/*
* mat2_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/>.
*
*/
#ifndef MAT2_PSC_EXP_H
#define MAT2_PSC_EXP_H
#include "nest.h"
#include "event.h"
#include "archiving_node.h"
#include "ring_buffer.h"
#include "connection.h"
#include "universal_data_logger.h"
#include "recordables_map.h"
namespace nest
{
class Network;
/* BeginDocumentation
Name: mat2_psc_exp - Non-resetting leaky integrate-and-fire neuron model with
exponential PSCs and adaptive threshold.
Description:
mat2_psc_exp is an implementation of a leaky integrate-and-fire model
with exponential shaped postsynaptic currents (PSCs). Thus, postsynaptic currents
have an infinitely short rise time.
The threshold is lifted when the neuron is fired and then decreases in a fixed
time scale toward a fixed level [3].
The threshold crossing is followed by a total refractory period
during which the neuron is not allowed to fire, even if the membrane
potential exceeds the threshold. The membrane potential is NOT reset,
but continuously integrated.
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].
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.
Parameters:
The following parameters can be set in the status dictionary:
C_m double - Capacity of the membrane in pF
E_L double - Resting potential in mV
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 double - Duration of absolute refractory period (no spiking) in ms
V_m double - Membrane potential in mV
I_e double - Constant input current in pA
t_spike double - Point in time of last spike in ms
tau_1 double - Short time constant of adaptive threshold in ms
tau_2 double - Long time constant of adaptive threshold in ms
alpha_1 double - Amplitude of short time threshold adaption in mV [3]
alpha_2 double - Amplitude of long time threshold adaption in mV [3]
omega double - Resting spike threshold in mV (absolute value, not relative to E_L as in [3])
The following state variables can be read out with the multimeter device:
V_m Non-resetting membrane potential
V_th Two-timescale adaptive threshold
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] 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] Kobayashi R, Tsubo Y and Shinomoto S (2009) Made-to-order
spiking neuron model equipped with a multi-timescale adaptive
threshold. Front. Comput. Neurosci. 3:9. doi:10.3389/neuro.10.009.2009
Sends: SpikeEvent
Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
FirstVersion: Mai 2009
Author: Thomas Pfeil (modified iaf_psc_exp model of Moritz Helias)
*/
/**
* Non-resetting leaky integrate-and-fire neuron model with
exponential PSCs and adaptive threshold.
*/
class mat2_psc_exp:
public Archiving_Node
{
public:
typedef Node base;
mat2_psc_exp();
mat2_psc_exp(const mat2_psc_exp&);
/**
* 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);
port connect_sender(SpikeEvent &, port);
port connect_sender(CurrentEvent &, port);
port connect_sender(DataLoggingRequest &, port);
void handle(SpikeEvent &);
void handle(CurrentEvent &);
void handle(DataLoggingRequest &);
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 private members
friend class RecordablesMap<mat2_psc_exp>;
friend class UniversalDataLogger<mat2_psc_exp>;
// ----------------------------------------------------------------
/**
* 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_;
/** Resting potential in mV. */
double_t U0_;
/** External current in pA */
double_t I_e_;
/** Time constant of excitatory synaptic current in ms. */
double_t tau_ex_;
/** Time constant of inhibitory synaptic current in ms. */
double_t tau_in_;
/** Short and long time constant of adaptive threshold*/
double_t tau_1_;
double_t tau_2_;
/** Amplitudes of threshold adaption*/
double_t alpha_1_;
double_t alpha_2_;
/** Resting threshold in mV
(relative to resting potential).
The real resting threshold is (U0_+omega_).
Called omega in [3]. */
double_t omega_;
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&); //!< Set values from dicitonary
};
// ----------------------------------------------------------------
/**
* 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
double_t V_th_1_; // short time adaptive threshold (related to tau_1_), variable 1
double_t V_th_2_; // long time adaptive threshold (related to tau_2_), variable 2
int_t r_; // 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);
};
// ----------------------------------------------------------------
/**
* Buffers of the model.
*/
struct Buffers_ {
Buffers_(mat2_psc_exp&); //!<Sets buffer pointers to 0
Buffers_(const Buffers_&, mat2_psc_exp&); //!<Sets buffer pointers to 0
/** buffers and sums up incoming spikes/currents */
RingBuffer spikes_ex_;
RingBuffer spikes_in_;
RingBuffer currents_;
//! Logger for all analog data
UniversalDataLogger<mat2_psc_exp> 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 of membrane potential
double_t P20_; // constant currents
double_t P11ex_;
double_t P11in_;
double_t P21ex_;
double_t P21in_;
double_t P22_expm1_;
// time evolution operator of dynamic threshold
//P = ( exp(-h/tau_1) 0 )
// ( 0 exp(-h/tau_2) )
double_t P11th_;
double_t P22th_;
int_t RefractoryCountsTot_;
};
// ----------------------------------------------------------------
//! Read out state variables, used by UniversalDataLogger
double_t get_V_m_() const { return S_.V_m_ + P_.U0_; }
double_t get_V_th_() const { return P_.U0_ + P_.omega_ + S_.V_th_1_ + S_.V_th_2_; }
// ----------------------------------------------------------------
/**
* @defgroup mat2_psc_exp_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<mat2_psc_exp> recordablesMap_;
};
inline
port mat2_psc_exp::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 mat2_psc_exp::connect_sender(SpikeEvent&, port receptor_type)
{
if (receptor_type != 0)
throw UnknownReceptorType(receptor_type, get_name());
return 0;
}
inline
port mat2_psc_exp::connect_sender(CurrentEvent&, port receptor_type)
{
if (receptor_type != 0)
throw UnknownReceptorType(receptor_type, get_name());
return 0;
}
inline
port mat2_psc_exp::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 mat2_psc_exp::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 mat2_psc_exp::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 //MAT2_PSC_EXP_H