SynapticNoise.py

'''
    Neuromuscular simulator in Python.
    Copyright (C) 2016  Renato Naville Watanabe

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.

    Contact: renato.watanabe@usp.br
'''


from NeuralTractUnit import NeuralTractUnit
import numpy as np

class SynapticNoise(object):
    '''
    Class that implements a synaptic noise for a pool of neurons.
    '''
    
    def __init__(self, conf, pool):
        '''
        Constructor

        - Inputs:
            + **conf**: Configuration object with the simulation parameters.

            + **pool**: string with the name of the pool.
        '''
        ## Indicates that is a neural tract.
        self.kind = 'SN'
        ## String with the name of the pool.
        self.pool = pool
        ## The number of neural tract units.
        self.Number = int(conf.parameterSet('Number_' + pool, pool, 0))
        
        ## List of NeuralTractUnit objects.
        self.unit = [] 
        
        self.conf = conf
        
        self.GammaOrder = int(conf.parameterSet('NoiseGammaOrder_' + pool, pool, 0))

        for i in xrange(0, self.Number): 
            self.unit.append(NeuralTractUnit(conf, pool, i))
        ## Vector with the instants of spikes in the terminal, in ms.
        self.poolTerminalSpikes = np.array([])
        ## Indicates the measure that the TargetFunction of the
        ## spikes follows. For now it can be *ISI* (interspike
        ## interval) or *FR* (firing rate).
        self.target = conf.parameterSet('NoiseTarget_' + pool, pool, 0)
        if self.target == 'ISI' :
            exec 'def NoiseFunction(t): return 1000.0/('  +  conf.parameterSet('NoiseFunction_' + pool, pool, 0) + ')'
        else:
            exec 'def NoiseFunction(t): return '   +  conf.parameterSet('NoiseFunction_' + pool, pool, 0)

        ## The  mean firing rate of the neural tract units.
        self.FR = conf.inputFunctionGet(NoiseFunction) * conf.timeStep_ms/1000.0

        
        ##
        self.timeIndex = 0
        ##
        print 'Synaptic Noise on ' + pool + ' built'
    
    def atualizePool(self, t):
        '''
        Update all neural tract units from the neural tract.
        
        - Inputs:
            + **t**: current instant, in ms.
        '''
        

        for i in self.unit: i.atualizeNeuralTractUnit(t, self.FR[self.timeIndex]*self.conf.timeStep_ms/1000.0, self.GammaOrder)
        self.timeIndex +=1

    def listSpikes(self):
        '''
        List the spikes that occurred in neural tract units.
        '''

        for i in xrange(0,self.Number):
            if i == 0: terminalSpikeTrain = np.array(self.unit[i].terminalSpikeTrain)
            else: terminalSpikeTrain = np.append(terminalSpikeTrain, np.array(self.unit[i].terminalSpikeTrain))
        self.poolTerminalSpikes = terminalSpikeTrain

        self.poolTerminalSpikes = np.reshape(self.poolTerminalSpikes, (-1, 2))