/*
* iaf_cond_exp_bias.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 "iaf_cond_exp_bias.h"
#ifdef HAVE_GSL
#include "exceptions.h"
#include "network.h"
#include "dict.h"
#include "integerdatum.h"
#include "doubledatum.h"
#include "dictutils.h"
#include "numerics.h"
#include <limits>
#include "universal_data_logger_impl.h"
#include "event.h"
#include <iomanip>
#include <iostream>
using namespace std;
#include <cstdio>
/* ----------------------------------------------------------------
* Recordables map
* ---------------------------------------------------------------- */
nest::RecordablesMap<mynest::iaf_cond_exp_bias> mynest::iaf_cond_exp_bias::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<mynest::iaf_cond_exp_bias>::create()
{
// use standard names whereever you can for consistency!
insert_(names::V_m,
&mynest::iaf_cond_exp_bias::get_y_elem_<mynest::iaf_cond_exp_bias::State_::V_M>);
insert_(names::g_ex,
&mynest::iaf_cond_exp_bias::get_y_elem_<mynest::iaf_cond_exp_bias::State_::G_EXC>);
insert_(names::g_in,
&mynest::iaf_cond_exp_bias::get_y_elem_<mynest::iaf_cond_exp_bias::State_::G_INH>);
insert_(Name("z_j"),
&mynest::iaf_cond_exp_bias::get_y_elem_<mynest::iaf_cond_exp_bias::State_::Z_J>);
insert_(Name("e_j"),
&mynest::iaf_cond_exp_bias::get_y_elem_<mynest::iaf_cond_exp_bias::State_::E_J>);
insert_(Name("p_j"),
&mynest::iaf_cond_exp_bias::get_y_elem_<mynest::iaf_cond_exp_bias::State_::P_J>);
insert_(Name("I_bias"),
&mynest::iaf_cond_exp_bias::get_y_elem_<mynest::iaf_cond_exp_bias::State_::I_BIAS>);
insert_(Name("epsilon"),
&mynest::iaf_cond_exp_bias::get_epsilon_);
insert_(Name("K"),
&mynest::iaf_cond_exp_bias::get_K_);
}
}
extern "C"
inline int mynest::iaf_cond_exp_bias_dynamics(double, const double y[], double f[], void* pnode)
{
// a shorthand
typedef mynest::iaf_cond_exp_bias::State_ S;
// get access to node so we can almost work as in a member function
assert(pnode);
const mynest::iaf_cond_exp_bias& node = *(reinterpret_cast<mynest::iaf_cond_exp_bias*>(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 ...
const double I_syn_exc = y[S::G_EXC] * (y[S::V_M] - node.P_.E_ex);
const double I_syn_inh = y[S::G_INH] * (y[S::V_M] - node.P_.E_in);
const double I_L = node.P_.g_L * ( y[S::V_M] - node.P_.E_L );
//V dot
f[0]= ( - I_L + node.B_.I_stim_ + node.P_.I_e - I_syn_exc - I_syn_inh + y[S::I_BIAS]) / node.P_.C_m;
f[1] = -y[S::G_EXC] / node.P_.tau_synE;
f[2] = -y[S::G_INH] / node.P_.tau_synI;
f[3] = (- y[S::Z_J] + node.P_.epsilon) / node.P_.tau_j;
f[4] = (y[S::Z_J] - y[S::E_J]) / node.P_.tau_e;
f[5] = node.P_.K * (y[S::E_J] - y[S::P_J]) / node.P_.tau_p;
f[6] = node.P_.gain * std::log(y[S::P_J]);
return GSL_SUCCESS;
}
/* ----------------------------------------------------------------
* Default constructors defining default parameters and state
* ---------------------------------------------------------------- */
mynest::iaf_cond_exp_bias::Parameters_::Parameters_()
: V_th_ (-55.0 ), // mV
V_reset_ (-60.0 ), // mV
t_ref_ ( 2.0 ), // ms
g_L ( 16.6667 ), // nS
C_m (250.0 ), // pF
E_ex ( 0.0 ), // mV
E_in (-85.0 ), // mV
E_L (-70.0 ), // mV
tau_synE ( 0.2 ), // ms
tau_synI ( 2.0 ), // ms
I_e ( 0.0 ), // pA
tau_j ( 10.0 ), // ms
tau_e (100.0 ), // ms
tau_p (1000.0 ), // ms
K (1.0 ), // dopamine
fmax (20.0 ),
gain (1.0 ),
epsilon (0.001 )
{
}
mynest::iaf_cond_exp_bias::State_::State_(const Parameters_& p)
: r_(0)
{
y_[V_M] = p.E_L;
y_[G_EXC] = y_[G_INH] = 0;
y_[Z_J] = 0.01;
y_[E_J] = 0.01;
y_[P_J] = 0.01;
y_[I_BIAS] = 0.0;
}
mynest::iaf_cond_exp_bias::State_::State_(const State_& s)
: r_(s.r_)
{
for ( size_t i = 0 ; i < STATE_VEC_SIZE ; ++i )
y_[i] = s.y_[i];
}
mynest::iaf_cond_exp_bias::State_& mynest::iaf_cond_exp_bias::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 mynest::iaf_cond_exp_bias::Parameters_::get(DictionaryDatum &dd) const
{
def<double>(dd,nest::names::V_th, V_th_);
def<double>(dd,nest::names::V_reset, V_reset_);
def<double>(dd,nest::names::t_ref, t_ref_);
def<double>(dd,nest::names::g_L, g_L);
def<double>(dd,nest::names::E_L, E_L);
def<double>(dd,nest::names::E_ex, E_ex);
def<double>(dd,nest::names::E_in, E_in);
def<double>(dd,nest::names::C_m, C_m);
def<double>(dd,nest::names::tau_syn_ex, tau_synE);
def<double>(dd,nest::names::tau_syn_in, tau_synI);
def<double>(dd,nest::names::I_e, I_e);
def<nest::double_t>(dd, "tau_j", tau_j);
def<nest::double_t>(dd, "tau_e", tau_e);
def<nest::double_t>(dd, "tau_p", tau_p);
def<nest::double_t>(dd, "K", K);
def<nest::double_t>(dd, "gain", gain);
def<nest::double_t>(dd, "fmax", fmax);
def<nest::double_t>(dd, "epsilon", epsilon);
}
void mynest::iaf_cond_exp_bias::Parameters_::set(const DictionaryDatum& dd)
{
// allow setting the membrane potential
updateValue<double>(dd,nest::names::V_th, V_th_);
updateValue<double>(dd,nest::names::V_reset, V_reset_);
updateValue<double>(dd,nest::names::t_ref, t_ref_);
updateValue<double>(dd,nest::names::E_L, E_L);
updateValue<double>(dd,nest::names::E_ex, E_ex);
updateValue<double>(dd,nest::names::E_in, E_in);
updateValue<double>(dd,nest::names::C_m, C_m);
updateValue<double>(dd,nest::names::g_L, g_L);
updateValue<double>(dd,nest::names::tau_syn_ex, tau_synE);
updateValue<double>(dd,nest::names::tau_syn_in, tau_synI);
updateValue<double>(dd,nest::names::I_e, I_e);
updateValue<nest::double_t>(dd, "tau_j", tau_j);
updateValue<nest::double_t>(dd, "tau_e", tau_e);
updateValue<nest::double_t>(dd, "tau_p", tau_p);
updateValue<nest::double_t>(dd, "K", K);
updateValue<nest::double_t>(dd, "gain", gain);
updateValue<nest::double_t>(dd, "fmax", fmax);
updateValue<nest::double_t>(dd, "epsilon", epsilon);
if ( V_reset_ >= V_th_ )
throw nest::BadProperty("Reset potential must be smaller than threshold.");
if ( C_m <= 0 )
throw nest::BadProperty("Capacitance must be strictly positive.");
if ( t_ref_ < 0 )
throw nest::BadProperty("Refractory time cannot be negative.");
if ( tau_synE <= 0 || tau_synI <= 0 )
throw nest::BadProperty("All time constants must be strictly positive.");
}
void mynest::iaf_cond_exp_bias::State_::get(DictionaryDatum &dd) const
{
def<double>(dd, nest::names::V_m, y_[V_M]); // Membrane potential
def<double>(dd, "I_bias", y_[I_BIAS]);
}
void mynest::iaf_cond_exp_bias::State_::set(const DictionaryDatum& dd, const Parameters_&)
{
updateValue<double>(dd, nest::names::V_m, y_[V_M]);
updateValue<double>(dd, "I_bias", y_[I_BIAS]);
}
mynest::iaf_cond_exp_bias::Buffers_::Buffers_(iaf_cond_exp_bias& n)
: logger_(n),
s_(0),
c_(0),
e_(0)
{
// Initialization of the remaining members is deferred to
// init_buffers_().
}
mynest::iaf_cond_exp_bias::Buffers_::Buffers_(const Buffers_&, iaf_cond_exp_bias& 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
* ---------------------------------------------------------------- */
mynest::iaf_cond_exp_bias::iaf_cond_exp_bias()
: Archiving_Node(),
P_(),
S_(P_),
B_(*this)
{
recordablesMap_.create();
}
mynest::iaf_cond_exp_bias::iaf_cond_exp_bias(const iaf_cond_exp_bias& n)
: Archiving_Node(n),
P_(n.P_),
S_(n.S_),
B_(n.B_, *this)
{
}
mynest::iaf_cond_exp_bias::~iaf_cond_exp_bias()
{
// 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 mynest::iaf_cond_exp_bias::init_state_(const Node& proto)
{
const iaf_cond_exp_bias& pr = downcast<iaf_cond_exp_bias>(proto);
S_ = pr.S_;
}
void mynest::iaf_cond_exp_bias::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_ = nest::Time::get_resolution().get_ms();
B_.IntegrationStep_ = 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_y_new (1e-3, 0.0);
else
gsl_odeiv_control_init(B_.c_, 1e-3, 0.0, 1.0, 0.0);
if ( B_.e_ == 0 )
B_.e_ = gsl_odeiv_evolve_alloc(State_::STATE_VEC_SIZE);
else
gsl_odeiv_evolve_reset(B_.e_);
B_.sys_.function = iaf_cond_exp_bias_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 mynest::iaf_cond_exp_bias::calibrate()
{
B_.logger_.init(); // ensures initialization in case mm connected after Simulate
V_.RefractoryCounts_ = nest::Time(nest::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 mynest::iaf_cond_exp_bias::update(nest::Time const & origin, const nest::long_t from, const nest::long_t to)
{
assert(to >= 0 && (nest::delay) from < nest::Scheduler::get_min_delay());
assert(from < to);
for ( nest::long_t lag = from ; lag < to ; ++lag )
{
double t = 0.0;
// 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 nest::GSLSolverFailure(get_name(), status);
}
S_.y_[State_::I_BIAS] = P_.gain * std::log(S_.y_[State_::P_J]);
S_.y_[State_::G_EXC] += B_.spike_exc_.get_value(lag);
S_.y_[State_::G_INH] += B_.spike_inh_.get_value(lag);
// absolute refractory period
if ( S_.r_ )
{// neuron is absolute refractory
--S_.r_;
S_.y_[State_::V_M] = P_.V_reset_;
}
else
// neuron is not absolute refractory
if ( S_.y_[State_::V_M] >= P_.V_th_ )
{
S_.r_ = V_.RefractoryCounts_;
S_.y_[State_::V_M] = P_.V_reset_;
S_.y_[State_::Z_J] += (1000.0/(P_.fmax*B_.step_) - S_.y_[State_::Z_J] + P_.epsilon) * B_.step_ / P_.tau_j;
S_.y_[State_::E_J] += (S_.y_[State_::Z_J] - S_.y_[State_::E_J]) * B_.step_ / P_.tau_e;
S_.y_[State_::P_J] += P_.K * (S_.y_[State_::E_J] - S_.y_[State_::P_J]) * B_.step_ / P_.tau_p;
set_spiketime(nest::Time::step(origin.get_steps()+lag+1));
nest::SpikeEvent se;
network()->send(*this, se, lag);
}
S_.y_[State_::I_BIAS] = P_.gain * std::log(S_.y_[State_::P_J]);
// set new input current
B_.I_stim_ = B_.currents_.get_value(lag);
// log state data
B_.logger_.record_data(origin.get_steps() + lag);
}
}
void mynest::iaf_cond_exp_bias::handle(nest::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() ); // ensure conductance is positive
}
void mynest::iaf_cond_exp_bias::handle(nest::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 mynest::iaf_cond_exp_bias::handle(nest::DataLoggingRequest& e)
{
B_.logger_.handle(e);
}
#endif //HAVE_GSL
