def _neuron_states(self, inputs, t, argv):
        """!
        @brief Returns new value of the neuron (oscillator).
        
        @param[in] inputs (list): Initial values (current) of the neuron - excitatory.
        @param[in] t (double): Current time of simulation.
        @param[in] argv (tuple): Extra arguments that are not used for integration - index of the neuron.
        
        @return (double) New value of the neuron.
        
        """
        
        xi = inputs[0];
        index = argv;
        
        # own impact
        impact = self._weight[index][index] * self._outputs[index];
        
        for i in range(0, self._num_osc, 1):
            if (self.has_connection(i, index)):
                impact += self._weight[index][i] * self._outputs[i];

        x = -xi + impact;
                
        if (xi > 1): self._outputs_buffer[index] = 1; 
        if (xi < -1): self._outputs_buffer[index] = -1;
       
        return x;
def _neuron_states(self, inputs, t, argv):
        """!
        @brief Returns new value of the neuron (oscillator).
        
        @param[in] inputs (list): Initial values (current) of the neuron - excitatory.
        @param[in] t (double): Current time of simulation.
        @param[in] argv (tuple): Extra arguments that are not used for integration - index of the neuron.
        
        @return (double) New value of the neuron.
        
        """
        
        xi = inputs[0];
        index = argv;
        
        # own impact
        impact = self._weight[index][index] * self._outputs[index];
        
        for i in range(0, self._num_osc, 1):
            if (self.has_connection(i, index)):
                impact += self._weight[index][i] * self._outputs[i];

        x = -xi + impact;
                
        if (xi > 1): self._outputs_buffer[index] = 1; 
        if (xi < -1): self._outputs_buffer[index] = -1;
       
        return x;
def _neuron_states ( self , inputs , t , argv ) : xi = inputs [ 0 ] index = argv # own impact
 impact = self . _weight [ index ] [ index ] * self . _outputs [ index ] for i in range ( 0 , self . _num_osc , 1 ) : if ( self . has_connection ( i , index ) ) : impact += self . _weight [ index ] [ i ] * self . _outputs [ i ] x = - xi + impact if ( xi > 1 ) : self . _outputs_buffer [ index ] = 1 if ( xi < - 1 ) : self . _outputs_buffer [ index ] = - 1 return x
!
        @brief Returns new value of the neuron (oscillator).
        
        @param[in] inputs (list): Initial values (current) of the neuron - excitatory.
        @param[in] t (double): Current time of simulation.
        @param[in] argv (tuple): Extra arguments that are not used for integration - index of the neuron.
        
        @return (double) New value of the neuron.
!