vecRef(var, 
                                                    sec, 
                                                    t_i_r)

        # Recording the synapses
        
        for spine_id in self.param['stimulated_spines']:
            spine = nrnManager.spines[spine_id]
            for syn in spine.synapses:
                self.manager.add_synVecRef(syn)    


    def run_simulation(self, nrnManager, excitatory_stims):

        """
        Run the simulation. If input synchronizes the two simulators, 
        otherwise run each on its own and advance quickly
            
        """
        # Processing the options
        tStop_final = self.param['tStop'] + self.param['t_equilibrium_neuron']        
        
        # Getting the calcium before the stims
        for spine_id in self.param['stimulated_spines']:
            spine = nrnManager.spines[spine_id]
            self.update_synape_weight(spine)
            
        while h.t < tStop_final:
            
            if excitatory_stims:
                t_stim = excitatory_stims.pop(0)
                s_log = "Current Neuron time: %s. \
                Current t_stim: %s, remaining input: %s" %(h.t, 
                                                           t_stim,
                                                           len(excitatory_stims))
                logger.debug( s_log)
                
                if h.t < t_stim:
                    self.advance_quickly(t_stim, nrnManager)
                    tmp_tstop = t_stim + self.param['t_buffer']
                    self.synch_simulators(tmp_tstop, nrnManager)
            else:
                logger.debug( "No excitatory input remaining. Quickly to the end")
                self.advance_quickly(tStop_final, nrnManager)
                h.fadvance() # This is to force the latest step and avoid the infinite loop.
        
        # Recording last 
        for spine_id in self.param['stimulated_spines']:
            spine = nrnManager.spines[spine_id]
            self.update_synape_weight(spine)
    
    def save_results(self, nrnManager, saving_dir):
        """Saving both results"""
        if self.param['bio_on']:
                # Add timeseries
            extRef = ExtRef()
            extRef.add_timeseries(self.manager, 
                                  self.param['stimulated_spines'], 
                                  nrnManager)
            
            # Saving the weight
            extRef.add_weights(self.manager, 
                               self.param['stimulated_spines'], 
                               nrnManager)
                        # Saving the weight
            extRef.add_kflux(self.manager, 
                               self.param['stimulated_spines'], 
                               nrnManager)
            
        logger.info( "Simulation Ended. Saving results")
        hdf_name = 'storage.h5'
        filename = os.path.join(saving_dir, hdf_name)
        logger.info( "Results will be saved in %s" %filename)
        # Saving everything
        self.manager.save_to_hdf(filename)
        
    def synch_simulators(self, tmp_tstop, nrnManager):
        """
        Calculate the synapse weight, using the calcium in the spine_heads 
        as input.
        
        Synch the two simulators using the following steps:
        1. Calculate the calcium concentration in the spines head in 
        NEURON and set this value in ecell.
        2. Advance ecell for the specified_delta
        3. Update the electric weight of the synapses in NEURON
        """
        logger.info ("Current time: %f Synchronizing sims till [ms] %s" %(h.t, tmp_tstop))
        
        stimulated_spines = self.param['stimulated_spines']
        t_sync_start = h.t
        while h.t < tmp_tstop:
            h.fadvance() # run Neuron for step
            # We are updating the calcium according to our 
            # delta calcium sampling. 
            
            # due to numerical errors we can't use straight comparison, 
            # but we need to wrap this into a lower/upper bounds 
            # conditions. 
                       
            lower_time = t_sync_start + self.param['delta_calcium_sampling']
            upper_time = lower_time + self.param['dtNeuron']
#            logger.debug( "Lower time: %.15f h.t: %.15f Upper time: %.15f" %(lower_time, 
#                                                            h.t, 
#                                                            upper_time))
            if lower_time <= h.t <= upper_time: 
                for spine_id in stimulated_spines :
                    spine = nrnManager.spines[spine_id]
                    self.sync_calcium(spine)
                    self.advance_ecell(spine, (h.t - t_sync_start) / 1e3)
                    # Stopping flux from the input.
                    spine.ecellMan.ca_in['k'] = 0
                    # Re-enabling pump and leak. 
                    spine.ecellMan.ca_leak['vmax'] = self.param['ca_leak_vmax']
                    spine.ecellMan.ca_pump['vmax'] = self.param['ca_pump_vmax']
                    self.update_synape_weight(spine)
                t_sync_start = h.t # Resetting the t_start to the new NEURON time.
    
    def sync_calcium(self, spine):
        """"
        Calculate the flux of the calcium in the spine_head and synch 
        it with ecell. 
        """
        if hasattr(spine, 'ecellMan'):
            
            k_ca_flux = self.get_calcium_flux(spine)
            # Unit conversion in update_calcium
            spine.update_calcium(k_ca_flux)
            
    def update_synape_weight(self, spine):
        """
        Update the electrical weight's synapses. Use the baseline calculated
        just after the equilibrium as reference to estimate the change of the weight
        
        spine : the spine where the weight should be updated
        baseline : the equilibrium concentration of AMPAR-P which we use as reference
        and  put equal to one.
        """    
        
        # Updating the AMPA synapses
        for syn in spine.synapses:
            if syn.chan_type == 'ampa':                       
            # Retrieve the value of the weight.
                weight = spine.ecellMan.ampar_P['Value']/spine.ampa_equilibrium_conc
                syn.netCon.weight[0] = weight
                # The weight of the ampa is a double list
                # Check the specs in synapse weight for more info. 
                syn.weight[0].append(h.t)
                syn.weight[1].append(weight)
                logger.debug( "Updating synapse weight in %s, time [ms]: %s, weight: %s, netCon: %s" %(spine.id,
                                                                                   h.t,
                                                                                   weight,
                                                                                   syn.netCon.weight[0]))
                logger.debug( "AMPA syn value g: %s itmp: %s ical: %s i: %s scale: %s voltage: %s" %(syn.chan.g, 
                                                                                  syn.chan.itmp,
                                                                                  syn.chan.ical,
                                                                                  syn.chan.i,
                                                                                  syn.chan.scale,
                                                                                  spine.psd.v ))
                itmp = syn.chan.scale * syn.chan.g * spine.psd.v
                logger.debug( "itmp in NEURON: %s, itmp calculated: %s" %(syn.chan.itmp, itmp))


    def test_electrical_weight_change(self):
        """Run the sims till tstop, and then change the weight"""
        t_eq = self.param['t_equilibrium_neuron']
        runner.nrnManager.run(200) # first input 180
        
        sp1 =runner.nrnManager.spines['spine1']
        syn_a = sp1.synapses[0]
        syn_a.netCon.weight[0] = 1.5
        tStop = self.param['tStop']
        tStop += t_eq
        runner.nrnManager.run(tStop)
        
        runner.plot_results
     

if __name__ == "__main__":
    
    if len(sys.argv) != 2:
        logger.warning("No parameter file supplied. Abort.")
        usage = 'python spineIntegration.py parameters_file.param'
        logger.info( usage)
        sys.exit()
    
    parameter_file = sys.argv[1]
    parameter_dict = parameters.ParameterSet(parameter_file)
    runner = Runner(parameter_dict)
    runner.main()
    


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