/*
* aeif_cond_alpha.cpp
*
* This file is part of NEST.
*
* Copyright (C) 2004 The NEST Initiative
*
* NEST 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 2 of the License, or
* (at your option) any later version.
*
* NEST 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 NEST. If not, see <http://www.gnu.org/licenses/>.
*
*/
#include "aeif_cond_alpha.h"
#include "nest_names.h"
#ifdef HAVE_GSL_1_11
#include "universal_data_logger_impl.h"
#include "exceptions.h"
#include "network.h"
#include "dict.h"
#include "integerdatum.h"
#include "doubledatum.h"
#include "dictutils.h"
#include "numerics.h"
#include <limits>
#include <cmath>
#include <iomanip>
#include <iostream>
#include <cstdio>
/* ----------------------------------------------------------------
* Recordables map
* ---------------------------------------------------------------- */
nest::RecordablesMap<nest::aeif_cond_alpha> nest::aeif_cond_alpha::recordablesMap_;
namespace nest // template specialization must be placed in namespace
{
// Override the create() method with one call to RecordablesMap::insert_()
// for each quantity to be recorded.
template <>
void RecordablesMap<aeif_cond_alpha>::create()
{
// use standard names whereever you can for consistency!
insert_(names::V_m,
&aeif_cond_alpha::get_y_elem_<aeif_cond_alpha::State_::V_M>);
insert_(names::g_ex,
&aeif_cond_alpha::get_y_elem_<aeif_cond_alpha::State_::G_EXC>);
insert_(names::g_in,
&aeif_cond_alpha::get_y_elem_<aeif_cond_alpha::State_::G_INH>);
insert_(names::w,
&aeif_cond_alpha::get_y_elem_<aeif_cond_alpha::State_::W>);
}
}
extern "C"
int nest::aeif_cond_alpha_dynamics (double, const double y[], double f[], void* pnode)
{
// a shorthand
typedef nest::aeif_cond_alpha::State_ S;
// get access to node so we can almost work as in a member function
assert(pnode);
const nest::aeif_cond_alpha& node = *(reinterpret_cast<nest::aeif_cond_alpha*>(pnode));
// y[] here is---and must be---the state vector supplied by the integrator,
// not the state vector in the node, node.S_.y[].
// The following code is verbose for the sake of clarity. We assume that a
// good compiler will optimize the verbosity away ...
// This constant is used below as the largest admissible value for the exponential spike upstroke
static const double_t largest_exp=std::exp(10.);
// shorthand for state variables
const double_t& V = y[S::V_M];
const double_t& dg_ex = y[S::DG_EXC];
const double_t& g_ex = y[S::G_EXC ];
const double_t& dg_in = y[S::DG_INH];
const double_t& g_in = y[S::G_INH ];
const double_t& w = y[S::W];
const double_t I_syn_exc = g_ex * (V - node.P_.E_ex);
const double_t I_syn_inh = g_in * (V - node.P_.E_in);
// We pre-compute the argument of the exponential
const double_t exp_arg=(V - node.P_.V_th) / node.P_.Delta_T;
// If the argument is too large, we clip it.
const double_t I_spike = (exp_arg>10.)? largest_exp : node.P_.Delta_T * std::exp(exp_arg);
// dv/dt
f[S::V_M ] = ( -node.P_.g_L *( (V-node.P_.E_L) - I_spike )
- I_syn_exc - I_syn_inh - w + node.P_.I_e + node.B_.I_stim_) / node.P_.C_m;
f[S::DG_EXC] = -dg_ex / node.P_.tau_syn_ex;
f[S::G_EXC ] = dg_ex - g_ex / node.P_.tau_syn_ex; // Synaptic Conductance (nS)
f[S::DG_INH] = -dg_in / node.P_.tau_syn_in;
f[S::G_INH ] = dg_in - g_in / node.P_.tau_syn_in; // Synaptic Conductance (nS)
// Adaptation current w.
f[S::W ] = ( node.P_.a * (V - node.P_.E_L) - w ) / node.P_.tau_w;
return GSL_SUCCESS;
}
/* ----------------------------------------------------------------
* Default constructors defining default parameters and state
* ---------------------------------------------------------------- */
nest::aeif_cond_alpha::Parameters_::Parameters_()
: V_peak_ ( 0.0 ), // mV, should not be larger that V_th+10
V_reset_ (-60.0 ), // mV
t_ref_ ( 0.0 ), // ms
g_L ( 30.0 ), // nS
C_m (281.0 ), // pF
E_ex ( 0.0 ), // mV
E_in (-85.0 ), // mV
E_L (-70.6 ), // mV
Delta_T ( 2.0 ), // mV
tau_w (144.0 ), // ms
a ( 4.0 ), // nS
b ( 80.5 ), // pA
V_th (-50.4 ), // mV
tau_syn_ex ( 0.2 ), // ms
tau_syn_in ( 2.0 ), // ms
I_e ( 0.0 ), // pA
gsl_error_tol( 1e-6 )
{
}
nest::aeif_cond_alpha::State_::State_(const Parameters_& p)
: r_(0)
{
y_[0] = p.E_L;
for ( size_t i = 1 ; i < STATE_VEC_SIZE ; ++i )
y_[i] = 0;
}
nest::aeif_cond_alpha::State_::State_(const State_& s)
: r_(s.r_)
{
for ( size_t i = 0 ; i < STATE_VEC_SIZE ; ++i )
y_[i] = s.y_[i];
}
nest::aeif_cond_alpha::State_& nest::aeif_cond_alpha::State_::operator=(const State_& s)
{
assert(this != &s); // would be bad logical error in program
for ( size_t i = 0 ; i < STATE_VEC_SIZE ; ++i )
y_[i] = s.y_[i];
r_ = s.r_;
return *this;
}
/* ----------------------------------------------------------------
* Parameter and state extractions and manipulation functions
* ---------------------------------------------------------------- */
void nest::aeif_cond_alpha::Parameters_::get(DictionaryDatum &d) const
{
def<double>(d,names::C_m, C_m);
def<double>(d,names::V_th, V_th);
def<double>(d,names::t_ref, t_ref_);
def<double>(d,names::g_L, g_L);
def<double>(d,names::E_L, E_L);
def<double>(d,names::V_reset,V_reset_);
def<double>(d,names::E_ex, E_ex);
def<double>(d,names::E_in, E_in);
def<double>(d,names::tau_syn_ex, tau_syn_ex);
def<double>(d,names::tau_syn_in, tau_syn_in);
def<double>(d,names::a, a);
def<double>(d,names::b, b);
def<double>(d,names::Delta_T,Delta_T);
def<double>(d,names::tau_w, tau_w);
def<double>(d,names::I_e, I_e);
def<double>(d,names::V_peak, V_peak_);
def<double>(d,names::gsl_error_tol, gsl_error_tol);
}
void nest::aeif_cond_alpha::Parameters_::set(const DictionaryDatum& d)
{
updateValue<double>(d,names::V_th, V_th);
updateValue<double>(d,names::V_peak, V_peak_);
updateValue<double>(d,names::t_ref, t_ref_);
updateValue<double>(d,names::E_L, E_L);
updateValue<double>(d,names::V_reset, V_reset_);
updateValue<double>(d,names::E_ex, E_ex);
updateValue<double>(d,names::E_in, E_in);
updateValue<double>(d,names::C_m, C_m);
updateValue<double>(d,names::g_L, g_L);
updateValue<double>(d,names::tau_syn_ex, tau_syn_ex);
updateValue<double>(d,names::tau_syn_in, tau_syn_in);
updateValue<double>(d,names::a, a);
updateValue<double>(d,names::b, b);
updateValue<double>(d,names::Delta_T,Delta_T);
updateValue<double>(d,names::tau_w, tau_w);
updateValue<double>(d,names::I_e, I_e);
updateValue<double>(d,names::gsl_error_tol, gsl_error_tol);
if ( V_peak_ <= V_th )
throw BadProperty("V_peak must be larger than threshold.");
if ( V_reset_ >= V_peak_ )
throw BadProperty("Ensure that: V_reset < V_peak .");
if ( C_m <= 0 )
{
throw BadProperty("Capacitance must be strictly positive.");
}
if ( t_ref_ < 0 )
throw BadProperty("Refractory time cannot be negative.");
if ( tau_syn_ex <= 0 || tau_syn_in <= 0 || tau_w <= 0 )
throw BadProperty("All time constants must be strictly positive.");
if ( gsl_error_tol <= 0. )
throw BadProperty("The gsl_error_tol must be strictly positive.");
}
void nest::aeif_cond_alpha::State_::get(DictionaryDatum &d) const
{
def<double>(d,names::V_m, y_[V_M]);
def<double>(d,names::g_ex, y_[G_EXC]);
def<double>(d,names::dg_ex, y_[DG_EXC]);
def<double>(d,names::g_in, y_[G_INH]);
def<double>(d,names::dg_in, y_[DG_INH]);
def<double>(d,names::w, y_[W]);
}
void nest::aeif_cond_alpha::State_::set(const DictionaryDatum& d, const Parameters_&)
{
updateValue<double>(d,names::V_m, y_[V_M]);
updateValue<double>(d,names::g_ex, y_[G_EXC]);
updateValue<double>(d,names::dg_ex, y_[DG_EXC]);
updateValue<double>(d,names::g_in, y_[G_INH]);
updateValue<double>(d,names::dg_in, y_[DG_INH]);
updateValue<double>(d,names::w, y_[W]);
if ( y_[G_EXC] < 0 || y_[G_INH] < 0 )
throw BadProperty("Conductances must not be negative.");
}
nest::aeif_cond_alpha::Buffers_::Buffers_(aeif_cond_alpha& n)
: logger_(n),
s_(0),
c_(0),
e_(0)
{
// Initialization of the remaining members is deferred to
// init_buffers_().
}
nest::aeif_cond_alpha::Buffers_::Buffers_(const Buffers_&, aeif_cond_alpha& n)
: logger_(n),
s_(0),
c_(0),
e_(0)
{
// Initialization of the remaining members is deferred to
// init_buffers_().
}
/* ----------------------------------------------------------------
* Default and copy constructor for node, and destructor
* ---------------------------------------------------------------- */
nest::aeif_cond_alpha::aeif_cond_alpha()
: Archiving_Node(),
P_(),
S_(P_),
B_(*this)
{
recordablesMap_.create();
}
nest::aeif_cond_alpha::aeif_cond_alpha(const aeif_cond_alpha& n)
: Archiving_Node(n),
P_(n.P_),
S_(n.S_),
B_(n.B_, *this)
{
}
nest::aeif_cond_alpha::~aeif_cond_alpha()
{
// GSL structs may not have been allocated, so we need to protect destruction
if ( B_.s_ ) gsl_odeiv_step_free(B_.s_);
if ( B_.c_ ) gsl_odeiv_control_free(B_.c_);
if ( B_.e_ ) gsl_odeiv_evolve_free(B_.e_);
}
/* ----------------------------------------------------------------
* Node initialization functions
* ---------------------------------------------------------------- */
void nest::aeif_cond_alpha::init_state_(const Node& proto)
{
const aeif_cond_alpha& pr = downcast<aeif_cond_alpha>(proto);
S_ = pr.S_;
}
void nest::aeif_cond_alpha::init_buffers_()
{
B_.spike_exc_.clear(); // includes resize
B_.spike_inh_.clear(); // includes resize
B_.currents_.clear(); // includes resize
Archiving_Node::clear_history();
B_.logger_.reset();
B_.step_ = Time::get_resolution().get_ms();
// We must integrate this model with high-precision to obtain decent results
B_.IntegrationStep_ = std::min(0.01, B_.step_);
static const gsl_odeiv_step_type* T1 = gsl_odeiv_step_rkf45;
if ( B_.s_ == 0 )
B_.s_ = gsl_odeiv_step_alloc (T1, State_::STATE_VEC_SIZE);
else
gsl_odeiv_step_reset(B_.s_);
if ( B_.c_ == 0 )
B_.c_ = gsl_odeiv_control_yp_new (P_.gsl_error_tol, P_.gsl_error_tol);
else
gsl_odeiv_control_init(B_.c_, P_.gsl_error_tol, P_.gsl_error_tol, 0.0, 1.0);
if ( B_.e_ == 0 )
B_.e_ = gsl_odeiv_evolve_alloc(State_::STATE_VEC_SIZE);
else
gsl_odeiv_evolve_reset(B_.e_);
B_.sys_.function = aeif_cond_alpha_dynamics;
B_.sys_.jacobian = NULL;
B_.sys_.dimension = State_::STATE_VEC_SIZE;
B_.sys_.params = reinterpret_cast<void*>(this);
B_.I_stim_ = 0.0;
}
void nest::aeif_cond_alpha::calibrate()
{
B_.logger_.init(); // ensures initialization in case mm connected after Simulate
V_.g0_ex_ = 1.0 * numerics::e / P_.tau_syn_ex;
V_.g0_in_ = 1.0 * numerics::e / P_.tau_syn_in;
V_.RefractoryCounts_ = Time(Time::ms(P_.t_ref_)).get_steps();
assert(V_.RefractoryCounts_ >= 0); // since t_ref_ >= 0, this can only fail in error
}
/* ----------------------------------------------------------------
* Update and spike handling functions
* ---------------------------------------------------------------- */
void nest::aeif_cond_alpha::update(Time const & origin, const long_t from, const long_t to)
{
assert ( to >= 0 && (delay) from < Scheduler::get_min_delay() );
assert ( from < to );
assert ( State_::V_M == 0 );
for ( long_t lag = from; lag < to; ++lag )
{
double t = 0.0;
if ( S_.r_ > 0 )
--S_.r_;
// numerical integration with adaptive step size control:
// ------------------------------------------------------
// gsl_odeiv_evolve_apply performs only a single numerical
// integration step, starting from t and bounded by step;
// the while-loop ensures integration over the whole simulation
// step (0, step] if more than one integration step is needed due
// to a small integration step size;
// note that (t+IntegrationStep > step) leads to integration over
// (t, step] and afterwards setting t to step, but it does not
// enforce setting IntegrationStep to step-t; this is of advantage
// for a consistent and efficient integration across subsequent
// simulation intervals
while ( t < B_.step_ )
{
const int status = gsl_odeiv_evolve_apply(B_.e_, B_.c_, B_.s_,
&B_.sys_, // system of ODE
&t, // from t
B_.step_, // to t <= step
&B_.IntegrationStep_, // integration step size
S_.y_); // neuronal state
if ( status != GSL_SUCCESS )
throw GSLSolverFailure(get_name(), status);
// check for unreasonable values; we allow V_M to explode
if ( S_.y_[State_::V_M] < -1e3 ||
S_.y_[State_::W ] < -1e6 || S_.y_[State_::W] > 1e6 )
throw NumericalInstability(get_name());
// spikes are handled inside the while-loop
// due to spike-driven adaptation
if ( S_.r_ > 0 )
S_.y_[State_::V_M] = P_.V_reset_;
else if ( S_.y_[State_::V_M] >= P_.V_peak_ )
{
S_.y_[State_::V_M] = P_.V_reset_;
S_.y_[State_::W] += P_.b; // spike-driven adaptation
S_.r_ = V_.RefractoryCounts_;
set_spiketime(Time::step(origin.get_steps() + lag + 1));
SpikeEvent se;
network()->send(*this, se, lag);
}
}
S_.y_[State_::DG_EXC] += B_.spike_exc_.get_value(lag) * V_.g0_ex_;
S_.y_[State_::DG_INH] += B_.spike_inh_.get_value(lag) * V_.g0_in_;
// set new input current
B_.I_stim_ = B_.currents_.get_value(lag);
// log state data
B_.logger_.record_data(origin.get_steps() + lag);
}
}
void nest::aeif_cond_alpha::handle(SpikeEvent & e)
{
assert(e.get_delay() > 0);
if(e.get_weight() > 0.0)
B_.spike_exc_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_multiplicity());
else
B_.spike_inh_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
-e.get_weight() * e.get_multiplicity()); // keep conductances positive
}
void nest::aeif_cond_alpha::handle(CurrentEvent& e)
{
assert(e.get_delay() > 0);
const double_t c=e.get_current();
const double_t w=e.get_weight();
// add weighted current; HEP 2002-10-04
B_.currents_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
w *c);
}
void nest::aeif_cond_alpha::handle(DataLoggingRequest& e)
{
B_.logger_.handle(e);
}
#endif //HAVE_GSL_1_11