/*
* iaf_tum_2000.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_TUM_2000_H
#define IAF_TUM_2000_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_tum_2000 - Leaky integrate-and-fire neuron model with exponential PSCs.
Description:
iaf_tum_2000 is an implementation of a leaky integrate-and-fire model
with exponential shaped postsynaptic currents (PSCs) according to [1].
The postsynaptic currents have an infinitely short rise time.
In particular, this model allows setting an absolute and relative
refractory time separately, as requied by [1].
The threshold crossing is followed by an absolute refractory period (tau_abs)
during which the membrane potential is clamped to the resting potential.
During the total refractory period, the membrane potential evolves,
but the neuron will not emit a spike, even if the membrane potential
reaches threshold. The total refratory time must be larger or equal to
the absolute refractory time. If equal, the refractoriness of the model
if equivalent to the other models of NEST.
The linear subthresold dynamics is integrated by the Exact
Integration scheme [2]. 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
[2]. A flow chart can be found in [3].
Remarks:
The present implementation uses individual variables for the
components of the state vector and the non-zero matrix elements of
the propagator. Because the propagator is a lower triangular matrix
no full matrix multiplication needs to be carried out and the
computation can be done "in place" i.e. no temporary state vector
object is required.
The template support of recent C++ compilers enables a more succinct
formulation without loss of runtime performance already at minimal
optimization levels. A future version of iaf_tum_2000 will probably
address the problem of efficient usage of appropriate vector and
matrix objects.
Parameters:
The following parameters can be set in the status dictionary.
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.
tau_syn_ex double - Time constant of postsynaptic excitatory currents in ms
tau_syn_in double - Time constant of postsynaptic inhibitory currents in ms
t_ref_abs double - Duration of absolute refractory period (V_m = V_reset) in ms.
t_ref_tot double - Duration of total refractory period (no spiking) in ms.
V_m double - Membrane potential in mV
V_th double - Spike threshold in mV.
V_reset double - Reset membrane potential after a spike in mV.
I_e double - Constant input current in pA.
t_spike double - Point in time of last spike in ms.
Note:
tau_m != tau_syn_{ex,in} 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] Misha Tsodyks, Asher Uziel, and Henry Markram (2000) Synchrony Generation in Recurrent
Networks with Frequency-Dependent Synapses, The Journal of Neuroscience, 2000, Vol. 20 RC50 p. 1-5
[2] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
systems with applications to neuronal modeling. Biologial Cybernetics
81:381-402.
[3] 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.
Sends: SpikeEvent
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
FirstVersion: March 2006
Author: Moritz Helias
*/
/**
* Leaky integrate-and-fire neuron with exponential PSCs.
*/
class iaf_tum_2000:
public Archiving_Node
{
public:
typedef Node base;
iaf_tum_2000();
iaf_tum_2000(const iaf_tum_2000&);
/**
* Import sets of overloaded virtual functions.
* We need to explicitly include sets of overloaded
* virtual functions into the current scope.
* According to the SUN C++ FAQ, this is the correct
* way of doing things, although all other compilers
* happily live without.
*/
using Node::connect_sender;
using Node::handle;
port check_connection(Connection&, port);
void handle(SpikeEvent &);
void handle(CurrentEvent &);
void handle(DataLoggingRequest &);
port connect_sender(SpikeEvent &, port);
port connect_sender(CurrentEvent &, port);
port connect_sender(DataLoggingRequest &, port);
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_tum_2000>;
friend class UniversalDataLogger<iaf_tum_2000>;
// ----------------------------------------------------------------
/**
* Independent parameters of the model.
*/
struct Parameters_ {
/** Membrane time constant in ms. */
double_t Tau_;
/** Membrane capacitance in pF. */
double_t C_;
/** Refractory period in ms. */
double_t tau_ref_tot_;
double_t tau_ref_abs_;
/** Resting potential in mV. */
double_t U0_;
/** External current in pA */
double_t I_e_;
/** Threshold, RELATIVE TO RESTING POTENTAIL(!).
I.e. the real threshold is (U0_+Theta_). */
double_t Theta_;
/** reset value of the membrane potential */
double_t V_reset_;
/** Time constant of excitatory synaptic current in ms. */
double_t tau_ex_;
/** Time constant of inhibitory synaptic current in ms. */
double_t tau_in_;
Parameters_(); //!< Sets default parameter values
void get(DictionaryDatum&) const; //!< Store current values in dictionary
/** Set values from dictionary.
* @returns Change in reversal potential E_L, to be passed to State_::set()
*/
double set(const DictionaryDatum&);
};
// ----------------------------------------------------------------
/**
* State variables of the model.
*/
struct State_ {
// state variables
double_t i_0_; // synaptic dc input current, variable 0
double_t i_syn_ex_; // postsynaptic current for exc. inputs, variable 1
double_t i_syn_in_; // postsynaptic current for inh. inputs, variable 1
double_t V_m_; // membrane potential, variable 2
int_t r_abs_; // absolute refractory counter (no membrane potential propagation)
int_t r_tot_; // total refractory counter (no spikes can be generated)
State_(); //!< Default initialization
void get(DictionaryDatum&, const Parameters_&) const;
/** Set values from dictionary.
* @param dictionary to take data from
* @param current parameters
* @param Change in reversal potential E_L specified by this dict
*/
void set(const DictionaryDatum&, const Parameters_&, double delta_EL);
};
// ----------------------------------------------------------------
/**
* Buffers of the model.
*/
struct Buffers_ {
Buffers_(iaf_tum_2000 &);
Buffers_(const Buffers_ &, iaf_tum_2000 &);
/** buffers and sums up incoming spikes/currents */
RingBuffer spikes_ex_;
RingBuffer spikes_in_;
RingBuffer currents_;
//! Logger for all analog data
UniversalDataLogger<iaf_tum_2000> logger_;
};
// ----------------------------------------------------------------
/**
* Internal variables of the model.
*/
struct Variables_ {
/** Amplitude of the synaptic current.
This value is chosen such that a post-synaptic potential with
weight one has an amplitude of 1 mV.
@note mog - I assume this, not checked.
*/
// double_t PSCInitialValue_;
// time evolution operator
double_t P20_;
double_t P11ex_;
double_t P11in_;
double_t P21ex_;
double_t P21in_;
double_t P22_;
int_t RefractoryCountsAbs_;
int_t RefractoryCountsTot_;
};
// Access functions for UniversalDataLogger -------------------------------
//! Read out the real membrane potential
double_t get_V_m_() const { return S_.V_m_; }
double_t get_I_syn_ex_() const { return S_.i_syn_ex_; }
double_t get_I_syn_in_() const { return S_.i_syn_in_; }
// ----------------------------------------------------------------
/**
* @defgroup iaf_tum_2000_data
* Instances of private data structures for the different types
* of data pertaining to the model.
* @note The order of definitions is important for speed.
* @{
*/
Parameters_ P_;
State_ S_;
Variables_ V_;
Buffers_ B_;
/** @} */
//! Mapping of recordables names to access functions
static RecordablesMap<iaf_tum_2000> recordablesMap_;
};
inline
port iaf_tum_2000::check_connection(Connection& c, port receptor_type)
{
SpikeEvent e;
e.set_sender(*this);
c.check_event(e);
return c.get_target()->connect_sender(e, receptor_type);
}
inline
port iaf_tum_2000::connect_sender(SpikeEvent&, port receptor_type)
{
if (receptor_type != 0)
throw UnknownReceptorType(receptor_type, get_name());
return 0;
}
inline
port iaf_tum_2000::connect_sender(CurrentEvent&, port receptor_type)
{
if (receptor_type != 0)
throw UnknownReceptorType(receptor_type, get_name());
return 0;
}
inline
port iaf_tum_2000::connect_sender(DataLoggingRequest &dlr,
port receptor_type)
{
if (receptor_type != 0)
throw UnknownReceptorType(receptor_type, get_name());
return B_.logger_.connect_logging_device(dlr, recordablesMap_);
}
inline
void iaf_tum_2000::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_tum_2000::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 //IAF_TUM_2000_H