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Actualizados los modelos de Vavoulis y la sinapsis de activación gradual #6

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Actualizados los modelos de Vavoulis y la sinapsis de activación gradual #6

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3 changes: 2 additions & 1 deletion include/GradualActivationSynapsis.h
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,8 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
template <typename TNode1, typename TNode2, typename TIntegrator,
typename precission = double>
requires NeuronConcept<TNode1> && NeuronConcept<TNode2> &&
IntegratorConcept<TIntegrator>
// IntegratorConcept<TIntegrator>
IntegratorConcept<TIntegrator, SerializableWrapper<SystemWrapper<GradualActivationSynapsisModel<precission>>>>
class GradualActivationSynapsis
Comment on lines 55 to 58
: public SerializableWrapper<
SystemWrapper<GradualActivationSynapsisModel<precission> > > {
Expand Down
74 changes: 57 additions & 17 deletions models/VavoulisModel.h
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Original file line number Diff line number Diff line change
Expand Up @@ -86,17 +86,59 @@ class VavoulisModel : public NeuronBase<Precission> {
return 90 * n * n * n * n * (va + 90);
}

Precission incr_p(type t, Precission p, Precission v,
Precission tau_p) const {
// switch (t) {
Precission pinf = 1 / (1 + exp((-61.6 - v) / 5.6));
Precission incr_p(type t, Precission p, Precission v, Precission va,
Precission tau_p_param) const {
Precission pinf;
Precission tau_p;
switch (t) {
case n1m:
// Tabla 1: p_inf = 1/(1+exp((-38.8-V_S)/10)), tau_p = 250 ms (constante)
pinf = 1 / (1 + exp((-38.8 - v) / 10.0));
tau_p = tau_p_param;
break;
case n2v:
// Tabla 1: p_inf = 1/(1+exp((-51-V_S)/10.3))
// tau_p = 28.3 + 44.1 * exp(-((-11.8-V_A)/26.6)^2)
pinf = 1 / (1 + exp((-51.0 - v) / 10.3));
tau_p = 28.3 + 44.1 * exp(-pow((-11.8 - va) / 26.6, 2));
break;
case n3t:
// Tabla 1: p_inf = 1/(1+exp((-61.6-V_S)/5.6)), tau_p = 4 ms (constante)
pinf = 1 / (1 + exp((-61.6 - v) / 5.6));
tau_p = tau_p_param;
break;
case so:
default:
// SO es pasivo, no tiene variable p
return 0;
}
Comment on lines +93 to +114
return (pinf - p) / tau_p;
}

Precission incr_q(type t, Precission q, Precission v,
Precission tau_q) const {
// switch (t) {
Precission qinf = 1 / (1 + exp((-73.2 - v) / -5.1));
Precission incr_q(type t, Precission q, Precission v, Precission va,
Precission tau_q_param) const {
Precission qinf;
Precission tau_q;
switch (t) {
case n1m:
// N1M no usa variable q (solo p^3)
return 0;
case n2v:
// Tabla 1: q_inf = 1/(1+exp((-45-V_S)/-3))
// tau_q = 187.6 + 637.7 * exp(-((-9.5-V_A)/23.3)^2)
qinf = 1 / (1 + exp((-45.0 - v) / -3.0));
tau_q = 187.6 + 637.7 * exp(-pow((-9.5 - va) / 23.3, 2));
break;
case n3t:
// Tabla 1: q_inf = 1/(1+exp((-73.2-V_S)/-5.1)), tau_q = 400 ms (constante)
qinf = 1 / (1 + exp((-73.2 - v) / -5.1));
tau_q = tau_q_param;
break;
case so:
default:
// SO es pasivo, no tiene variable q
return 0;
}
return (qinf - q) / tau_q;
}

Expand All @@ -115,26 +157,24 @@ class VavoulisModel : public NeuronBase<Precission> {
}

public:
VavoulisModel(ConstructorArgs const &args) {
std::copy(args.params, args.params + n_parameters, m_parameters);
std::copy(args.variables, args.variables + n_variables, m_variables);
}

void eval(const Precission *const vars, Precission *const params,
Precission *const incs) const {
incs[v] = (-SYNAPTIC_INPUT - il(vars[v]) -
ix((type)params[n_type], vars[v], vars[p], vars[q]) -
iec(vars[v], vars[va], vars[g_ecs]))/10;
iec(vars[v], vars[va], params[g_ecs]))/10;

incs[va] = (-il(vars[va]) - inat(vars[va], vars[h]) - ik(vars[va], vars[n]) -
iec(vars[va], vars[v], vars[g_eca]))/10;
iec(vars[va], vars[v], params[g_eca]))/10;
Comment on lines 160 to +167

incs[p] = incr_p((type)params[n_type], vars[p], vars[v], params[tau_p]);
incs[q] = incr_q((type)params[n_type], vars[q], vars[v], params[tau_q]);
// Para N2v, tau_p y tau_q dependen del voltaje axonal (va)
// Para N1M y N3t, se usan los valores constantes del parametro
incs[p] = incr_p((type)params[n_type], vars[p], vars[v], vars[va], params[tau_p]);
incs[q] = incr_q((type)params[n_type], vars[q], vars[v], vars[va], params[tau_q]);

incs[h] = incr_h(vars[h], vars[va]);
incs[n] = incr_n(vars[n], vars[va]);
}
};


#endif /*VAVOULISMODEL_H_*/