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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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jhayav:actualizar-vavoulis-models

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@jhayav jhayav commented Jun 7, 2026

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Actualización de la librería para poder simular los modelos de Vavoulis con n1, n2, n3 y SO

Copilot AI review requested due to automatic review settings June 7, 2026 16:54

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Pull request overview

Note

Copilot was unable to run its full agentic suite in this review.

This PR updates neuron and synapse templates to better reflect subtype-specific dynamics and integration constraints.

Changes:

  • Implement subtype-specific p/q gating dynamics in VavoulisModel (including N2v dependence on axonal voltage va).
  • Change electrical coupling conductance inputs in eval() from state variables to parameters (g_ecs, g_eca).
  • Update GradualActivationSynapsis template constraints to require an integrator compatible with the serializable system wrapper type.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File Description
models/VavoulisModel.h Adds subtype-aware incr_p/incr_q and switches electrical coupling conductance lookup to params
include/GradualActivationSynapsis.h Tightens integrator concept constraint to match the wrapped/serializable system type

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Comment thread models/VavoulisModel.h
Comment on lines 160 to +167
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 thread models/VavoulisModel.h
Comment on lines +93 to +114
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 55 to 58
requires NeuronConcept<TNode1> && NeuronConcept<TNode2> &&
IntegratorConcept<TIntegrator>
// IntegratorConcept<TIntegrator>
IntegratorConcept<TIntegrator, SerializableWrapper<SystemWrapper<GradualActivationSynapsisModel<precission>>>>
class GradualActivationSynapsis
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2 participants