iaf_neuron.h

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/*
 *  iaf_neuron.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 IAF_NEURON_H
#define IAF_NEURON_H

#include "nest.h"
#include "event.h"
#include "archiving_node.h"
#include "ring_buffer.h"
#include "connection.h"
#include "universal_data_logger.h"

namespace nest
{
  class Network;

  /* BeginDocumentation
Name: iaf_neuron - Leaky integrate-and-fire neuron model.

Description:

  iaf_neuron is an implementation of a leaky integrate-and-fire model
  with alpha-function shaped synaptic currents. Thus, synaptic currents
  and the resulting post-synaptic potentials have a finite rise time. 
  The threshold crossing is followed by an absolute refractory period
  during which the membrane potential is clamped to the resting potential.

  The subthreshold membrane potential dynamics are given by [3]

  dV_m/dt = - ( V_m - E_L ) / tau_m + I_syn(t) / C_m + I_e / C_m

  where I_syn(t) is the sum of alpha-shaped synaptic currents

  I_syn(t) = Sum[w_j alpha(t-t_j) for t_j in input spike times]

  w_j is the synaptic weight of the connection through which the spike
  at time t_j arrived. Each individual alpha-current is given by

  alpha(t) = e * t/tau_s * e^{-t/tau_s} * Heaviside(t)

  alpha(t=tau_s) == 1 is the maximum of the alpha-current.

  The linear subthresold dynamics is integrated by the Exact
  Integration scheme [1]. The neuron dynamics is solved on the time
  grid given by the computation step size. Incoming as well as emitted
  spikes are forced to that grid.  

  An additional state variable and the corresponding differential
  equation represents a piecewise constant external current.

  The general framework for the consistent formulation of systems with
  neuron like dynamics interacting by point events is described in
  [1].  A flow chart can be found in [2].

  Critical tests for the formulation of the neuron model are the
  comparisons of simulation results for different computation step
  sizes. sli/testsuite/nest contains a number of such tests.
  
  The iaf_neuron is the standard model used to check the consistency
  of the nest simulation kernel because it is at the same time complex
  enough to exhibit non-trivial dynamics and simple enough to compute
  relevant measures analytically.


Parameters: 

  The following parameters can be set in the status dictionary.

  V_m        double - Membrane potential in mV 
  E_L        double - Resting membrane potential in mV. 
  C_m        double - Capacity of the membrane in pF
  tau_m      double - Membrane time constant in ms.
  t_ref      double - Duration of refractory period in ms. 
  V_th       double - Spike threshold in mV.
  V_reset    double - Reset potential of the membrane in mV.
  tau_syn    double - Rise time of the excitatory synaptic alpha function in ms.
  I_e        double - Constant external input current in pA.

Note:
  tau_m != tau_syn is required by the current implementation to avoid a
  degenerate case of the ODE describing the model [1]. For very similar values,
  numerics will be unstable.
 
References:
  [1] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
      systems with applications to neuronal modeling. Biologial Cybernetics
      81:381-402.
  [2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001) State space 
      analysis of synchronous spiking in cortical neural networks. 
      Neurocomputing 38-40:565-571.
  [3] Morrison A, Straube S, Plesser H E, & Diesmann M (2007) Exact subthreshold 
      integration with continuous spike times in discrete time neural network 
      simulations. Neural Computation 19:47-79.

Sends: SpikeEvent

Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
      
Author:  September 1999, Diesmann, Gewaltig
SeeAlso: iaf_psc_alpha, testsuite::test_iaf
*/

  class iaf_neuron: public Archiving_Node
  {
    
  public:
    
    iaf_neuron();
    iaf_neuron(const iaf_neuron&);

    using Node::handle;
    using Node::handles_test_event;

    port send_test_event(Node&, rport, synindex, bool);
    
    void handle(SpikeEvent &);
    void handle(CurrentEvent &);
    void handle(DataLoggingRequest &);
    
    port handles_test_event(SpikeEvent&, rport);
    port handles_test_event(CurrentEvent&, rport);
    port handles_test_event(DataLoggingRequest&, rport);

    void get_status(DictionaryDatum &) const;
    void set_status(const DictionaryDatum &);

  private:

    void init_state_(const Node& proto);
    void init_buffers_();
    void calibrate();

    void update(Time const &, const long_t, const long_t); 

    // The next two classes need to be friends to access the State_ class/member
    friend class RecordablesMap<iaf_neuron>;
    friend class UniversalDataLogger<iaf_neuron>;
     
    // ---------------------------------------------------------------- 

    struct Parameters_ {
      double_t C_;
    
      double_t Tau_; 

      double_t tau_syn_;
      
      double_t TauR_;

      double_t U0_;

      double_t V_reset_;

      double_t Theta_;

      double_t I_e_;

      Parameters_();  

      void get(DictionaryDatum&) const;  

      double set(const DictionaryDatum&);
    };

    // ---------------------------------------------------------------- 

    struct State_ {
      double_t y0_; 
      double_t y1_;  
      double_t y2_;
      double_t y3_; 

      int_t    r_;  

      State_();  
      
      void get(DictionaryDatum&, const Parameters_&) const;

      void set(const DictionaryDatum&, const Parameters_&, double);
    };
    
    // ---------------------------------------------------------------- 

    struct Buffers_ {
      Buffers_(iaf_neuron &);
      Buffers_(const Buffers_ &, iaf_neuron &);

      RingBuffer spikes_;
      RingBuffer currents_;

      UniversalDataLogger<iaf_neuron> logger_;
    };
    
    // ---------------------------------------------------------------- 

    struct Variables_ { 
     double_t PSCInitialValue_;
     int_t    RefractoryCounts_;  
    
     double_t P11_;   
     double_t P21_;
     double_t P22_;
     double_t P31_;
     double_t P32_;
     double_t P30_;
     double_t P33_;     
   };

    // Access functions for UniversalDataLogger -------------------------------

    double_t get_V_m_() const { return S_.y3_ + P_.U0_; }

   // ---------------------------------------------------------------- 

   Parameters_ P_;
   State_      S_;
   Variables_  V_;
   Buffers_    B_;

   static RecordablesMap<iaf_neuron> recordablesMap_;

};

inline
port iaf_neuron::send_test_event(Node& target, rport receptor_type, synindex, bool)
{
  SpikeEvent e;
  e.set_sender(*this);
  
  return target.handles_test_event(e, receptor_type);
}

inline
port iaf_neuron::handles_test_event(SpikeEvent&, rport receptor_type)
{
  if (receptor_type != 0)
    throw UnknownReceptorType(receptor_type, get_name());
  return 0;
}

inline
port iaf_neuron::handles_test_event(CurrentEvent&, rport receptor_type)
{
  if (receptor_type != 0)
    throw UnknownReceptorType(receptor_type, get_name());
  return 0;
}

inline
port iaf_neuron::handles_test_event(DataLoggingRequest &dlr, 
                    rport receptor_type)
{
  if (receptor_type != 0)
    throw UnknownReceptorType(receptor_type, get_name());
  return B_.logger_.connect_logging_device(dlr, recordablesMap_);
}

inline
void iaf_neuron::get_status(DictionaryDatum &d) const
{
  P_.get(d);
  S_.get(d, P_);
  Archiving_Node::get_status(d);
  (*d)[names::recordables] = recordablesMap_.get_list();
}

inline
void iaf_neuron::set_status(const DictionaryDatum &d)
{
  Parameters_ ptmp = P_;  // temporary copy in case of errors
  const double delta_EL = ptmp.set(d); // throws if BadProperty
  State_      stmp = S_;  // temporary copy in case of errors
  stmp.set(d, ptmp, delta_EL);         // 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.
  Archiving_Node::set_status(d);

  // if we get here, temporaries contain consistent set of properties
  P_ = ptmp;
  S_ = stmp;
}

} // namespace

#endif /* #ifndef IAF_NEURON_H */