/*
* iaf_cond_alpha_bias.cpp
*
* This file is part of NEST
*
* Copyright (C) 2005-2009 by
* The NEST Initiative
*
* See the file AUTHORS for details.
*
* Permission is granted to compile and modify
* this file for non-commercial use.
* See the file LICENSE for details.
*
*/
#include "iaf_cond_alpha_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 "universal_data_logger_impl.h"
#include <limits>
#include <iomanip>
#include <iostream>
#include <cstdio>
#include <iostream>
using namespace std;
/* ----------------------------------------------------------------
* Recordables map
* ---------------------------------------------------------------- */
nest::RecordablesMap<mynest::iaf_cond_alpha_bias> mynest::iaf_cond_alpha_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_alpha_bias>::create()
{
// use standard names whereever you can for consistency!
insert_(names::V_m,
&mynest::iaf_cond_alpha_bias::get_y_elem_<mynest::iaf_cond_alpha_bias::State_::V_M>);
insert_(names::g_ex,
&mynest::iaf_cond_alpha_bias::get_y_elem_<mynest::iaf_cond_alpha_bias::State_::G_EXC>);
insert_(names::g_in,
&mynest::iaf_cond_alpha_bias::get_y_elem_<mynest::iaf_cond_alpha_bias::State_::G_INH>);
insert_(Name("z_j"),
&mynest::iaf_cond_alpha_bias::get_y_elem_<mynest::iaf_cond_alpha_bias::State_::Z_J>);
insert_(Name("e_j"),
&mynest::iaf_cond_alpha_bias::get_y_elem_<mynest::iaf_cond_alpha_bias::State_::E_J>);
insert_(Name("p_j"),
&mynest::iaf_cond_alpha_bias::get_y_elem_<mynest::iaf_cond_alpha_bias::State_::P_J>);
insert_(names::t_ref_remaining,
&mynest::iaf_cond_alpha_bias::get_r_);
insert_(Name("bias"),
&mynest::iaf_cond_alpha_bias::get_bias_);
insert_(Name("epsilon"),
&mynest::iaf_cond_alpha_bias::get_epsilon_);
insert_(Name("kappa"),
&mynest::iaf_cond_alpha_bias::get_kappa_);
}
}
/* ----------------------------------------------------------------
* Iteration function
* ---------------------------------------------------------------- */
extern "C"
inline int mynest::iaf_cond_alpha_bias_dynamics(double, const double y[], double f[], void* pnode)
{
// a shorthand
typedef mynest::iaf_cond_alpha_bias::State_ S;
// get access to node so we can almost work as in a member function
assert(pnode);
const mynest::iaf_cond_alpha_bias& node = *(reinterpret_cast<mynest::iaf_cond_alpha_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 nest::double_t I_syn_exc = y[S::G_EXC] * ( y[S::V_M] - node.P_.E_ex );
const nest::double_t I_syn_inh = y[S::G_INH] * ( y[S::V_M] - node.P_.E_in );
const nest::double_t I_leak = node.P_.g_L * ( y[S::V_M] - node.P_.E_L );
const nest::double_t I_bias = node.P_.gain * std::log(y[S::P_J]);
// dV_m/dt
f[0] = (-I_leak - I_syn_exc - I_syn_inh + node.B_.I_stim_ + node.P_.I_e + I_bias) / node.P_.C_m;
// d dg_exc/dt, dg_exc/dt
f[1] = -y[S::DG_EXC] / node.P_.tau_synE;
f[2] = y[S::DG_EXC] - (y[S::G_EXC]/node.P_.tau_synE);
// d dg_exc/dt, dg_exc/dt
f[3] = -y[S::DG_INH] / node.P_.tau_synI;
f[4] = y[S::DG_INH] - (y[S::G_INH]/node.P_.tau_synI);
f[5] = (- y[S::Z_J] + node.P_.epsilon) / node.P_.tau_j;
f[6] = (y[S::Z_J] - y[S::E_J]) / node.P_.tau_e;
f[7] = node.P_.kappa * (y[S::E_J] - y[S::P_J]) / node.P_.tau_p;
return GSL_SUCCESS;
}
/* ----------------------------------------------------------------
* Default constructors defining default parameters and state
* ---------------------------------------------------------------- */
mynest::iaf_cond_alpha_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
kappa (1.0 ), // dopamine
fmax (20.0 ),
gain (1.0 ),
bias (0.0 ),
epsilon (0.001 )
{
recordablesMap_.create();
}
mynest::iaf_cond_alpha_bias::State_::State_(const Parameters_& p)
: r(0), bias(0)
{
y[V_M] = p.E_L; // initialize to reversal potential
for ( size_t i = 1 ; i < 5 ; ++i )
y[i] = 0;
y[Z_J] = 0.01;
y[E_J] = 0.01;
y[P_J] = 0.01;
//y[BIAS] = 0.0;
}
mynest::iaf_cond_alpha_bias::State_::State_(const State_& s)
: r(s.r),bias(s.bias)
{
for ( size_t i = 0 ; i < STATE_VEC_SIZE ; ++i )
y[i] = s.y[i];
}
mynest::iaf_cond_alpha_bias::State_& mynest::iaf_cond_alpha_bias::State_::operator=(const State_& s)
{
if ( this == &s ) // avoid assignment to self
return *this;
for ( size_t i = 0 ; i < STATE_VEC_SIZE ; ++i )
y[i] = s.y[i];
r = s.r;
return *this;
}
mynest::iaf_cond_alpha_bias::Buffers_::Buffers_(iaf_cond_alpha_bias& n)
: logger_(n),
s_(0),
c_(0),
e_(0)
{
// Initialization of the remaining members is deferred to
// init_buffers_().
}
mynest::iaf_cond_alpha_bias::Buffers_::Buffers_(const Buffers_&, iaf_cond_alpha_bias& n)
: logger_(n),
s_(0),
c_(0),
e_(0)
{
// Initialization of the remaining members is deferred to
// init_buffers_().
}
/* ----------------------------------------------------------------
* Parameter and state extractions and manipulation functions
* ---------------------------------------------------------------- */
void mynest::iaf_cond_alpha_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, "kappa", kappa);
def<nest::double_t>(dd, "bias", bias);
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_alpha_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, "kappa", kappa);
updateValue<nest::double_t>(dd, "gain", gain);
updateValue<nest::double_t>(dd, "bias", bias);
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_alpha_bias::State_::get(DictionaryDatum &dd) const
{
def<double>(dd, nest::names::V_m, y[V_M]); // Membrane potential
}
void mynest::iaf_cond_alpha_bias::State_::set(const DictionaryDatum& dd, const Parameters_&)
{
updateValue<double>(dd, nest::names::V_m, y[V_M]);
}
/* ----------------------------------------------------------------
* Default and copy constructor for node, and destructor
* ---------------------------------------------------------------- */
mynest::iaf_cond_alpha_bias::iaf_cond_alpha_bias()
: Archiving_Node(),
P_(),
S_(P_),
B_(*this)
{
recordablesMap_.create();
}
mynest::iaf_cond_alpha_bias::iaf_cond_alpha_bias(const iaf_cond_alpha_bias& n)
: Archiving_Node(n),
P_(n.P_),
S_(n.S_),
B_(n.B_, *this)
{
}
mynest::iaf_cond_alpha_bias::~iaf_cond_alpha_bias()
{
// GSL structs only allocated by init_nodes_(), 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_alpha_bias::init_node_(const Node& proto)
{
const iaf_cond_alpha_bias& pr = downcast<iaf_cond_alpha_bias>(proto);
P_ = pr.P_;
S_ = pr.S_;
}
void mynest::iaf_cond_alpha_bias::init_state_(const Node& proto)
{
const iaf_cond_alpha_bias& pr = downcast<iaf_cond_alpha_bias>(proto);
S_ = pr.S_;
}
void mynest::iaf_cond_alpha_bias::init_buffers_()
{
Archiving_Node::clear_history();
B_.spike_exc_.clear(); // includes resize
B_.spike_inh_.clear(); // includes resize
B_.currents_.clear(); // includes resize
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_alpha_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_alpha_bias::calibrate()
{
B_.logger_.init(); // ensures initialization in case mm connected after Simulate
V_.PSConInit_E = 1.0 * numerics::e / P_.tau_synE;
V_.PSConInit_I = 1.0 * numerics::e / P_.tau_synI;
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_alpha_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);
}
// refractoriness and spike generation
if ( S_.r )
{// neuron is absolute refractory
--S_.r;
S_.y[State_::V_M] = P_.V_reset; // clamp potential
}
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; /* 10k = 1000 * 10 timesteps... */
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_.kappa * (S_.y[State_::E_J] - S_.y[State_::P_J]) * B_.step_ / P_.tau_p;
// log spike with Archiving_Node
set_spiketime(nest::Time::step(origin.get_steps()+lag+1));
nest::SpikeEvent se;
network()->send(*this, se, lag);
}
// add incoming spikes
S_.y[State_::DG_EXC] += B_.spike_exc_.get_value(lag) * V_.PSConInit_E;
S_.y[State_::DG_INH] += B_.spike_inh_.get_value(lag) * V_.PSConInit_I;
S_.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_alpha_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_alpha_bias::handle(nest::CurrentEvent& e)
{
assert(e.get_delay() > 0);
// add weighted current; HEP 2002-10-04
B_.currents_.add_value(e.get_rel_delivery_steps(network()->get_slice_origin()),
e.get_weight() * e.get_current() );
}
void mynest::iaf_cond_alpha_bias::handle(nest::DataLoggingRequest& e)
{
B_.logger_.handle(e);
}
#endif //HAVE_GSL
