pyclustering/nnet/hhn.py
def hnn_state(self, inputs, t, argv):
        """!
        @brief Returns new values of excitatory and inhibitory parts of oscillator and potential of oscillator.
        
        @param[in] inputs (list): States of oscillator for integration [v, m, h, n] (see description below).
        @param[in] t (double): Current time of simulation.
        @param[in] argv (tuple): Extra arguments that are not used for integration - index of oscillator.
        
        @return (list) new values of oscillator [v, m, h, n], where:
                v - membrane potantial of oscillator,
                m - activation conductance of the sodium channel,
                h - inactication conductance of the sodium channel,
                n - activation conductance of the potassium channel.
        
        """
        
        index = argv;
        
        v = inputs[0]; # membrane potential (v).
        m = inputs[1]; # activation conductance of the sodium channel (m).
        h = inputs[2]; # inactivaton conductance of the sodium channel (h).
        n = inputs[3]; # activation conductance of the potassium channel (n).
        
        # Calculate ion current
        # gNa * m[i]^3 * h * (v[i] - vNa) + gK * n[i]^4 * (v[i] - vK) + gL  (v[i] - vL)
        active_sodium_part = self._params.gNa * (m ** 3) * h * (v - self._params.vNa);
        inactive_sodium_part = self._params.gK * (n ** 4) * (v - self._params.vK);
        active_potassium_part = self._params.gL * (v - self._params.vL);
        
        Iion = active_sodium_part + inactive_sodium_part + active_potassium_part;
        
        Iext = 0.0;
        Isyn = 0.0;
        if (index < self._num_osc): 
            # PN - peripheral neuron - calculation of external current and synaptic current.
            Iext = self._stimulus[index] * self._noise[index];    # probably noise can be pre-defined for reducting compexity
            
            memory_impact1 = 0.0;
            for i in range(0, len(self._central_element[0].pulse_generation_time)):
                memory_impact1 += self.__alfa_function(t - self._central_element[0].pulse_generation_time[i], self._params.alfa_inhibitory, self._params.betta_inhibitory);
            
            memory_impact2 = 0.0;
            for i in range(0, len(self._central_element[1].pulse_generation_time)):
                memory_impact2 += self.__alfa_function(t - self._central_element[1].pulse_generation_time[i], self._params.alfa_inhibitory, self._params.betta_inhibitory);
    
            Isyn = self._params.w2 * (v - self._params.Vsyninh) * memory_impact1 + self._link_weight3[index] * (v - self._params.Vsyninh) * memory_impact2;
        else:
            # CN - central element.
            central_index = index - self._num_osc;
            if (central_index == 0):
                Iext = self._params.Icn1;   # CN1
                
                memory_impact = 0.0;
                for index_oscillator in range(0, self._num_osc):
                    for index_generation in range(0, len(self._pulse_generation_time[index_oscillator])):
                        memory_impact += self.__alfa_function(t - self._pulse_generation_time[index_oscillator][index_generation], self._params.alfa_excitatory, self._params.betta_excitatory);
                 
                Isyn = self._params.w1 * (v - self._params.Vsynexc) * memory_impact;
                
            elif (central_index == 1):
                Iext = self._params.Icn2;   # CN2
                Isyn = 0.0;
                
            else:
                assert 0;
        
        
        # Membrane potential
        dv = -Iion + Iext - Isyn;
        
        # Calculate variables
        potential = v - self._params.vRest;
        am = (2.5 - 0.1 * potential) / (math.exp(2.5 - 0.1 * potential) - 1.0);
        ah = 0.07 * math.exp(-potential / 20.0);
        an = (0.1 - 0.01 * potential) / (math.exp(1.0 - 0.1 * potential) - 1.0);
        
        bm = 4.0 * math.exp(-potential / 18.0);
        bh = 1.0 / (math.exp(3.0 - 0.1 * potential) + 1.0);
        bn = 0.125 * math.exp(-potential / 80.0);
        
        dm = am * (1.0 - m) - bm * m;
        dh = ah * (1.0 - h) - bh * h;
        dn = an * (1.0 - n) - bn * n;
        
        return [dv, dm, dh, dn];
def hnn_state(self, inputs, t, argv):
        """!
        @brief Returns new values of excitatory and inhibitory parts of oscillator and potential of oscillator.
        
        @param[in] inputs (list): States of oscillator for integration [v, m, h, n] (see description below).
        @param[in] t (double): Current time of simulation.
        @param[in] argv (tuple): Extra arguments that are not used for integration - index of oscillator.
        
        @return (list) new values of oscillator [v, m, h, n], where:
                v - membrane potantial of oscillator,
                m - activation conductance of the sodium channel,
                h - inactication conductance of the sodium channel,
                n - activation conductance of the potassium channel.
        
        """
        
        index = argv;
        
        v = inputs[0]; # membrane potential (v).
        m = inputs[1]; # activation conductance of the sodium channel (m).
        h = inputs[2]; # inactivaton conductance of the sodium channel (h).
        n = inputs[3]; # activation conductance of the potassium channel (n).
        
        # Calculate ion current
        # gNa * m[i]^3 * h * (v[i] - vNa) + gK * n[i]^4 * (v[i] - vK) + gL  (v[i] - vL)
        active_sodium_part = self._params.gNa * (m ** 3) * h * (v - self._params.vNa);
        inactive_sodium_part = self._params.gK * (n ** 4) * (v - self._params.vK);
        active_potassium_part = self._params.gL * (v - self._params.vL);
        
        Iion = active_sodium_part + inactive_sodium_part + active_potassium_part;
        
        Iext = 0.0;
        Isyn = 0.0;
        if (index < self._num_osc): 
            # PN - peripheral neuron - calculation of external current and synaptic current.
            Iext = self._stimulus[index] * self._noise[index];    # probably noise can be pre-defined for reducting compexity
            
            memory_impact1 = 0.0;
            for i in range(0, len(self._central_element[0].pulse_generation_time)):
                memory_impact1 += self.__alfa_function(t - self._central_element[0].pulse_generation_time[i], self._params.alfa_inhibitory, self._params.betta_inhibitory);
            
            memory_impact2 = 0.0;
            for i in range(0, len(self._central_element[1].pulse_generation_time)):
                memory_impact2 += self.__alfa_function(t - self._central_element[1].pulse_generation_time[i], self._params.alfa_inhibitory, self._params.betta_inhibitory);
    
            Isyn = self._params.w2 * (v - self._params.Vsyninh) * memory_impact1 + self._link_weight3[index] * (v - self._params.Vsyninh) * memory_impact2;
        else:
            # CN - central element.
            central_index = index - self._num_osc;
            if (central_index == 0):
                Iext = self._params.Icn1;   # CN1
                
                memory_impact = 0.0;
                for index_oscillator in range(0, self._num_osc):
                    for index_generation in range(0, len(self._pulse_generation_time[index_oscillator])):
                        memory_impact += self.__alfa_function(t - self._pulse_generation_time[index_oscillator][index_generation], self._params.alfa_excitatory, self._params.betta_excitatory);
                 
                Isyn = self._params.w1 * (v - self._params.Vsynexc) * memory_impact;
                
            elif (central_index == 1):
                Iext = self._params.Icn2;   # CN2
                Isyn = 0.0;
                
            else:
                assert 0;
        
        
        # Membrane potential
        dv = -Iion + Iext - Isyn;
        
        # Calculate variables
        potential = v - self._params.vRest;
        am = (2.5 - 0.1 * potential) / (math.exp(2.5 - 0.1 * potential) - 1.0);
        ah = 0.07 * math.exp(-potential / 20.0);
        an = (0.1 - 0.01 * potential) / (math.exp(1.0 - 0.1 * potential) - 1.0);
        
        bm = 4.0 * math.exp(-potential / 18.0);
        bh = 1.0 / (math.exp(3.0 - 0.1 * potential) + 1.0);
        bn = 0.125 * math.exp(-potential / 80.0);
        
        dm = am * (1.0 - m) - bm * m;
        dh = ah * (1.0 - h) - bh * h;
        dn = an * (1.0 - n) - bn * n;
        
        return [dv, dm, dh, dn];
def hnn_state ( self , inputs , t , argv ) : index = argv v = inputs [ 0 ] # membrane potential (v).
 m = inputs [ 1 ] # activation conductance of the sodium channel (m).
 h = inputs [ 2 ] # inactivaton conductance of the sodium channel (h).
 n = inputs [ 3 ] # activation conductance of the potassium channel (n).
 # Calculate ion current
 # gNa * m[i]^3 * h * (v[i] - vNa) + gK * n[i]^4 * (v[i] - vK) + gL  (v[i] - vL)
 active_sodium_part = self . _params . gNa * ( m ** 3 ) * h * ( v - self . _params . vNa ) inactive_sodium_part = self . _params . gK * ( n ** 4 ) * ( v - self . _params . vK ) active_potassium_part = self . _params . gL * ( v - self . _params . vL ) Iion = active_sodium_part + inactive_sodium_part + active_potassium_part Iext = 0.0 Isyn = 0.0 if ( index < self . _num_osc ) : # PN - peripheral neuron - calculation of external current and synaptic current.
 Iext = self . _stimulus [ index ] * self . _noise [ index ] # probably noise can be pre-defined for reducting compexity
 memory_impact1 = 0.0 for i in range ( 0 , len ( self . _central_element [ 0 ] . pulse_generation_time ) ) : memory_impact1 += self . __alfa_function ( t - self . _central_element [ 0 ] . pulse_generation_time [ i ] , self . _params . alfa_inhibitory , self . _params . betta_inhibitory ) memory_impact2 = 0.0 for i in range ( 0 , len ( self . _central_element [ 1 ] . pulse_generation_time ) ) : memory_impact2 += self . __alfa_function ( t - self . _central_element [ 1 ] . pulse_generation_time [ i ] , self . _params . alfa_inhibitory , self . _params . betta_inhibitory ) Isyn = self . _params . w2 * ( v - self . _params . Vsyninh ) * memory_impact1 + self . _link_weight3 [ index ] * ( v - self . _params . Vsyninh ) * memory_impact2 else : # CN - central element.
 central_index = index - self . _num_osc if ( central_index == 0 ) : Iext = self . _params . Icn1 # CN1
 memory_impact = 0.0 for index_oscillator in range ( 0 , self . _num_osc ) : for index_generation in range ( 0 , len ( self . _pulse_generation_time [ index_oscillator ] ) ) : memory_impact += self . __alfa_function ( t - self . _pulse_generation_time [ index_oscillator ] [ index_generation ] , self . _params . alfa_excitatory , self . _params . betta_excitatory ) Isyn = self . _params . w1 * ( v - self . _params . Vsynexc ) * memory_impact elif ( central_index == 1 ) : Iext = self . _params . Icn2 # CN2
 Isyn = 0.0 else : assert 0 # Membrane potential
 dv = - Iion + Iext - Isyn # Calculate variables
 potential = v - self . _params . vRest am = ( 2.5 - 0.1 * potential ) / ( math . exp ( 2.5 - 0.1 * potential ) - 1.0 ) ah = 0.07 * math . exp ( - potential / 20.0 ) an = ( 0.1 - 0.01 * potential ) / ( math . exp ( 1.0 - 0.1 * potential ) - 1.0 ) bm = 4.0 * math . exp ( - potential / 18.0 ) bh = 1.0 / ( math . exp ( 3.0 - 0.1 * potential ) + 1.0 ) bn = 0.125 * math . exp ( - potential / 80.0 ) dm = am * ( 1.0 - m ) - bm * m dh = ah * ( 1.0 - h ) - bh * h dn = an * ( 1.0 - n ) - bn * n return [ dv , dm , dh , dn ]
!
        @brief Returns new values of excitatory and inhibitory parts of oscillator and potential of oscillator.
        
        @param[in] inputs (list): States of oscillator for integration [v, m, h, n] (see description below).
        @param[in] t (double): Current time of simulation.
        @param[in] argv (tuple): Extra arguments that are not used for integration - index of oscillator.
        
        @return (list) new values of oscillator [v, m, h, n], where:
                v - membrane potantial of oscillator,
                m - activation conductance of the sodium channel,
                h - inactication conductance of the sodium channel,
                n - activation conductance of the potassium channel.
!