aeif_cond_alpha.h

Go to the documentation of this file.

/*
 *  aeif_cond_alpha.h
 *
 *  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/>.
 *
 */
 
#ifndef AEIF_COND_ALPHA_H
#define AEIF_COND_ALPHA_H
 
// Generated includes:
#include "config.h"
 
#ifdef HAVE_GSL
 
// External includes:
#include <gsl/gsl_errno.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_odeiv.h>
 
// Includes from nestkernel:
#include "archiving_node.h"
#include "connection.h"
#include "event.h"
#include "nest_types.h"
#include "recordables_map.h"
#include "ring_buffer.h"
#include "universal_data_logger.h"
 
namespace nest
{
extern "C" int aeif_cond_alpha_dynamics( double, const double*, double*, void* );
 
extern "C" int aeif_cond_alpha_dynamics_DT0( double, const double*, double*, void* );
 
/* BeginUserDocs: neuron, integrate-and-fire, adaptation, conductance-based, soft threshold
 
Short description
+++++++++++++++++
 
Conductance based exponential integrate-and-fire neuron model
 
Description
+++++++++++
 
``aeif_cond_alpha`` is the adaptive exponential integrate and fire neuron according
to Brette and Gerstner (2005).
Synaptic conductances are modelled as alpha-functions.
 
This implementation uses the embedded 4th order Runge-Kutta-Fehlberg solver with
adaptive step size to integrate the differential equation.
 
The membrane potential is given by the following differential equation:
 
.. math::
 
 C_m \frac{dV}{dt} =
 -g_L(V-E_L)+g_L\Delta_T\exp\left(\frac{V-V_{th}}{\Delta_T}\right) -
 g_e(t)(V-E_e) \\
                                                     -g_i(t)(V-E_i)-w +I_e
 
and
 
.. math::
 
 \tau_w \frac{dw}{dt} = a(V-E_L) - w
 
For the reference implementation of this model, see
`aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
 
.. note::
 
    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
    Brette & Gerstner :footcite:p:`Brette2005`.  Thus, an ``aeif`` neuron with default parameters can fire multiple
    spikes in a single time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
 
    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
 
Parameters
++++++++++
 
The following parameters can be set in the status Dictionary.
 
======== ======= =======================================
**Dynamic state variables:**
--------------------------------------------------------
 V_m     mV      Membrane potential
 g_ex    nS      Excitatory synaptic conductance
 dg_ex   nS/ms   First derivative of g_ex
 g_in    nS      Inhibitory synaptic conductance
 dg_in   nS/ms   First derivative of g_in
 w       pA      Spike-adaptation current
======== ======= =======================================
 
======== ======= =======================================
**Membrane Parameters**
--------------------------------------------------------
 C_m     pF      Capacity of the membrane
 t_ref   ms      Duration of refractory period
 V_reset mV      Reset value for V_m after a spike
 E_L     mV      Leak reversal potential
 g_L     nS      Leak conductance
 I_e     pA      Constant external input current
======== ======= =======================================
 
======== ======= ==================================
**Spike adaptation parameters**
---------------------------------------------------
 a       ns      Subthreshold adaptation
 b       pA      Spike-triggered adaptation
 Delta_T mV      Slope factor
 tau_w   ms      Adaptation time constant
 V_th    mV      Spike initiation threshold
 V_peak  mV      Spike detection threshold
======== ======= ==================================
 
=========== ======= ===========================================================
**Synaptic parameters**
-------------------------------------------------------------------------------
 E_ex       mV      Excitatory reversal potential
 tau_syn_ex ms      Rise time of excitatory synaptic conductance (alpha
                    function)
 E_in       mV      Inhibitory reversal potential
 tau_syn_in ms      Rise time of the inhibitory synaptic conductance
                    (alpha function)
=========== ======= ===========================================================
 
============= ======= =========================================================
**Integration parameters**
-------------------------------------------------------------------------------
gsl_error_tol real    This parameter controls the admissible error of the
                      GSL integrator. Reduce it if NEST complains about
                      numerical instabilities.
============= ======= =========================================================
 
Sends
+++++
 
SpikeEvent
 
Receives
++++++++
 
SpikeEvent, CurrentEvent, DataLoggingRequest
 
References
++++++++++
 
.. footbibliography::
 
See also
++++++++
 
iaf_cond_alpha, aeif_cond_exp
 
Examples using this model
+++++++++++++++++++++++++
 
.. listexamples:: aeif_cond_alpha
 
EndUserDocs */
 
void register_aeif_cond_alpha( const std::string& name );
 
class aeif_cond_alpha : public ArchivingNode
{
 
public:
  aeif_cond_alpha();
  aeif_cond_alpha( const aeif_cond_alpha& );
  ~aeif_cond_alpha() override;
 
  using Node::handle;
  using Node::handles_test_event;
 
  size_t send_test_event( Node&, size_t, synindex, bool ) override;
 
  void handle( SpikeEvent& ) override;
  void handle( CurrentEvent& ) override;
  void handle( DataLoggingRequest& ) override;
 
  size_t handles_test_event( SpikeEvent&, size_t ) override;
  size_t handles_test_event( CurrentEvent&, size_t ) override;
  size_t handles_test_event( DataLoggingRequest&, size_t ) override;
 
  void get_status( Dictionary& ) const override;
  void set_status( const Dictionary& ) override;
 
private:
  void init_buffers_() override;
  void pre_run_hook() override;
  void update( Time const&, const long, const long ) override;
 
  // END Boilerplate function declarations ----------------------------
 
  // Friends --------------------------------------------------------
 
  // make dynamics function quasi-member
  friend int aeif_cond_alpha_dynamics( double, const double*, double*, void* );
 
  // The next two classes need to be friends to access the State_ class/member
  friend class RecordablesMap< aeif_cond_alpha >;
  friend class UniversalDataLogger< aeif_cond_alpha >;
 
private:
  // ----------------------------------------------------------------
 
  struct Parameters_
  {
    double V_peak_;   
    double V_reset_;  
    double t_ref_;    
 
    double g_L;         
    double C_m;         
    double E_ex;        
    double E_in;        
    double E_L;         
    double Delta_T;     
    double tau_w;       
    double a;           
    double b;           
    double V_th;        
    double tau_syn_ex;  
    double tau_syn_in;  
    double I_e;         
 
    double gsl_error_tol;  
 
    Parameters_();  
 
    void get( Dictionary& ) const;              
    void set( const Dictionary&, Node* node );  
  };
 
public:
  // ----------------------------------------------------------------
 
  struct State_
  {
    enum StateVecElems
    {
      V_M = 0,
      DG_EXC,  // 1
      G_EXC,   // 2
      DG_INH,  // 3
      G_INH,   // 4
      W,       // 5
      STATE_VEC_SIZE
    };
 
    double y_[ STATE_VEC_SIZE ];  
    unsigned int r_;              
 
    State_( const Parameters_& );  
    State_( const State_& );
 
    State_& operator=( const State_& );
 
    void get( Dictionary& ) const;
    void set( const Dictionary&, const Parameters_&, Node* );
  };
 
  // ----------------------------------------------------------------
 
  struct Buffers_
  {
    Buffers_( aeif_cond_alpha& );                   
    Buffers_( const Buffers_&, aeif_cond_alpha& );  
 
    UniversalDataLogger< aeif_cond_alpha > logger_;
 
    RingBuffer spike_exc_;
    RingBuffer spike_inh_;
    RingBuffer currents_;
 
    gsl_odeiv_step* s_;     
    gsl_odeiv_control* c_;  
    gsl_odeiv_evolve* e_;   
    gsl_odeiv_system sys_;  
 
    // Since IntegrationStep_ is initialized with step_, and the resolution
    // cannot change after nodes have been created, it is safe to place both
    // here.
    double step_;             
    double IntegrationStep_;  
 
    double I_stim_;
  };
 
  // ----------------------------------------------------------------
 
  struct Variables_
  {
    double g0_ex_;
 
    double g0_in_;
 
    double V_peak;
 
    unsigned int refractory_counts_;
  };
 
  // Access functions for UniversalDataLogger -------------------------------
 
  template < State_::StateVecElems elem >
  double
  get_y_elem_() const
  {
    return S_.y_[ elem ];
  }
 
  // ----------------------------------------------------------------
 
  Parameters_ P_;
  State_ S_;
  Variables_ V_;
  Buffers_ B_;
 
  static RecordablesMap< aeif_cond_alpha > recordablesMap_;
};
 
inline size_t
aeif_cond_alpha::send_test_event( Node& target, size_t receptor_type, synindex, bool )
{
  SpikeEvent e;
  e.set_sender( *this );
 
  return target.handles_test_event( e, receptor_type );
}
 
inline size_t
aeif_cond_alpha::handles_test_event( SpikeEvent&, size_t receptor_type )
{
  if ( receptor_type != 0 )
  {
    throw UnknownReceptorType( receptor_type, get_name() );
  }
  return 0;
}
 
inline size_t
aeif_cond_alpha::handles_test_event( CurrentEvent&, size_t receptor_type )
{
  if ( receptor_type != 0 )
  {
    throw UnknownReceptorType( receptor_type, get_name() );
  }
  return 0;
}
 
inline size_t
aeif_cond_alpha::handles_test_event( DataLoggingRequest& dlr, size_t receptor_type )
{
  if ( receptor_type != 0 )
  {
    throw UnknownReceptorType( receptor_type, get_name() );
  }
  return B_.logger_.connect_logging_device( dlr, recordablesMap_ );
}
 
inline void
aeif_cond_alpha::get_status( Dictionary& d ) const
{
  P_.get( d );
  S_.get( d );
  ArchivingNode::get_status( d );
 
  d[ names::recordables ] = recordablesMap_.get_list();
}
 
inline void
aeif_cond_alpha::set_status( const Dictionary& d )
{
  Parameters_ ptmp = P_;      // temporary copy in case of errors
  ptmp.set( d, this );        // throws if BadProperty
  State_ stmp = S_;           // temporary copy in case of errors
  stmp.set( d, ptmp, this );  // throws if BadProperty
 
  // We now know that (ptmp, stmp) are consistent. We do not
  // write them back to (P_, S_) before we are also sure that
  // the properties to be set in the parent class are internally
  // consistent.
  ArchivingNode::set_status( d );
 
  // if we get here, temporaries contain consistent set of properties
  P_ = ptmp;
  S_ = stmp;
}
 
}  // namespace
 
#endif  // HAVE_GSL
#endif  // AEIF_COND_ALPHA_H