void nest::spike_generator::Parameters_::assert_valid_spike_time_and_insert_(double t,
const Time& origin,
const Time& now)
{
Time t_spike;
if ( precise_times_ )
t_spike = Time::ms_stamp(t);
else
{
// In this case, we need to force the spike time to the grid
// First, convert the spike time to tics, may not be on grid
t_spike = Time::ms(t);
if ( not t_spike.is_grid_time() )
{
if ( allow_offgrid_spikes_ )
{
// In this case, we need to round to the end of the step
// in which t lies, ms_stamp does that for us.
t_spike = Time::ms_stamp(t);
}
else
{
std::stringstream msg;
msg << "spike_generator: Time point " << t
<< " is not representable in current resolution.";
throw BadProperty(msg.str());
}
}
assert(t_spike.is_grid_time());
if ( origin + t_spike == now && shift_now_spikes_ )
t_spike.advance();
}
// t_spike is now the correct time stamp given the chosen options
// when we get here, we know that the spike time is valid
spike_stamps_.push_back(t_spike);
if ( precise_times_ )
{
assert(t_spike.get_ms() - t >= 0);
spike_offsets_.push_back(t_spike.get_ms() - t);
}
}
nest::FixedOutDegreeBuilder::FixedOutDegreeBuilder(
const GIDCollection& sources,
const GIDCollection& targets,
const DictionaryDatum& conn_spec,
const DictionaryDatum& syn_spec )
: ConnBuilder( sources, targets, conn_spec, syn_spec )
, outdegree_( ( *conn_spec )[ Name( "outdegree" ) ] )
{
// check for potential errors
// verify that outdegree is not larger than target population if multapses are
// disabled
if ( not multapses_ )
{
if ( ( outdegree_ > static_cast< long >( targets_->size() ) ) )
throw BadProperty( "Outdegree cannot be larger than population size." );
}
}
void
Node::set_status_base( const DictionaryDatum& dict )
{
assert( dict.valid() );
try
{
set_status( dict );
}
catch ( BadProperty& e )
{
throw BadProperty( String::compose(
"Setting status of a '%1' with GID %2: %3", get_name(), get_gid(), e.message() ) );
}
updateValue< bool >( dict, names::frozen, frozen_ );
updateValue< bool >( dict, names::needs_prelim_update, needs_prelim_up_ );
}
// finds threshpassing
inline nest::double_t
nest::iaf_psc_alpha_canon::thresh_find_( double_t const dt ) const
{
switch ( P_.Interpol_ )
{
case NO_INTERPOL:
return dt;
case LINEAR:
return thresh_find1_( dt );
case QUADRATIC:
return thresh_find2_( dt );
case CUBIC:
return thresh_find3_( dt );
default:
throw BadProperty( "Invalid interpolation order in iaf_psc_alpha_canon." );
}
return 0;
}
void
ConnectionManager::set_prototype_status( synindex syn_id, const DictionaryDatum& d )
{
assert_valid_syn_id( syn_id );
for ( thread t = 0; t < net_.get_num_threads(); ++t )
{
try
{
prototypes_[ t ][ syn_id ]->set_status( d );
}
catch ( BadProperty& e )
{
throw BadProperty( String::compose( "Setting status of prototype '%1': %2",
prototypes_[ t ][ syn_id ]->get_name(),
e.message() ) );
}
}
}
double nest::iaf_psc_exp_ps::Parameters_::set(const DictionaryDatum & d)
{
// if E_L_ is changed, we need to adjust all variables defined relative to E_L_
const double ELold = E_L_;
updateValue<double>(d, names::E_L, E_L_);
const double delta_EL = E_L_ - ELold;
updateValue<double>(d, names::tau_m, tau_m_);
updateValue<double>(d, names::tau_syn_ex, tau_ex_);
updateValue<double>(d, names::tau_syn_in, tau_in_);
updateValue<double>(d, names::C_m, c_m_);
updateValue<double>(d, names::t_ref, t_ref_);
updateValue<double>(d, names::I_e, I_e_);
if ( updateValue<double>(d, names::V_th, U_th_) )
U_th_ -= E_L_;
else
U_th_ -= delta_EL;
if ( updateValue<double>(d, names::V_min, U_min_) )
U_min_ -= E_L_;
else
U_min_ -= delta_EL;
if ( updateValue<double>(d, names::V_reset, U_reset_) )
U_reset_ -= E_L_;
else
U_reset_ -= delta_EL;
if ( U_reset_ >= U_th_ )
throw BadProperty("Reset potential must be smaller than threshold.");
if ( U_reset_ < U_min_ )
throw BadProperty("Reset potential must be greater equal minimum potential.");
if ( c_m_ <= 0 )
throw BadProperty("Capacitance must be strictly positive.");
if ( Time(Time::ms(t_ref_)).get_steps() < 1 )
throw BadProperty("Refractory time must be at least one time step.");
if ( tau_m_ <= 0 || tau_ex_ <= 0 || tau_in_ <= 0 )
throw BadProperty("All time constants must be strictly positive.");
if ( tau_m_ == tau_ex_ || tau_m_ == tau_in_ )
throw BadProperty("Membrane and synapse time constant(s) must differ."
"See note in documentation.");
return delta_EL;
}
static AbstractMask*
create_doughnut( const DictionaryDatum& d )
{
// The doughnut (actually an annulus) is created using a DifferenceMask
Position< 2 > center( 0, 0 );
if ( d->known( names::anchor ) )
center = getValue< std::vector< double > >( d, names::anchor );
const double outer = getValue< double >( d, names::outer_radius );
const double inner = getValue< double >( d, names::inner_radius );
if ( inner >= outer )
throw BadProperty(
"topology::create_doughnut: "
"inner_radius < outer_radius required." );
BallMask< 2 > outer_circle( center, outer );
BallMask< 2 > inner_circle( center, inner );
return new DifferenceMask< 2 >( outer_circle, inner_circle );
}
void
nest::pp_pop_psc_delta::Parameters_::set( const DictionaryDatum& d )
{
updateValue< long >( d, names::N, N_ );
updateValue< double >( d, names::rho_0, rho_0_ );
updateValue< double >( d, names::delta_u, delta_u_ );
updateValue< double >( d, names::len_kernel, len_kernel_ );
updateValue< double >( d, names::I_e, I_e_ );
updateValue< double >( d, names::C_m, c_m_ );
updateValue< double >( d, names::tau_m, tau_m_ );
updateValue< std::vector< double > >( d, names::taus_eta, taus_eta_ );
updateValue< std::vector< double > >( d, names::vals_eta, vals_eta_ );
if ( taus_eta_.size() != vals_eta_.size() )
throw BadProperty( String::compose(
"'taus_eta' and 'vals_eta' need to have the same dimension.\nSize of taus_eta: %1\nSize of "
"vals_eta: %2",
taus_eta_.size(),
vals_eta_.size() ) );
if ( c_m_ <= 0 )
throw BadProperty( "Capacitance must be strictly positive." );
if ( tau_m_ <= 0 )
throw BadProperty( "The time constants must be strictly positive." );
for ( uint_t i = 0; i < taus_eta_.size(); i++ )
{
if ( taus_eta_[ i ] <= 0 )
throw BadProperty( "All time constants must be strictly positive." );
}
if ( N_ <= 0 )
throw BadProperty( "Number of neurons must be positive." );
if ( rho_0_ < 0 )
throw BadProperty( "Rho_0 cannot be negative." );
if ( delta_u_ <= 0 )
throw BadProperty( "Delta_u must be positive." );
}
// finds threshpassing
inline nest::double_t
nest::iaf_psc_alpha_presc::thresh_find_( double_t const dt ) const
{
switch ( P_.Interpol_ )
{
case NO_INTERPOL:
return dt;
case LINEAR:
return thresh_find1_( dt );
case QUADRATIC:
return thresh_find2_( dt );
case CUBIC:
return thresh_find3_( dt );
default:
LOG( M_ERROR,
"iaf_psc_alpha_presc::thresh_find_()",
"Invalid interpolation---Internal model error." );
throw BadProperty();
}
return 0;
}
void STDPDopaCommonProperties::set_status(const DictionaryDatum & d, ConnectorModel &cm)
{
CommonSynapseProperties::set_status(d, cm);
long_t vtgid;
if ( updateValue<long_t>(d, "vt", vtgid) )
{
vt_ = dynamic_cast<volume_transmitter *>(NestModule::get_network().get_node(vtgid));
if(vt_==0)
throw BadProperty("Dopamine source must be volume transmitter");
}
updateValue<double_t>(d, "A_plus", A_plus_);
updateValue<double_t>(d, "A_minus", A_minus_);
updateValue<double_t>(d, "tau_plus", tau_plus_);
updateValue<double_t>(d, "tau_c", tau_c_);
updateValue<double_t>(d, "tau_n", tau_n_);
updateValue<double_t>(d, "b", b_);
updateValue<double_t>(d, "Wmin", Wmin_);
updateValue<double_t>(d, "Wmax", Wmax_);
}
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
ConnectionManager::set_synapse_status( index gid,
synindex syn_id,
port p,
thread tid,
const DictionaryDatum& dict )
{
assert_valid_syn_id( syn_id );
try
{
validate_pointer( connections_[ tid ].get( gid ) )
->set_synapse_status( syn_id, *( prototypes_[ tid ][ syn_id ] ), dict, p );
}
catch ( BadProperty& e )
{
throw BadProperty(
String::compose( "Setting status of '%1' connecting from GID %2 to port %3: %4",
prototypes_[ tid ][ syn_id ]->get_name(),
gid,
p,
e.message() ) );
}
}
void nest::ac_poisson_generator::calibrate()
{
// This check is placed here so that it only gets executed on actual
// instances, not on Model instances during a ResetKernel.
// TODO: Make work for parallel simulation!
if ( network()->get_num_threads() > 1 || network()->get_num_processes() > 1 )
throw BadProperty("ac_poisson_generator is presently not suitable"
" for parallel simulation.");
device_.calibrate();
// time resolution
const double h = Time::get_resolution().get_ms();
V_.sins_.resize(B_.N_osc_);
V_.coss_.resize(B_.N_osc_);
V_.sins_ = std::sin(h * P_.om_); // block elements
V_.coss_ = std::cos(h * P_.om_);
B_.rates_.calibrate();
return;
}
bool nest::ac_poisson_generator::
Parameters_::extract_array_(const DictionaryDatum &d,
const std::string& dname,
std::valarray<double>& data) const
{
if ( d->known(dname) )
{
ArrayDatum *ad = dynamic_cast<ArrayDatum *>((*d)[dname].datum());
if ( ad == 0 )
throw BadProperty();
const size_t nd = ad->size();
data.resize(nd);
for ( size_t n = 0 ; n < nd ; ++n )
{
data[n] = getValue<double>((*ad)[n]);
}
return true;
}
else
return false;
}
/**
* Set properties of this connection from position p in the properties
* array given in dictionary.
*/
void STDP_Tsodyk_Connection_Plus::set_status(const DictionaryDatum & d, index p, ConnectorModel &cm)
{
ConnectionHetWD::set_status(d, p, cm);
set_property<double_t>(d, "Us", p, U_);
set_property<double_t>(d, "tau_pscs", p, tau_psc_);
set_property<double_t>(d, "tau_facs", p, tau_fac_);
set_property<double_t>(d, "tau_recs", p, tau_rec_);
double_t x = x_;
double_t y = y_;
set_property<double_t>(d, "xs", p, x);
set_property<double_t>(d, "ys", p, y);
if (x + y > 1.0)
{
cm.network().message(SLIInterpreter::M_ERROR,
"TsodyksConnection::set_status()",
"x + y must be <= 1.0.");
throw BadProperty();
}
else
{
x_ = x;
y_ = y;
}
set_property<double_t>(d, "us", p, u_);
set_property<double_t>(d, "tau_pluss", p, tau_plus_);
set_property<double_t>(d, "lambdas", p, lambda_);
set_property<double_t>(d, "alphas", p, alpha_);
set_property<double_t>(d, "mu_pluss", p, mu_plus_);
set_property<double_t>(d, "mu_minuss", p, mu_minus_);
set_property<double_t>(d, "Wmaxs", p, Wmax_);
}
void nest::iaf_psc_exp_canon::Parameters_::set(const DictionaryDatum& d)
{
updateValue<double>(d, names::tau_m, tau_m_);
updateValue<double>(d, names::tau_syn_ex, tau_ex_);
updateValue<double>(d, names::tau_syn_in, tau_in_);
updateValue<double>(d, names::C_m, c_m_);
updateValue<double>(d, names::t_ref, t_ref_);
updateValue<double>(d, names::E_L, E_L_);
updateValue<double>(d, names::I_e, I_e_);
if ( updateValue<double>(d, names::V_th, U_th_) )
U_th_ -= E_L_;
if ( updateValue<double>(d, names::V_min, U_min_) )
U_min_ -= E_L_;
if ( updateValue<double>(d, names::V_reset, U_reset_) )
U_reset_ -= E_L_;
long_t tmp;
if ( updateValue<long_t>(d, names::Interpol_Order, tmp) )
if ( NO_INTERPOL <= tmp && tmp < END_INTERP_ORDER )
Interpol_ = static_cast<interpOrder>(tmp);
else
throw BadProperty("Interpolation order must be 0, 1, 2 or 3.");
if ( tau_m_ <= 0 || tau_ex_ <= 0 || tau_in_ <= 0 )
throw BadProperty("All time constants must be strictly positive.");
if ( c_m_ <= 0 )
throw BadProperty("Capacitance must be strictly positive.");
if ( t_ref_ < 0 )
throw BadProperty("Refractory time must not be negative.");
if ( U_reset_ >= U_th_ )
throw BadProperty("Reset potential must be smaller than threshold.");
if ( U_reset_ < U_min_ )
throw BadProperty("Reset potential must be greater equal minimum potential.");
}
void
nest::cg_connect( nest::ConnectionGeneratorDatum& cg,
const index source_id,
const index target_id,
const DictionaryDatum& params_map,
const Name& synmodel_name )
{
Subnet* sources =
dynamic_cast< Subnet* >( kernel().node_manager.get_node( source_id ) );
if ( sources == NULL )
{
LOG( M_ERROR, "CGConnect_cg_i_i_D_l", "sources must be a subnet." );
throw SubnetExpected();
}
if ( !sources->is_homogeneous() )
{
LOG( M_ERROR,
"CGConnect_cg_i_i_D_l",
"sources must be a homogeneous subnet." );
throw BadProperty();
}
if ( dynamic_cast< Subnet* >( *sources->local_begin() ) )
{
LOG( M_ERROR,
"CGConnect_cg_i_i_D_l",
"Only 1-dim subnets are supported as sources." );
throw BadProperty();
}
Subnet* targets =
dynamic_cast< Subnet* >( kernel().node_manager.get_node( target_id ) );
if ( targets == NULL )
{
LOG( M_ERROR, "CGConnect_cg_i_i_D_l", "targets must be a subnet." );
throw SubnetExpected();
}
if ( !targets->is_homogeneous() )
{
LOG( M_ERROR,
"CGConnect_cg_i_i_D_l",
"targets must be a homogeneous subnet." );
throw BadProperty();
}
if ( dynamic_cast< Subnet* >( *targets->local_begin() ) )
{
LOG( M_ERROR,
"CGConnect_cg_i_i_D_l",
"Only 1-dim subnets are supported as targets." );
throw BadProperty();
}
const Token synmodel =
kernel().model_manager.get_synapsedict()->lookup( synmodel_name );
if ( synmodel.empty() )
throw UnknownSynapseType( synmodel_name.toString() );
const index synmodel_id = static_cast< index >( synmodel );
const modelrange source_range =
kernel().modelrange_manager.get_contiguous_gid_range(
( *sources->local_begin() )->get_gid() );
index source_offset = source_range.get_first_gid();
RangeSet source_ranges;
source_ranges.push_back(
Range( source_range.get_first_gid(), source_range.get_last_gid() ) );
const modelrange target_range =
kernel().modelrange_manager.get_contiguous_gid_range(
( *targets->local_begin() )->get_gid() );
index target_offset = target_range.get_first_gid();
RangeSet target_ranges;
target_ranges.push_back(
Range( target_range.get_first_gid(), target_range.get_last_gid() ) );
cg_connect( cg,
source_ranges,
source_offset,
target_ranges,
target_offset,
params_map,
synmodel_id );
}
void
nest::amat2_psc_exp::calibrate()
{
// ensures initialization in case mm connected after Simulate
B_.logger_.init();
const double h = Time::get_resolution().get_ms();
// numbering of state variables:
// membrane potential: i_0 = 0, i_syn_ = 1, V_m_ = 2
// adaptive threshold: V_th_1_ = 1, V_th_2_ = 2
// --------------------
// membrane potential
// --------------------
const double c = P_.C_;
const double beta = P_.beta_;
const double taum = P_.Tau_;
const double tauE = P_.tau_ex_;
const double tauI = P_.tau_in_;
const double tauV = P_.tau_v_;
// these P are independent
const double eE = std::exp( -h / P_.tau_ex_ );
const double eI = std::exp( -h / P_.tau_in_ );
const double em = std::exp( -h / P_.Tau_ );
const double e1 = std::exp( -h / P_.tau_1_ );
const double e2 = std::exp( -h / P_.tau_2_ );
const double eV = std::exp( -h / P_.tau_v_ );
// V_.P00 = 1;
V_.P11_ = eE;
V_.P22_ = eI;
V_.P33_ = em;
V_.P44_ = e1;
V_.P55_ = e2;
V_.P66_ = eV;
V_.P77_ = eV;
// these depend on the above. Please do not change the order.
// TODO Shortcut for beta=0
V_.P30_ = ( taum - em * taum ) / c;
V_.P31_ = ( ( eE - em ) * tauE * taum ) / ( c * ( tauE - taum ) );
V_.P32_ = ( ( eI - em ) * tauI * taum ) / ( c * ( tauI - taum ) );
V_.P60_ = ( beta * ( em - eV ) * taum * tauV ) / ( c * ( taum - tauV ) );
V_.P61_ =
( beta * tauE * taum * tauV * ( eV * ( -tauE + taum ) + em * ( tauE - tauV )
+ eE * ( -taum + tauV ) ) )
/ ( c * ( tauE - taum ) * ( tauE - tauV ) * ( taum - tauV ) );
V_.P62_ =
( beta * tauI * taum * tauV * ( eV * ( -tauI + taum ) + em * ( tauI - tauV )
+ eI * ( -taum + tauV ) ) )
/ ( c * ( tauI - taum ) * ( tauI - tauV ) * ( taum - tauV ) );
V_.P63_ = ( beta * ( -em + eV ) * tauV ) / ( taum - tauV );
V_.P70_ =
( beta * taum * tauV
* ( em * taum * tauV - eV * ( h * ( taum - tauV ) + taum * tauV ) ) )
/ ( c * std::pow( taum - tauV, 2 ) );
V_.P71_ =
( beta * tauE * taum * tauV
* ( ( em * taum * std::pow( tauE - tauV, 2 )
- eE * tauE * std::pow( taum - tauV, 2 ) ) * tauV
- eV * ( tauE - taum )
* ( h * ( tauE - tauV ) * ( taum - tauV ) + tauE * taum * tauV
- std::pow( tauV, 3 ) ) ) )
/ ( c * ( tauE - taum ) * std::pow( tauE - tauV, 2 )
* std::pow( taum - tauV, 2 ) );
V_.P72_ =
( beta * tauI * taum * tauV
* ( ( em * taum * std::pow( tauI - tauV, 2 )
- eI * tauI * std::pow( taum - tauV, 2 ) ) * tauV
- eV * ( tauI - taum )
* ( h * ( tauI - tauV ) * ( taum - tauV ) + tauI * taum * tauV
- std::pow( tauV, 3 ) ) ) )
/ ( c * ( tauI - taum ) * std::pow( tauI - tauV, 2 )
* std::pow( taum - tauV, 2 ) );
V_.P73_ = ( beta * tauV * ( -( em * taum * tauV )
+ eV * ( h * ( taum - tauV ) + taum * tauV ) ) )
/ std::pow( taum - tauV, 2 );
V_.P76_ = eV * h;
// tau_ref_ specifies the length of the total refractory period as
// a double in ms. The grid based amat2_psc_exp can only handle refractory
// periods that are integer multiples of the computation step size (h).
// To ensure consistency with the overall simulation scheme such conversion
// should be carried out via objects of class nest::Time. The conversion
// requires 2 steps:
// 1. A time object r is constructed defining representation of
// tau_ref_ in tics. This representation is then converted to
// computation time
// steps again by a strategy defined by class nest::Time.
// 2. The refractory time in units of steps is read out get_steps(), a
// member function of class nest::Time.
//
// Choosing a tau_ref_ that is not an integer multiple of the computation time
// step h will led to accurate (up to the resolution h) and self-consistent
// results. However, a neuron model capable of operating with real valued
// spike time may exhibit a different effective refractory time.
V_.RefractoryCountsTot_ = Time( Time::ms( P_.tau_ref_ ) ).get_steps();
if ( V_.RefractoryCountsTot_ < 1 )
{
throw BadProperty(
"Total refractory time must be at least one time step." );
}
}
void
nest::RNGManager::set_status( const DictionaryDatum& d )
{
// have those two for later asking, whether threads have changed:
long n_threads;
bool n_threads_updated =
updateValue< long >( d, "local_num_threads", n_threads );
// set RNGs --- MUST come after n_threads_ is updated
if ( d->known( "rngs" ) )
{
// this array contains pre-seeded RNGs, so they can be used
// directly, no seeding required
ArrayDatum* ad = dynamic_cast< ArrayDatum* >( ( *d )[ "rngs" ].datum() );
if ( ad == 0 )
throw BadProperty();
// n_threads_ is the new value after a change of the number of
// threads
if ( ad->size()
!= ( size_t )( kernel().vp_manager.get_num_virtual_processes() ) )
{
LOG( M_ERROR,
"RNGManager::set_status",
"Number of RNGs must equal number of virtual processes "
"(threads*processes). RNGs "
"unchanged." );
throw DimensionMismatch(
( size_t )( kernel().vp_manager.get_num_virtual_processes() ),
ad->size() );
}
// delete old generators, insert new generators this code is
// robust under change of thread number in this call to
// set_status, as long as it comes AFTER n_threads_ has been
// upated
rng_.clear();
for ( index i = 0; i < ad->size(); ++i )
if ( kernel().vp_manager.is_local_vp( i ) )
rng_.push_back( getValue< librandom::RngDatum >(
( *ad )[ kernel().vp_manager.suggest_vp( i ) ] ) );
}
else if ( n_threads_updated && kernel().node_manager.size() == 0 )
{
LOG( M_WARNING,
"RNGManager::set_status",
"Equipping threads with new default RNGs" );
create_rngs_();
}
if ( d->known( "rng_seeds" ) )
{
ArrayDatum* ad =
dynamic_cast< ArrayDatum* >( ( *d )[ "rng_seeds" ].datum() );
if ( ad == 0 )
throw BadProperty();
if ( ad->size()
!= ( size_t )( kernel().vp_manager.get_num_virtual_processes() ) )
{
LOG( M_ERROR,
"RNGManager::set_status",
"Number of seeds must equal number of virtual processes "
"(threads*processes). RNGs unchanged." );
throw DimensionMismatch(
( size_t )( kernel().vp_manager.get_num_virtual_processes() ),
ad->size() );
}
// check if seeds are unique
std::set< ulong_t > seedset;
for ( index i = 0; i < ad->size(); ++i )
{
long s = ( *ad )[ i ]; // SLI has no ulong tokens
if ( !seedset.insert( s ).second )
{
LOG( M_WARNING,
"RNGManager::set_status",
"Seeds are not unique across threads!" );
break;
}
}
// now apply seeds, resets generators automatically
for ( index i = 0; i < ad->size(); ++i )
{
long s = ( *ad )[ i ];
if ( kernel().vp_manager.is_local_vp( i ) )
rng_[ kernel().vp_manager.vp_to_thread(
kernel().vp_manager.suggest_vp( i ) ) ]->seed( s );
rng_seeds_[ i ] = s;
}
} // if rng_seeds
// set GRNG
if ( d->known( "grng" ) )
{
// pre-seeded grng that can be used directly, no seeding required
updateValue< librandom::RngDatum >( d, "grng", grng_ );
}
else if ( n_threads_updated && kernel().node_manager.size() == 0 )
{
LOG( M_WARNING,
"RNGManager::set_status",
"Equipping threads with new default GRNG" );
create_grng_();
}
if ( d->known( "grng_seed" ) )
{
const long gseed = getValue< long >( d, "grng_seed" );
// check if grng seed is unique with respect to rng seeds
// if grng_seed and rng_seeds given in one SetStatus call
std::set< ulong_t > seedset;
seedset.insert( gseed );
if ( d->known( "rng_seeds" ) )
{
ArrayDatum* ad_rngseeds =
dynamic_cast< ArrayDatum* >( ( *d )[ "rng_seeds" ].datum() );
if ( ad_rngseeds == 0 )
throw BadProperty();
for ( index i = 0; i < ad_rngseeds->size(); ++i )
{
const long vpseed = ( *ad_rngseeds )[ i ]; // SLI has no ulong tokens
if ( !seedset.insert( vpseed ).second )
{
LOG( M_WARNING,
"RNGManager::set_status",
"Seeds are not unique across threads!" );
break;
}
}
}
// now apply seed, resets generator automatically
grng_seed_ = gseed;
grng_->seed( gseed );
} // if grng_seed
}
nest::ConnBuilder::ConnBuilder( const GIDCollection& sources,
const GIDCollection& targets,
const DictionaryDatum& conn_spec,
const DictionaryDatum& syn_spec )
: sources_( &sources )
, targets_( &targets )
, autapses_( true )
, multapses_( true )
, symmetric_( false )
, exceptions_raised_( kernel().vp_manager.get_num_threads() )
, synapse_model_( kernel().model_manager.get_synapsedict()->lookup(
"static_synapse" ) )
, weight_( 0 )
, delay_( 0 )
, param_dicts_()
, parameters_requiring_skipping_()
{
// read out rule-related parameters -------------------------
// - /rule has been taken care of above
// - rule-specific params are handled by subclass c'tor
updateValue< bool >( conn_spec, names::autapses, autapses_ );
updateValue< bool >( conn_spec, names::multapses, multapses_ );
updateValue< bool >( conn_spec, names::symmetric, symmetric_ );
// read out synapse-related parameters ----------------------
if ( !syn_spec->known( names::model ) )
throw BadProperty( "Synapse spec must contain synapse model." );
const std::string syn_name = ( *syn_spec )[ names::model ];
if ( not kernel().model_manager.get_synapsedict()->known( syn_name ) )
throw UnknownSynapseType( syn_name );
// if another synapse than static_synapse is defined we need to make
// sure that Connect can process all parameter specified
if ( syn_name != "static_synapse" )
check_synapse_params_( syn_name, syn_spec );
synapse_model_ = kernel().model_manager.get_synapsedict()->lookup( syn_name );
DictionaryDatum syn_defaults =
kernel().model_manager.get_connector_defaults( synapse_model_ );
// All synapse models have the possibility to set the delay (see
// SynIdDelay), but some have homogeneous weights, hence it should
// be possible to set the delay without the weight.
default_weight_ = !syn_spec->known( names::weight );
default_delay_ = !syn_spec->known( names::delay );
// If neither weight nor delay are given in the dict, we handle this
// separately. Important for hom_w synapses, on which weight cannot
// be set. However, we use default weight and delay for _all_ types
// of synapses.
default_weight_and_delay_ = ( default_weight_ && default_delay_ );
#ifdef HAVE_MUSIC
// We allow music_channel as alias for receptor_type during
// connection setup
( *syn_defaults )[ names::music_channel ] = 0;
#endif
if ( !default_weight_and_delay_ )
{
weight_ = syn_spec->known( names::weight )
? ConnParameter::create( ( *syn_spec )[ names::weight ],
kernel().vp_manager.get_num_threads() )
: ConnParameter::create( ( *syn_defaults )[ names::weight ],
kernel().vp_manager.get_num_threads() );
register_parameters_requiring_skipping_( *weight_ );
delay_ = syn_spec->known( names::delay )
? ConnParameter::create(
( *syn_spec )[ names::delay ], kernel().vp_manager.get_num_threads() )
: ConnParameter::create( ( *syn_defaults )[ names::delay ],
kernel().vp_manager.get_num_threads() );
}
else if ( default_weight_ )
{
delay_ = syn_spec->known( names::delay )
? ConnParameter::create(
( *syn_spec )[ names::delay ], kernel().vp_manager.get_num_threads() )
: ConnParameter::create( ( *syn_defaults )[ names::delay ],
kernel().vp_manager.get_num_threads() );
}
register_parameters_requiring_skipping_( *delay_ );
// Structural plasticity parameters
// Check if both pre and post synaptic element are provided
if ( syn_spec->known( names::pre_synaptic_element )
&& syn_spec->known( names::post_synaptic_element ) )
{
pre_synaptic_element_name =
getValue< std::string >( syn_spec, names::pre_synaptic_element );
post_synaptic_element_name =
getValue< std::string >( syn_spec, names::post_synaptic_element );
}
else
{
if ( syn_spec->known( names::pre_synaptic_element )
|| syn_spec->known( names::post_synaptic_element ) )
{
throw BadProperty(
"In order to use structural plasticity, both a pre and post synaptic "
"element must be specified" );
}
pre_synaptic_element_name = "";
post_synaptic_element_name = "";
}
// synapse-specific parameters
// TODO: Can we create this set once and for all?
// Should not be done as static initialization, since
// that might conflict with static initialization of
// Name system.
std::set< Name > skip_set;
skip_set.insert( names::weight );
skip_set.insert( names::delay );
skip_set.insert( Name( "min_delay" ) );
skip_set.insert( Name( "max_delay" ) );
skip_set.insert( Name( "num_connections" ) );
skip_set.insert( Name( "num_connectors" ) );
skip_set.insert( Name( "property_object" ) );
skip_set.insert( Name( "synapsemodel" ) );
for ( Dictionary::const_iterator default_it = syn_defaults->begin();
default_it != syn_defaults->end();
++default_it )
{
const Name param_name = default_it->first;
if ( skip_set.find( param_name ) != skip_set.end() )
continue; // weight, delay or not-settable parameter
if ( syn_spec->known( param_name ) )
{
synapse_params_[ param_name ] = ConnParameter::create(
( *syn_spec )[ param_name ], kernel().vp_manager.get_num_threads() );
register_parameters_requiring_skipping_( *synapse_params_[ param_name ] );
}
}
// Now create dictionary with dummy values that we will use
// to pass settings to the synapses created. We create it here
// once to avoid re-creating the object over and over again.
if ( synapse_params_.size() > 0 )
{
for ( index t = 0; t < kernel().vp_manager.get_num_threads(); ++t )
{
param_dicts_.push_back( new Dictionary() );
for ( ConnParameterMap::const_iterator it = synapse_params_.begin();
it != synapse_params_.end();
++it )
{
if ( it->first == names::receptor_type
|| it->first == names::music_channel
|| it->first == names::synapse_label )
( *param_dicts_[ t ] )[ it->first ] = Token( new IntegerDatum( 0 ) );
else
( *param_dicts_[ t ] )[ it->first ] = Token( new DoubleDatum( 0.0 ) );
}
}
}
// If symmetric_ is requested call reset on all parameters in order
// to check if all parameters support symmetric connections
if ( symmetric_ )
{
if ( weight_ )
{
weight_->reset();
}
if ( delay_ )
{
delay_->reset();
}
for ( ConnParameterMap::const_iterator it = synapse_params_.begin();
it != synapse_params_.end();
++it )
{
it->second->reset();
}
}
}
double
iaf_psc_alpha_multisynapse::Parameters_::set( const DictionaryDatum& d )
{
// if E_L_ is changed, we need to adjust all variables defined relative to
// E_L_
const double ELold = E_L_;
updateValue< double >( d, names::E_L, E_L_ );
const double delta_EL = E_L_ - ELold;
if ( updateValue< double >( d, names::V_reset, V_reset_ ) )
{
V_reset_ -= E_L_;
}
else
{
V_reset_ -= delta_EL;
}
if ( updateValue< double >( d, names::V_th, Theta_ ) )
{
Theta_ -= E_L_;
}
else
{
Theta_ -= delta_EL;
}
if ( updateValue< double >( d, names::V_min, LowerBound_ ) )
{
LowerBound_ -= E_L_;
}
else
{
LowerBound_ -= delta_EL;
}
updateValue< double >( d, names::I_e, I_e_ );
updateValue< double >( d, names::C_m, C_ );
updateValue< double >( d, names::tau_m, Tau_ );
updateValue< double >( d, names::t_ref, refractory_time_ );
if ( C_ <= 0 )
{
throw BadProperty( "Capacitance must be strictly positive." );
}
if ( Tau_ <= 0. )
{
throw BadProperty( "Membrane time constant must be strictly positive." );
}
const size_t old_n_receptors = this->n_receptors_();
if ( updateValue< std::vector< double > >( d, "tau_syn", tau_syn_ ) )
{
if ( this->n_receptors_() != old_n_receptors && has_connections_ == true )
{
throw BadProperty(
"The neuron has connections, therefore the number of ports cannot be "
"reduced." );
}
for ( size_t i = 0; i < tau_syn_.size(); ++i )
{
if ( tau_syn_[ i ] <= 0 )
{
throw BadProperty(
"All synaptic time constants must be strictly positive." );
}
}
}
if ( refractory_time_ < 0. )
{
throw BadProperty( "Refractory time must not be negative." );
}
if ( V_reset_ >= Theta_ )
{
throw BadProperty( "Reset potential must be smaller than threshold." );
}
return delta_EL;
}
inline void
nest::ConnBuilder::check_synapse_params_( std::string syn_name,
const DictionaryDatum& syn_spec )
{
// throw error if weight is specified with static_synapse_hom_w
if ( syn_name == "static_synapse_hom_w" )
{
if ( syn_spec->known( names::weight ) )
throw BadProperty(
"Weight cannot be specified since it needs to be equal "
"for all connections when static_synapse_hom_w is used." );
return;
}
// throw error if n or a are set in quantal_stp_synapse, Connect cannot handle
// them since they are integer
if ( syn_name == "quantal_stp_synapse" )
{
if ( syn_spec->known( names::n ) )
throw NotImplemented(
"Connect doesn't support the setting of parameter "
"n in quantal_stp_synapse. Use SetDefaults() or CopyModel()." );
if ( syn_spec->known( names::a ) )
throw NotImplemented(
"Connect doesn't support the setting of parameter "
"a in quantal_stp_synapse. Use SetDefaults() or CopyModel()." );
return;
}
// print warning if delay is specified outside cont_delay_synapse
if ( syn_name == "cont_delay_synapse" )
{
if ( syn_spec->known( names::delay ) )
LOG( M_WARNING,
"Connect",
"The delay will be rounded to the next multiple of the time step. "
"To use a more precise time delay it needs to be defined within "
"the synapse, e.g. with CopyModel()." );
return;
}
// throw error if no volume transmitter is defined or parameters are specified
// that need to be introduced via CopyModel or SetDefaults
if ( syn_name == "stdp_dopamine_synapse" )
{
if ( syn_spec->known( "vt" ) )
throw NotImplemented(
"Connect doesn't support the direct specification of the "
"volume transmitter of stdp_dopamine_synapse in syn_spec."
"Use SetDefaults() or CopyModel()." );
// setting of parameter c and n not thread save
if ( kernel().vp_manager.get_num_threads() > 1 )
{
if ( syn_spec->known( names::c ) )
throw NotImplemented(
"For multi-threading Connect doesn't support the setting "
"of parameter c in stdp_dopamine_synapse. "
"Use SetDefaults() or CopyModel()." );
if ( syn_spec->known( names::n ) )
throw NotImplemented(
"For multi-threading Connect doesn't support the setting "
"of parameter n in stdp_dopamine_synapse. "
"Use SetDefaults() or CopyModel()." );
}
std::string param_arr[] = {
"A_minus", "A_plus", "Wmax", "Wmin", "b", "tau_c", "tau_n", "tau_plus"
};
std::vector< std::string > param_vec( param_arr, param_arr + 8 );
for ( std::vector< std::string >::iterator it = param_vec.begin();
it != param_vec.end();
it++ )
{
if ( syn_spec->known( *it ) )
throw NotImplemented(
"Connect doesn't support the setting of parameter " + *it
+ " in stdp_dopamine_synapse. Use SetDefaults() or CopyModel()." );
}
return;
}
}
inline void
nest::ConnBuilder::single_connect_( index sgid,
Node& target,
thread target_thread,
librandom::RngPtr& rng )
{
if ( param_dicts_.empty() ) // indicates we have no synapse params
{
if ( default_weight_and_delay_ )
kernel().connection_manager.connect(
sgid, &target, target_thread, synapse_model_ );
else if ( default_weight_ )
kernel().connection_manager.connect( sgid,
&target,
target_thread,
synapse_model_,
delay_->value_double( target_thread, rng ) );
else
{
double delay = delay_->value_double( target_thread, rng );
double weight = weight_->value_double( target_thread, rng );
kernel().connection_manager.connect(
sgid, &target, target_thread, synapse_model_, delay, weight );
}
}
else
{
assert( kernel().vp_manager.get_num_threads() == param_dicts_.size() );
for ( ConnParameterMap::const_iterator it = synapse_params_.begin();
it != synapse_params_.end();
++it )
{
if ( it->first == names::receptor_type
|| it->first == names::music_channel
|| it->first == names::synapse_label )
{
try
{
// change value of dictionary entry without allocating new datum
IntegerDatum* id = static_cast< IntegerDatum* >(
( ( *param_dicts_[ target_thread ] )[ it->first ] ).datum() );
( *id ) = it->second->value_int( target_thread, rng );
}
catch ( KernelException& e )
{
if ( it->first == names::receptor_type )
{
throw BadProperty( "Receptor type must be of type integer." );
}
else if ( it->first == names::music_channel )
{
throw BadProperty( "Music channel type must be of type integer." );
}
else if ( it->first == names::synapse_label )
{
throw BadProperty( "Synapse label must be of type integer." );
}
}
}
else
{
// change value of dictionary entry without allocating new datum
DoubleDatum* dd = static_cast< DoubleDatum* >(
( ( *param_dicts_[ target_thread ] )[ it->first ] ).datum() );
( *dd ) = it->second->value_double( target_thread, rng );
}
}
if ( default_weight_and_delay_ )
kernel().connection_manager.connect( sgid,
&target,
target_thread,
synapse_model_,
param_dicts_[ target_thread ] );
else if ( default_weight_ )
kernel().connection_manager.connect( sgid,
&target,
target_thread,
synapse_model_,
param_dicts_[ target_thread ],
delay_->value_double( target_thread, rng ) );
else
{
double delay = delay_->value_double( target_thread, rng );
double weight = weight_->value_double( target_thread, rng );
kernel().connection_manager.connect( sgid,
&target,
target_thread,
synapse_model_,
param_dicts_[ target_thread ],
delay,
weight );
}
}
}
MaskDatum
TopologyModule::create_mask( const Token& t )
{
// t can be either an existing MaskDatum, or a Dictionary containing
// mask parameters
MaskDatum* maskd = dynamic_cast< MaskDatum* >( t.datum() );
if ( maskd )
{
return *maskd;
}
else
{
DictionaryDatum* dd = dynamic_cast< DictionaryDatum* >( t.datum() );
if ( dd == 0 )
{
throw BadProperty( "Mask must be masktype or dictionary." );
}
// The dictionary should contain one key which is the name of the
// mask type, and optionally the key 'anchor'. To find the unknown
// mask type key, we must loop through all keys. The value for the
// anchor key will be stored in the anchor_token variable.
Token anchor_token;
bool has_anchor = false;
AbstractMask* mask = 0;
for ( Dictionary::iterator dit = ( *dd )->begin(); dit != ( *dd )->end();
++dit )
{
if ( dit->first == names::anchor )
{
anchor_token = dit->second;
has_anchor = true;
}
else
{
if ( mask != 0 )
{ // mask has already been defined
throw BadProperty(
"Mask definition dictionary contains extraneous items." );
}
mask =
create_mask( dit->first, getValue< DictionaryDatum >( dit->second ) );
}
}
if ( has_anchor )
{
// The anchor may be an array of doubles (a spatial position), or a
// dictionary containing the keys 'column' and 'row' (for grid
// masks only)
try
{
std::vector< double > anchor =
getValue< std::vector< double > >( anchor_token );
AbstractMask* amask;
switch ( anchor.size() )
{
case 2:
amask = new AnchoredMask< 2 >(
dynamic_cast< Mask< 2 >& >( *mask ), anchor );
break;
case 3:
amask = new AnchoredMask< 3 >(
dynamic_cast< Mask< 3 >& >( *mask ), anchor );
break;
default:
throw BadProperty( "Anchor must be 2- or 3-dimensional." );
}
delete mask;
mask = amask;
}
catch ( TypeMismatch& e )
{
DictionaryDatum ad = getValue< DictionaryDatum >( anchor_token );
int dim = 2;
int column = getValue< long >( ad, names::column );
int row = getValue< long >( ad, names::row );
int layer;
if ( ad->known( names::layer ) )
{
layer = getValue< long >( ad, names::layer );
dim = 3;
}
switch ( dim )
{
case 2:
try
{
GridMask< 2 >& grid_mask_2d =
dynamic_cast< GridMask< 2 >& >( *mask );
grid_mask_2d.set_anchor( Position< 2, int >( column, row ) );
}
catch ( std::bad_cast& e )
{
throw BadProperty( "Mask must be 2-dimensional grid mask." );
}
break;
case 3:
try
{
GridMask< 3 >& grid_mask_3d =
dynamic_cast< GridMask< 3 >& >( *mask );
grid_mask_3d.set_anchor( Position< 3, int >( column, row, layer ) );
}
catch ( std::bad_cast& e )
{
throw BadProperty( "Mask must be 3-dimensional grid mask." );
}
break;
}
}
}
return mask;
}
}
bool
nest::correlospinmatrix_detector::Parameters_::set( const DictionaryDatum& d,
const correlospinmatrix_detector& n )
{
bool reset = false;
double_t t;
long_t N;
if ( updateValue< long_t >( d, names::N_channels, N ) )
{
if ( N < 1 )
{
throw BadProperty( "/N_channels can only be larger than zero." );
}
else
{
N_channels_ = N;
reset = true;
}
}
if ( updateValue< double_t >( d, names::delta_tau, t ) )
{
delta_tau_ = Time::ms( t );
reset = true;
if ( t < 0 )
{
throw BadProperty( "/delta_tau must not be negative." );
}
}
if ( updateValue< double_t >( d, names::tau_max, t ) )
{
tau_max_ = Time::ms( t );
reset = true;
if ( t < 0 )
{
throw BadProperty( "/tau_max must not be negative." );
}
}
if ( updateValue< double_t >( d, names::Tstart, t ) )
{
Tstart_ = Time::ms( t );
reset = true;
if ( t < 0 )
{
throw BadProperty( "/Tstart must not be negative." );
}
}
if ( updateValue< double_t >( d, names::Tstop, t ) )
{
Tstop_ = Time::ms( t );
reset = true;
if ( t < 0 )
{
throw BadProperty( "/Tstop must not be negative." );
}
}
if ( !delta_tau_.is_step() )
{
throw StepMultipleRequired( n.get_name(), names::delta_tau, delta_tau_ );
}
if ( !tau_max_.is_multiple_of( delta_tau_ ) )
{
throw TimeMultipleRequired(
n.get_name(), names::tau_max, tau_max_, names::delta_tau, delta_tau_ );
}
return reset;
}
double
nest::iaf_psc_alpha_canon::Parameters_::set( const DictionaryDatum& d )
{
// if E_L_ is changed, we need to adjust all variables defined relative to
// E_L_
const double ELold = E_L_;
updateValue< double >( d, names::E_L, E_L_ );
const double delta_EL = E_L_ - ELold;
updateValue< double >( d, names::tau_m, tau_m_ );
updateValue< double >( d, names::tau_syn, tau_syn_ );
updateValue< double >( d, names::C_m, c_m_ );
updateValue< double >( d, names::t_ref, t_ref_ );
updateValue< double >( d, names::I_e, I_e_ );
if ( updateValue< double >( d, names::V_th, U_th_ ) )
{
U_th_ -= E_L_;
}
else
{
U_th_ -= delta_EL;
}
if ( updateValue< double >( d, names::V_min, U_min_ ) )
{
U_min_ -= E_L_;
}
else
{
U_min_ -= delta_EL;
}
if ( updateValue< double >( d, names::V_reset, U_reset_ ) )
{
U_reset_ -= E_L_;
}
else
{
U_reset_ -= delta_EL;
}
long tmp;
if ( updateValue< long >( d, names::Interpol_Order, tmp ) )
{
if ( NO_INTERPOL <= tmp && tmp < END_INTERP_ORDER )
{
Interpol_ = static_cast< interpOrder >( tmp );
}
else
{
throw BadProperty(
"Invalid interpolation order. "
"Valid orders are 0, 1, 2, 3." );
}
}
if ( U_reset_ >= U_th_ )
{
throw BadProperty( "Reset potential must be smaller than threshold." );
}
if ( U_reset_ < U_min_ )
{
throw BadProperty(
"Reset potential must be greater equal minimum potential." );
}
if ( c_m_ <= 0 )
{
throw BadProperty( "Capacitance must be strictly positive." );
}
if ( Time( Time::ms( t_ref_ ) ).get_steps() < 1 )
{
throw BadProperty( "Refractory time must be at least one time step." );
}
if ( tau_m_ <= 0 || tau_syn_ <= 0 )
{
throw BadProperty( "All time constants must be strictly positive." );
}
return delta_EL;
}
index NodeManager::add_node( index mod, long n ) // no_p
{
assert( current_ != 0 );
assert( root_ != 0 );
if ( mod >= kernel().model_manager.get_num_node_models() )
{
throw UnknownModelID( mod );
}
if ( n < 1 )
{
throw BadProperty();
}
const thread n_threads = kernel().vp_manager.get_num_threads();
assert( n_threads > 0 );
const index min_gid = local_nodes_.get_max_gid() + 1;
const index max_gid = min_gid + n;
Model* model = kernel().model_manager.get_model( mod );
assert( model != 0 );
model->deprecation_warning( "Create" );
/* current_ points to the instance of the current subnet on thread 0.
The following code makes subnet a pointer to the wrapper container
containing the instances of the current subnet on all threads.
*/
const index subnet_gid = current_->get_gid();
Node* subnet_node = local_nodes_.get_node_by_gid( subnet_gid );
assert( subnet_node != 0 );
SiblingContainer* subnet_container =
dynamic_cast< SiblingContainer* >( subnet_node );
assert( subnet_container != 0 );
assert( subnet_container->num_thread_siblings()
== static_cast< size_t >( n_threads ) );
assert( subnet_container->get_thread_sibling( 0 ) == current_ );
if ( max_gid > local_nodes_.max_size() || max_gid < min_gid )
{
LOG( M_ERROR,
"NodeManager::add:node",
"Requested number of nodes will overflow the memory." );
LOG( M_ERROR, "NodeManager::add:node", "No nodes were created." );
throw KernelException( "OutOfMemory" );
}
kernel().modelrange_manager.add_range( mod, min_gid, max_gid - 1 );
if ( model->potential_global_receiver()
and kernel().mpi_manager.get_num_rec_processes() > 0 )
{
// In this branch we create nodes for global receivers
const int n_per_process = n / kernel().mpi_manager.get_num_rec_processes();
const int n_per_thread = n_per_process / n_threads + 1;
// We only need to reserve memory on the ranks on which we
// actually create nodes. In this if-branch ---> Only on recording
// processes
if ( kernel().mpi_manager.get_rank()
>= kernel().mpi_manager.get_num_sim_processes() )
{
local_nodes_.reserve( std::ceil( static_cast< double >( max_gid )
/ kernel().mpi_manager.get_num_sim_processes() ) );
for ( thread t = 0; t < n_threads; ++t )
{
// Model::reserve() reserves memory for n ADDITIONAL nodes on thread t
model->reserve_additional( t, n_per_thread );
}
}
for ( size_t gid = min_gid; gid < max_gid; ++gid )
{
const thread vp = kernel().vp_manager.suggest_rec_vp( get_n_gsd() );
const thread t = kernel().vp_manager.vp_to_thread( vp );
if ( kernel().vp_manager.is_local_vp( vp ) )
{
Node* newnode = model->allocate( t );
newnode->set_gid_( gid );
newnode->set_model_id( mod );
newnode->set_thread( t );
newnode->set_vp( vp );
newnode->set_has_proxies( true );
newnode->set_local_receiver( false );
local_nodes_.add_local_node( *newnode ); // put into local nodes list
current_->add_node( newnode ); // and into current subnet, thread 0.
}
else
{
local_nodes_.add_remote_node( gid ); // ensures max_gid is correct
current_->add_remote_node( gid, mod );
}
increment_n_gsd();
}
}
else if ( model->has_proxies() )
{
// In this branch we create nodes for all GIDs which are on a local thread
const int n_per_process = n / kernel().mpi_manager.get_num_sim_processes();
const int n_per_thread = n_per_process / n_threads + 1;
// We only need to reserve memory on the ranks on which we
// actually create nodes. In this if-branch ---> Only on
// simulation processes
if ( kernel().mpi_manager.get_rank()
< kernel().mpi_manager.get_num_sim_processes() )
{
// TODO: This will work reasonably for round-robin. The extra 50 entries
// are for subnets and devices.
local_nodes_.reserve(
std::ceil( static_cast< double >( max_gid )
/ kernel().mpi_manager.get_num_sim_processes() ) + 50 );
for ( thread t = 0; t < n_threads; ++t )
{
// Model::reserve() reserves memory for n ADDITIONAL nodes on thread t
// reserves at least one entry on each thread, nobody knows why
model->reserve_additional( t, n_per_thread );
}
}
size_t gid;
if ( kernel().vp_manager.is_local_vp(
kernel().vp_manager.suggest_vp( min_gid ) ) )
{
gid = min_gid;
}
else
{
gid = next_local_gid_( min_gid );
}
size_t next_lid = current_->global_size() + gid - min_gid;
// The next loop will not visit every node, if more than one rank is
// present.
// Since we already know what range of gids will be created, we can tell the
// current subnet the range and subsequent calls to
// `current_->add_remote_node()`
// become irrelevant.
current_->add_gid_range( min_gid, max_gid - 1 );
// min_gid is first valid gid i should create, hence ask for the first local
// gid after min_gid-1
while ( gid < max_gid )
{
const thread vp = kernel().vp_manager.suggest_vp( gid );
const thread t = kernel().vp_manager.vp_to_thread( vp );
if ( kernel().vp_manager.is_local_vp( vp ) )
{
Node* newnode = model->allocate( t );
newnode->set_gid_( gid );
newnode->set_model_id( mod );
newnode->set_thread( t );
newnode->set_vp( vp );
local_nodes_.add_local_node( *newnode ); // put into local nodes list
current_->add_node( newnode ); // and into current subnet, thread 0.
// lid setting is wrong, if a range is set, as the subnet already
// assumes,
// the nodes are available.
newnode->set_lid_( next_lid );
const size_t next_gid = next_local_gid_( gid );
next_lid += next_gid - gid;
gid = next_gid;
}
else
{
++gid; // brutal fix, next_lid has been set in if-branch
}
}
// if last gid is not on this process, we need to add it as a remote node
if ( not kernel().vp_manager.is_local_vp(
kernel().vp_manager.suggest_vp( max_gid - 1 ) ) )
{
local_nodes_.add_remote_node( max_gid - 1 ); // ensures max_gid is correct
current_->add_remote_node( max_gid - 1, mod );
}
}
else if ( not model->one_node_per_process() )
{
// We allocate space for n containers which will hold the threads
// sorted. We use SiblingContainers to store the instances for
// each thread to exploit the very efficient memory allocation for
// nodes.
//
// These containers are registered in the global nodes_ array to
// provide access to the instances both for manipulation by SLI
// functions and so that NodeManager::calibrate() can discover the
// instances and register them for updating.
//
// The instances are also registered with the instance of the
// current subnet for the thread to which the created instance
// belongs. This is mainly important so that the subnet structure
// is preserved on all VPs. Node enumeration is done on by the
// registration with the per-thread instances.
//
// The wrapper container can be addressed under the GID assigned
// to no-proxy node created. If this no-proxy node is NOT a
// container (e.g. a device), then each instance can be retrieved
// by giving the respective thread-id to get_node(). Instances of
// SiblingContainers cannot be addressed individually.
//
// The allocation of the wrapper containers is spread over threads
// to balance memory load.
size_t container_per_thread = n / n_threads + 1;
// since we create the n nodes on each thread, we reserve the full load.
for ( thread t = 0; t < n_threads; ++t )
{
model->reserve_additional( t, n );
siblingcontainer_model_->reserve_additional( t, container_per_thread );
static_cast< Subnet* >( subnet_container->get_thread_sibling( t ) )
->reserve( n );
}
// The following loop creates n nodes. For each node, a wrapper is created
// and filled with one instance per thread, in total n * n_thread nodes in
// n wrappers.
local_nodes_.reserve(
std::ceil( static_cast< double >( max_gid )
/ kernel().mpi_manager.get_num_sim_processes() ) + 50 );
for ( index gid = min_gid; gid < max_gid; ++gid )
{
thread thread_id = kernel().vp_manager.vp_to_thread(
kernel().vp_manager.suggest_vp( gid ) );
// Create wrapper and register with nodes_ array.
SiblingContainer* container = static_cast< SiblingContainer* >(
siblingcontainer_model_->allocate( thread_id ) );
container->set_model_id(
-1 ); // mark as pseudo-container wrapping replicas, see reset_network()
container->reserve( n_threads ); // space for one instance per thread
container->set_gid_( gid );
local_nodes_.add_local_node( *container );
// Generate one instance of desired model per thread
for ( thread t = 0; t < n_threads; ++t )
{
Node* newnode = model->allocate( t );
newnode->set_gid_( gid ); // all instances get the same global id.
newnode->set_model_id( mod );
newnode->set_thread( t );
newnode->set_vp( kernel().vp_manager.thread_to_vp( t ) );
// Register instance with wrapper
// container has one entry for each thread
container->push_back( newnode );
// Register instance with per-thread instance of enclosing subnet.
static_cast< Subnet* >( subnet_container->get_thread_sibling( t ) )
->add_node( newnode );
}
}
}
else
{
// no proxies and one node per process
// this is used by MUSIC proxies
// Per r9700, this case is only relevant for music_*_proxy models,
// which have a single instance per MPI process.
for ( index gid = min_gid; gid < max_gid; ++gid )
{
Node* newnode = model->allocate( 0 );
newnode->set_gid_( gid );
newnode->set_model_id( mod );
newnode->set_thread( 0 );
newnode->set_vp( kernel().vp_manager.thread_to_vp( 0 ) );
// Register instance
local_nodes_.add_local_node( *newnode );
// and into current subnet, thread 0.
current_->add_node( newnode );
}
}
// set off-grid spike communication if necessary
if ( model->is_off_grid() )
{
kernel().event_delivery_manager.set_off_grid_communication( true );
LOG( M_INFO,
"NodeManager::add_node",
"Neuron models emitting precisely timed spikes exist: "
"the kernel property off_grid_spiking has been set to true.\n\n"
"NOTE: Mixing precise-spiking and normal neuron models may "
"lead to inconsistent results." );
}
return max_gid - 1;
}
void
nest::SimulationManager::set_status( const DictionaryDatum& d )
{
// Create an instance of time converter here to capture the current
// representation of time objects: TICS_PER_MS and TICS_PER_STEP
// will be stored in time_converter.
// This object can then be used to convert times in steps
// (e.g. Connection::delay_) or tics to the new representation.
// We pass this object to ConnectionManager::calibrate to update
// all time objects in the connection system to the new representation.
// MH 08-04-14
TimeConverter time_converter;
double_t time;
if ( updateValue< double_t >( d, "time", time ) )
{
if ( time != 0.0 )
throw BadProperty( "The simulation time can only be set to 0.0." );
if ( clock_ > TimeZero )
{
// reset only if time has passed
LOG( M_WARNING,
"SimulationManager::set_status",
"Simulation time reset to t=0.0. Resetting the simulation time is not "
"fully supported in NEST at present. Some spikes may be lost, and "
"stimulating devices may behave unexpectedly. PLEASE REVIEW YOUR "
"SIMULATION OUTPUT CAREFULLY!" );
clock_ = Time::step( 0 );
from_step_ = 0;
slice_ = 0;
// clear all old spikes
kernel().event_delivery_manager.configure_spike_buffers();
}
}
updateValue< bool >( d, "print_time", print_time_ );
// tics_per_ms and resolution must come after local_num_thread /
// total_num_threads because they might reset the network and the time
// representation
nest::double_t tics_per_ms = 0.0;
bool tics_per_ms_updated =
updateValue< nest::double_t >( d, "tics_per_ms", tics_per_ms );
double_t resd = 0.0;
bool res_updated = updateValue< double_t >( d, "resolution", resd );
if ( tics_per_ms_updated || res_updated )
{
if ( kernel().node_manager.size() > 1 ) // root always exists
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Cannot change time representation after nodes have been created. "
"Please call ResetKernel first." );
throw KernelException();
}
else if ( has_been_simulated() ) // someone may have simulated empty network
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Cannot change time representation after the network has been "
"simulated. Please call ResetKernel first." );
throw KernelException();
}
else if ( kernel().connection_manager.get_num_connections() != 0 )
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Cannot change time representation after connections have been "
"created. Please call ResetKernel first." );
throw KernelException();
}
else if ( res_updated && tics_per_ms_updated ) // only allow TICS_PER_MS to
// be changed together with
// resolution
{
if ( resd < 1.0 / tics_per_ms )
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Resolution must be greater than or equal to one tic. Value "
"unchanged." );
throw KernelException();
}
else
{
nest::Time::set_resolution( tics_per_ms, resd );
// adjust to new resolution
clock_.calibrate();
// adjust delays in the connection system to new resolution
kernel().connection_manager.calibrate( time_converter );
kernel().model_manager.calibrate( time_converter );
LOG( M_INFO,
"SimulationManager::set_status",
"tics per ms and resolution changed." );
// make sure that wfr communication interval is always greater or equal
// to resolution if no wfr is used explicitly set wfr_comm_interval
// to resolution because communication in every step is needed
if ( wfr_comm_interval_ < Time::get_resolution().get_ms()
|| not use_wfr_ )
{
wfr_comm_interval_ = Time::get_resolution().get_ms();
}
}
}
else if ( res_updated ) // only resolution changed
{
if ( resd < Time::get_ms_per_tic() )
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Resolution must be greater than or equal to one tic. Value "
"unchanged." );
throw KernelException();
}
else
{
Time::set_resolution( resd );
clock_.calibrate(); // adjust to new resolution
// adjust delays in the connection system to new resolution
kernel().connection_manager.calibrate( time_converter );
kernel().model_manager.calibrate( time_converter );
LOG( M_INFO,
"SimulationManager::set_status",
"Temporal resolution changed." );
// make sure that wfr communication interval is always greater or equal
// to resolution if no wfr is used explicitly set wfr_comm_interval
// to resolution because communication in every step is needed
if ( wfr_comm_interval_ < Time::get_resolution().get_ms()
|| not use_wfr_ )
{
wfr_comm_interval_ = Time::get_resolution().get_ms();
}
}
}
else
{
LOG( M_ERROR,
"SimulationManager::set_status",
"change of tics_per_step requires simultaneous specification of "
"resolution." );
throw KernelException();
}
}
// The decision whether the waveform relaxation is used
// must be set before nodes are created.
// Important: wfr_comm_interval_ may change depending on use_wfr_
bool wfr;
if ( updateValue< bool >( d, "use_wfr", wfr ) )
{
if ( kernel().node_manager.size() > 1 )
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Cannot enable/disable usage of waveform relaxation after nodes have "
"been created. Please call ResetKernel first." );
throw KernelException();
}
else
{
use_wfr_ = wfr;
// if no wfr is used explicitly set wfr_comm_interval to resolution
// because communication in every step is needed
if ( not use_wfr_ )
{
wfr_comm_interval_ = Time::get_resolution().get_ms();
}
}
}
// wfr_comm_interval_ can only be changed if use_wfr_ is true and before
// connections are created. If use_wfr_ is false wfr_comm_interval_ is set to
// the resolution whenever the resolution changes.
double_t wfr_interval;
if ( updateValue< double_t >( d, "wfr_comm_interval", wfr_interval ) )
{
if ( not use_wfr_ )
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Cannot set waveform communication interval when usage of waveform "
"relaxation is disabled. Set use_wfr to true first." );
throw KernelException();
}
else if ( kernel().connection_manager.get_num_connections() != 0 )
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Cannot change waveform communication interval after connections have "
"been created. Please call ResetKernel first." );
throw KernelException();
}
else if ( wfr_interval < Time::get_resolution().get_ms() )
{
LOG( M_ERROR,
"SimulationManager::set_status",
"Communication interval of the waveform relaxation must be greater or "
"equal to the resolution of the simulation." );
throw KernelException();
}
else
{
LOG( M_INFO,
"SimulationManager::set_status",
"Waveform communication interval changed successfully. " );
wfr_comm_interval_ = wfr_interval;
}
}
// set the convergence tolerance for the waveform relaxation method
double_t tol;
if ( updateValue< double_t >( d, "wfr_tol", tol ) )
{
if ( tol < 0.0 )
LOG( M_ERROR,
"SimulationManager::set_status",
"Tolerance must be zero or positive" );
else
wfr_tol_ = tol;
}
// set the maximal number of iterations for the waveform relaxation method
long max_iter;
if ( updateValue< long >( d, "wfr_max_iterations", max_iter ) )
{
if ( max_iter <= 0 )
LOG( M_ERROR,
"SimulationManager::set_status",
"Maximal number of iterations for the waveform relaxation must be "
"positive. To disable waveform relaxation set use_wfr instead." );
else
wfr_max_iterations_ = max_iter;
}
// set the interpolation order for the waveform relaxation method
long interp_order;
if ( updateValue< long >( d, "wfr_interpolation_order", interp_order ) )
{
if ( ( interp_order < 0 ) || ( interp_order == 2 ) || ( interp_order > 3 ) )
LOG( M_ERROR,
"SimulationManager::set_status",
"Interpolation order must be 0, 1, or 3." );
else
wfr_interpolation_order_ = interp_order;
}
}
void nest::spike_generator::Parameters_::set(const DictionaryDatum& d, State_& s,
const Time& origin, const Time& now)
{
const bool flags_changed = updateValue<bool>(d, names::precise_times, precise_times_)
|| updateValue<bool>(d, "allow_offgrid_spikes", allow_offgrid_spikes_)
|| updateValue<bool>(d, "shift_now_spikes", shift_now_spikes_);
if ( precise_times_ && ( allow_offgrid_spikes_ || shift_now_spikes_ ) )
throw BadProperty("Option precise_times cannot be set to true when either allow_offgrid_spikes "
"or shift_now_spikes is set to true.");
const bool updated_spike_times = d->known(names::spike_times);
if ( flags_changed && !(updated_spike_times || spike_stamps_.empty()) )
throw BadProperty("Options can only be set together with spike times or if no "
"spike times have been set.");
if ( updated_spike_times )
{
const std::vector<double_t> d_times = getValue<std::vector<double> >(d->lookup(names::spike_times));
const size_t n_spikes = d_times.size();
spike_stamps_.clear();
spike_stamps_.reserve(n_spikes);
spike_offsets_.clear();
if ( precise_times_ )
spike_offsets_.reserve(n_spikes);
// Check spike times for ordering and grid compatibility and insert them
if ( !d_times.empty() )
{
// handle first spike time, no predecessor to compare with
std::vector<double_t>::const_iterator prev = d_times.begin();
assert_valid_spike_time_and_insert_(*prev, origin, now);
// handle all remaining spike times, compare to predecessor
for ( std::vector<double_t>::const_iterator next = prev + 1;
next != d_times.end() ; ++next, ++prev )
if ( *prev > *next )
throw BadProperty("Spike times must be sorted in non-descending order.");
else
assert_valid_spike_time_and_insert_(*next, origin, now);
}
}
// spike_weights can be the same size as spike_times, or can be of size 0 to
// only use the spike_times array
bool updated_spike_weights = d->known("spike_weights");
if (updated_spike_weights)
{
std::vector<double> spike_weights = getValue<std::vector<double> >(d->lookup("spike_weights"));
if (spike_weights.empty())
spike_weights_.clear();
else
{
if (spike_weights.size() != spike_stamps_.size())
throw BadProperty("spike_weights must have the same number of elements as spike_times,"
" or 0 elements to clear the property.");
spike_weights_.swap(spike_weights);
}
}
// Set position to start if something changed
if ( updated_spike_times || updated_spike_weights || d->known(names::origin) )
s.position_ = 0;
}
void
nest::iaf_tum_2000::calibrate()
{
B_.logger_.init();
const double h = Time::get_resolution().get_ms();
// numbering of state vaiables: i_0 = 0, i_syn_ = 1, V_m_ = 2
// commented out propagators: forward Euler
// needed to exactly reproduce Tsodyks network
// these P are independent
V_.P11ex_ = std::exp( -h / P_.tau_ex_ );
// P11ex_ = 1.0-h/tau_ex_;
V_.P11in_ = std::exp( -h / P_.tau_in_ );
// P11in_ = 1.0-h/tau_in_;
V_.P22_ = std::exp( -h / P_.Tau_ );
// P22_ = 1.0-h/Tau_;
// these are determined according to a numeric stability criterion
V_.P21ex_ = propagator_32( P_.tau_ex_, P_.Tau_, P_.C_, h );
V_.P21in_ = propagator_32( P_.tau_in_, P_.Tau_, P_.C_, h );
// P21ex_ = h/C_;
// P21in_ = h/C_;
V_.P20_ = P_.Tau_ / P_.C_ * ( 1.0 - V_.P22_ );
// P20_ = h/C_;
// tau_ref_abs_ and tau_ref_tot_ specify the length of the corresponding
// refractory periods as doubles in ms. The grid based iaf_tum_2000 can
// only handle refractory periods that are integer multiples of the
// computation step size (h). To ensure consistency with the overall
// simulation scheme such conversion should be carried out via objects of
// class nest::Time. The conversion requires 2 steps:
// 1. A time object r is constructed, defining representation of
// tau_ref_{abs,tot} in tics. This representation is then converted
// to computation time steps again by a strategy defined by class
// nest::Time.
// 2. The refractory time in units of steps is read out get_steps(), a
// member function of class nest::Time.
//
// Choosing a tau_ref_{abs,tot} that is not an integer multiple of the
// computation time step h will lead to accurate (up to the resolution h)
// and self- consistent results. However, a neuron model capable of
// operating with real valued spike time may exhibit a different
// effective refractory time.
V_.RefractoryCountsAbs_ = Time( Time::ms( P_.tau_ref_abs_ ) ).get_steps();
V_.RefractoryCountsTot_ = Time( Time::ms( P_.tau_ref_tot_ ) ).get_steps();
if ( V_.RefractoryCountsAbs_ < 1 )
{
throw BadProperty(
"Absolute refractory time must be at least one time step." );
}
if ( V_.RefractoryCountsTot_ < 1 )
{
throw BadProperty(
"Total refractory time must be at least one time step." );
}
}
