gif_psc_exp.h

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/*
 *  gif_psc_exp.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/>.
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 */
 
#ifndef GIF_PSC_EXP_H
#define GIF_PSC_EXP_H
 
// Includes from nestkernel:
#include "archiving_node.h"
#include "connection.h"
#include "event.h"
#include "ring_buffer.h"
#include "universal_data_logger.h"
 
#include "nest.h"
 
namespace nest
{
 
/* BeginUserDocs: neuron, integrate-and-fire, current-based, adaptation, stochastic
 
Short description
+++++++++++++++++
 
Current-based generalized integrate-and-fire neuron (GIF) model (from the Gerstner lab)
 
Description
+++++++++++
 
``gif_psc_exp`` is the generalized integrate-and-fire neuron according to
Mensi et al. (2012) :footcite:p:`Mensi2012` and Pozzorini et al. (2015) :footcite:p:`Pozzorini2015`, with exponential
shaped postsynaptic currents.
 
This model features both an adaptation current and a dynamic threshold for
spike-frequency adaptation. The membrane potential (V) is described by the
differential equation:
 
.. math::
 
 C \cdot dV(t)/dt = -g_L \cdot (V(t)-E_L) - \eta_1(t) - \eta_2(t) - \ldots
    - \eta_n(t) + I(t)
 
where each :math:`\eta_i` is a spike-triggered current (stc), and the neuron
model can have arbitrary number of them.
Dynamic of each :math:`\eta_i` is described by:
 
.. math::
 
 \tau_\eta{_i} \cdot d{\eta_i}/dt = -\eta_i
 
and in case of spike emission, its value increased by a constant (which can be
positive or negative):
 
.. math::
 
 \eta_i = \eta_i + q_{\eta_i} \text{ (in case of spike emission).}
 
Neuron produces spikes stochastically according to a point process with the
firing intensity:
 
.. math::
 
 \lambda(t) = \lambda_0 \cdot \exp (V(t)-V_T(t)) / \Delta_V
 
where :math:`V_T(t)` is a time-dependent firing threshold:
 
.. math::
 
 V_T(t) = V_{T_{star}} + \gamma_1(t) + \gamma_2(t) + \ldots + \gamma_m(t)
 
where :math:`\gamma_i` is a kernel of spike-frequency adaptation (sfa), and the
neuron model can have arbitrary number of them.
Dynamic of each :math:`\gamma_i` is described by:
 
.. math::
 
   \tau_{\gamma_i} \cdot d\gamma_i/dt = -\gamma_i
 
and in case of spike emission, its value increased by a constant (which can be
positive or negative):
 
.. math::
 
   \gamma_i = \gamma_i + q_{\gamma_i}  \text{ (in case of spike emission).}
 
 
Note:
 
In the current implementation of the model, the values of
:math:`\eta_i` and :math:`\gamma_i` are affected immediately after spike
emission. However, `GIF toolbox <http://wiki.epfl.ch/giftoolbox>`_, which
fits the model using experimental data, requires a different set of
:math:`\eta_i` and :math:`\gamma_i`. It applies the jump of
:math:`\eta_i` and :math:`\gamma_i` after the refractory period. One can
easily convert between :math:`q_\eta/\gamma` of these two approaches:
 
.. math::
 
  q{_\eta}_{giftoolbox} = q_{\eta_{NEST}} \cdot (1 - \exp( -\tau_{ref} /
   \tau_\eta ))
 
The same formula applies for :math:`q_{\gamma}`.
 
 
The shape of postsynaptic current is exponential.
 
.. note::
 
  If ``tau_m`` is very close to ``tau_syn_ex`` or ``tau_syn_in``, the model
  will numerically behave as if ``tau_m`` is equal to `tau_syn_ex` or
  ``tau_syn_in``, respectively, to avoid numerical instabilities.
 
  For implementation details see the
  `IAF Integration Singularity notebook <../model_details/IAF_Integration_Singularity.ipynb>`_.
 
Parameters
++++++++++
 
The following parameters can be set in the status dictionary.
 
======== ======= ================================================
**Membrane Parameters**
-----------------------------------------------------------------
 C_m        pF    Capacitance of the membrane
 t_ref      ms    Duration of refractory period
 V_reset    mV    Membrane potential is reset to this value after
                  a spike
 E_L        mV    Resting potential
 g_L        nS    Leak conductance
 I_e        pA    Constant input current
======== ======= ================================================
 
=========  ========== ====================================================
**Spike adaptation and firing intensity parameters**
--------------------------------------------------------------------------
q_stc      list of nA   Values added to spike-triggered currents (stc)
                        after each spike emission
tau_stc    list of ms   Time constants of stc variables
q_sfa      list of mV   Values added to spike-frequency adaptation
                        (sfa) after each spike emission
tau_sfa    list of ms   Time constants of sfa variables
Delta_V    mV           Stochasticity level
lambda_0   1/s          Stochastic intensity at firing threshold V_T
V_T_star   mV           Base threshold
=========  ========== ====================================================
 
=========== ======= ===========================================================
**Synaptic parameters**
-------------------------------------------------------------------------------
 tau_syn_ex ms      Time constant of excitatory synaptic conductance
 tau_syn_in ms      Time constant of the inhibitory synaptic conductance
=========== ======= ===========================================================
 
References
++++++++++
 
.. footbibliography::
 
Sends
+++++
 
SpikeEvent
 
Receives
++++++++
 
SpikeEvent, CurrentEvent, DataLoggingRequest
 
See also
++++++++
 
pp_psc_delta, gif_psc_exp_multisynapse, gif_cond_exp, gif_cond_exp_multisynapse, gif_pop_psc_exp
 
Examples using this model
+++++++++++++++++++++++++
 
.. listexamples:: gif_psc_exp
 
EndUserDocs */
 
void register_gif_psc_exp( const std::string& name );
 
class gif_psc_exp : public ArchivingNode
{
 
public:
  gif_psc_exp();
  gif_psc_exp( const gif_psc_exp& );
 
  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;
 
  // The next two classes need to be friends to access the State_ class/member
  friend class RecordablesMap< gif_psc_exp >;
  friend class UniversalDataLogger< gif_psc_exp >;
 
  // ----------------------------------------------------------------
 
  struct Parameters_
  {
    double g_L_;
    double E_L_;
    double V_reset_;
    double Delta_V_;
    double V_T_star_;
    double lambda_0_; 
    double t_ref_;
 
    double c_m_;
 
    std::vector< double > tau_stc_;
 
    std::vector< double > q_stc_;
 
    std::vector< double > tau_sfa_;
 
    std::vector< double > q_sfa_;
 
    double tau_ex_;
 
    double tau_in_;
 
    double I_e_;
 
    Parameters_();  
 
    void get( Dictionary& ) const;              
    void set( const Dictionary&, Node* node );  
  };
 
  // ----------------------------------------------------------------
 
  struct State_
  {
    double I_stim_;  
    double V_;       
    double sfa_;     
    double stc_;     
 
    std::vector< double > sfa_elems_;  
    std::vector< double > stc_elems_;  
 
    double I_syn_ex_;  
    double I_syn_in_;  
 
    unsigned int r_ref_;  
 
    State_();  
 
    void get( Dictionary&, const Parameters_& ) const;
    void set( const Dictionary&, const Parameters_&, Node* );
  };
 
  // ----------------------------------------------------------------
 
  struct Buffers_
  {
    Buffers_( gif_psc_exp& );
    Buffers_( const Buffers_&, gif_psc_exp& );
 
    RingBuffer spikes_ex_;
    RingBuffer spikes_in_;
    RingBuffer currents_;
 
    UniversalDataLogger< gif_psc_exp > logger_;
  };
 
  // ----------------------------------------------------------------
 
  struct Variables_
  {
    double P30_;    // coefficient for solving membrane potential equation
    double P33_;    // decay term of membrane potential
    double P31_;    // coefficient for solving membrane potential equation
    double P11ex_;  // decay terms of excitatory synaptic currents
    double P11in_;  // decay terms of inhibitory synaptic currents
    double P21ex_;  // coefficient for solving membrane potential equation
    double P21in_;  // coefficient for solving membrane potential equation
 
    std::vector< double > P_sfa_;  // decay terms of spike-triggered current elements
    std::vector< double > P_stc_;  // decay terms of adaptive threshold elements
 
    RngPtr rng_;  // random number generator of my own thread
 
    unsigned int RefractoryCounts_;
  };
 
  // Access functions for UniversalDataLogger -----------------------
 
  double
  get_V_m_() const
  {
    return S_.V_;
  }
 
  double
  get_E_sfa_() const
  {
    return S_.sfa_;
  }
 
  double
  get_I_stc_() const
  {
    return S_.stc_;
  }
 
  double
  get_I_syn_ex_() const
  {
    return S_.I_syn_ex_;
  }
 
  double
  get_I_syn_in_() const
  {
    return S_.I_syn_in_;
  }
 
  // ----------------------------------------------------------------
 
  Parameters_ P_;
  State_ S_;
  Variables_ V_;
  Buffers_ B_;
  static RecordablesMap< gif_psc_exp > recordablesMap_;
};
 
inline size_t
gif_psc_exp::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
gif_psc_exp::handles_test_event( SpikeEvent&, size_t receptor_type )
{
  if ( receptor_type != 0 )
  {
    throw UnknownReceptorType( receptor_type, get_name() );
  }
  return 0;
}
 
inline size_t
gif_psc_exp::handles_test_event( CurrentEvent&, size_t receptor_type )
{
  if ( receptor_type != 0 )
  {
    throw UnknownReceptorType( receptor_type, get_name() );
  }
  return 0;
}
 
inline size_t
gif_psc_exp::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
gif_psc_exp::get_status( Dictionary& d ) const
{
  P_.get( d );
  S_.get( d, P_ );
  ArchivingNode::get_status( d );
  d[ names::recordables ] = recordablesMap_.get_list();
}
 
inline void
gif_psc_exp::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 /* #ifndef GIF_PSC_EXP_H */