An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)

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We present an electrophysiological model of double bouquet cells (DBCs) and integrate them into an established cortical columnar microcircuit model that implements a BCPNN (Bayesian Confidence Propagation Neural Network) learning rule. The proposed architecture effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. The introduction of DBCs improves the biological plausibility of our model, without affecting the model's spiking activity, basic operation, and learning abilities.
1 . Chrysanthidis N, Fiebig F, Lansner A (2019) Introducing double bouquet cells into a modular cortical associative memory model Journal of Computational Neuroscience
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 interneuron basket PV GABA cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Abstract integrate-and-fire adaptive exponential (AdEx) neuron; Neocortex layer 2-3 interneuron; Neocortex bitufted interneuron;
Gap Junctions:
Simulation Environment: NEST;
Model Concept(s): Learning;
Implementer(s): Chrysanthidis, Nikolaos [nchr at]; Fiebig, Florian [fiebig at]; Lansner, Anders [ala at];
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 interneuron basket PV GABA cell;
aclocal.m4 *
aeif_cond_exp_multisynapse.cpp *
aeif_cond_exp_multisynapse.h *
bcpnn_connection.cpp *
bcpnn_connection.h *
bcpnn_connection_backup.cpp *
bcpnn_connection_backup.h * *
compile *
config.guess *
config.sub *
configure * *
depcomp *
iaf_cond_alpha_bias.cpp *
iaf_cond_alpha_bias.h *
iaf_cond_exp_bias.cpp *
iaf_cond_exp_bias.h *
install-sh * * *
missing *
pt_module.cpp *
pt_module.h * *
pt_module_names.cpp *
pt_module_names.h *
 *  iaf_cond_alpha_bias.h
 *  This file is part of NEST
 *  Copyright (C) 2005-2009 by
 *  The NEST Initiative
 *  See the file AUTHORS for details.
 *  Permission is granted to compile and modify
 *  this file for non-commercial use.
 *  See the file LICENSE for details.
 *  written by Philip Tully
 *  first version February 2012


#include "config.h"

#ifdef HAVE_GSL

#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"

#include <gsl/gsl_errno.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_odeiv.h>

/* BeginDocumentation
Name: iaf_cond_alpha_bias - Simple conductance based leaky integrate-and-fire neuron model.
                            Incorporates Bayesian Bias dynamics depending on incoming spike events.

iaf_cond_alpha is an implementation of a spiking neuron using IAF dynamics with
conductance-based synapses. Incoming spike events induce a post-synaptic change 
of conductance modelled by an alpha function. The alpha function 
is normalised such that an event of weight 1.0 results in a peak current of 1 nS
at t = tau_syn.

The following parameters can be set in the status dictionary.

V_m        double - Membrane potential in mV 
E_L        double - Leak reversal potential in mV.
C_m        double - Capacity of the membrane in pF
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.
E_ex       double - Excitatory reversal potential in mV.
E_in       double - Inhibitory reversal potential in mV.
g_L        double - Leak conductance in nS;
tau_syn_ex double - Rise time of the excitatory synaptic alpha function in ms.
tau_syn_in double - Rise time of the inhibitory synaptic alpha function in ms.
I_e        double - Constant input current in pA.
tau_e   >- double - Postsynaptic trace time constants
kappa      double - 'print now' signal

Sends: SpikeEvent

Receives: SpikeEvent, CurrentEvent, DataLoggingRequest


Author: Tully, Philip

SeeAlso: iaf_cond_alpha, iaf_cond_exp, iaf_cond_alpha_mc

namespace mynest
   * Function computing right-hand side of ODE for GSL solver.
   * @note Must be declared here so we can befriend it in class.
   * @note Must have C-linkage for passing to GSL. Internally, it is
   *       a first-class C++ function, but cannot be a member function
   *       because of the C-linkage.
   * @note No point in declaring it inline, since it is called
   *       through a function pointer.
   * @param void* Pointer to model neuron instance.
  extern "C"
  int iaf_cond_alpha_bias_dynamics (double, const double*, double*, void*);

   * Integrate-and-fire neuron model with two conductance-based synapses.
   * @note Per 2009-04-17, this class has been revised to our newest 
   *       insights into class design. Please use THIS CLASS as a reference
   *       when designing your own models with nonlinear dynamics.
   *       One weakness of this class is that it distinguishes between
   *       inputs to the two synapses by the sign of the synaptic weight.
   *       It would be better to use receptor_types, cf iaf_cond_alpha_mc.
  class iaf_cond_alpha_bias : public nest::Archiving_Node
    // Boilerplate function declarations --------------------------------

    iaf_cond_alpha_bias(const iaf_cond_alpha_bias&);

     * Import all overloaded virtual functions that we
     * override in this class.  For background information, 
     * see

    using nest::Node::connect_sender;
    using nest::Node::handle;

    nest::port check_connection(nest::Connection&, nest::port);

    nest::port connect_sender(nest::SpikeEvent &, nest::port);
    nest::port connect_sender(nest::CurrentEvent &, nest::port);
    nest::port connect_sender(nest::DataLoggingRequest &, nest::port);
    void handle(nest::SpikeEvent &);
    void handle(nest::CurrentEvent &);
    void handle(nest::DataLoggingRequest &); 
    void get_status(DictionaryDatum &) const;
    void set_status(const DictionaryDatum &);

    void init_node_(const Node& proto);
    void init_state_(const Node& proto);
    void init_buffers_();
    void calibrate();
    void update(nest::Time const &, const nest::long_t, const nest::long_t);

    // END Boilerplate function declarations ----------------------------

    // Friends --------------------------------------------------------

    // make dynamics function quasi-member mynest? or nest? also above
    friend int mynest::iaf_cond_alpha_bias_dynamics(double, const double*, double*, void*);

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


    // Parameters class ------------------------------------------------- 

    //! Model parameters
    struct Parameters_ {
      nest::double_t V_th;        //!< Threshold Potential in mV
      nest::double_t V_reset;     //!< Reset Potential in mV
      nest::double_t t_ref;       //!< Refractory period in ms
      nest::double_t g_L;         //!< Leak Conductance in nS
      nest::double_t C_m;         //!< Membrane Capacitance in pF
      nest::double_t E_ex;        //!< Excitatory reversal Potential in mV
      nest::double_t E_in;        //!< Inhibitory reversal Potential in mV
      nest::double_t E_L;         //!< Leak reversal Potential (aka resting potential) in mV
      nest::double_t tau_synE;    //!< Synaptic Time Constant Excitatory Synapse in ms
      nest::double_t tau_synI;    //!< Synaptic Time Constant for Inhibitory Synapse in ms
      nest::double_t I_e;         //!< Constant Current in pA
      nest::double_t tau_j;
      nest::double_t tau_e;
      nest::double_t tau_p;
      nest::double_t bias;
      nest::double_t fmax;
      nest::double_t gain;
      nest::double_t epsilon;
      nest::double_t kappa;
      Parameters_();        //!< Set default parameter values

      void get(DictionaryDatum&) const;  //!< Store current values in dictionary
      void set(const DictionaryDatum&);  //!< Set values from dicitonary
    // State variables class -------------------------------------------- 

     * State variables of the model.
     * State variables consist of the state vector for the subthreshold
     * dynamics and the refractory count. The state vector must be a
     * C-style array to be compatible with GSL ODE solvers.
     * @note Copy constructor and assignment operator are required because
     *       of the C-style array.
    struct State_ {
      //! Symbolic indices to the elements of the state vector y
      enum StateVecElems { V_M = 0,           
			   DG_EXC, //1
                           G_EXC,  //2    
			   DG_INH, //3
                           G_INH,  //4
                           Z_J,    //5
                           E_J,    //6
                           P_J,    //7
                           //BIAS,   //8

      //! state vector, must be C-array for GSL solver
      nest::double_t y[STATE_VEC_SIZE];
      //!< number of refractory steps remaining
      nest::int_t    r; 

      //!< Bias calculated from the P_J trace 
      nest::double_t bias;
      nest::double_t epsilon;
      nest::double_t kappa;

      State_(const Parameters_&);  //!< Default initialization
      State_(const State_&);
      State_& operator=(const State_&);

      void get(DictionaryDatum&) const;  //!< Store current values in dictionary

       * Set state from values in dictionary.
       * Requires Parameters_ as argument to, eg, check bounds.'
      void set(const DictionaryDatum&, const Parameters_&);

    // Buffers class -------------------------------------------------------- 

     * Buffers of the model.
     * Buffers are on par with state variables in terms of persistence,
     * i.e., initalized only upon first Simulate call after ResetKernel
     * or ResetNetwork, but are implementation details hidden from the user.
    struct Buffers_ {
      Buffers_(iaf_cond_alpha_bias&); //!<Sets buffer pointers to 0
      Buffers_(const Buffers_&, iaf_cond_alpha_bias&); //!<Sets buffer pointers to 0

      //! Logger for all analog data
      nest::UniversalDataLogger<iaf_cond_alpha_bias> logger_;

      /** buffers and sums up incoming spikes/currents */
      nest::RingBuffer spike_exc_;
      nest::RingBuffer spike_inh_;
      nest::RingBuffer currents_;

      /* GSL ODE stuff */
      gsl_odeiv_step*    s_;    //!< stepping function
      gsl_odeiv_control* c_;    //!< adaptive stepsize control function
      gsl_odeiv_evolve*  e_;    //!< evolution function
      gsl_odeiv_system   sys_;  //!< struct describing system
      // IntergrationStep_ should be reset with the neuron on ResetNetwork,
      // but remain unchanged during calibration. Since it is initialized with
      // step_, and the resolution cannot change after nodes have been created,
      // it is safe to place both here.
      nest::double_t step_;           //!< step size in ms
      double   IntegrationStep_;//!< current integration time step, updated by GSL

       * Input current injected by CurrentEvent.
       * This variable is used to transport the current applied into the
       * _dynamics function computing the derivative of the state vector.
       * It must be a part of Buffers_, since it is initialized once before
       * the first simulation, but not modified before later Simulate calls.
      double_t I_stim_;
    // Variables class ------------------------------------------------------- 
     * Internal variables of the model.
     * Variables are re-initialized upon each call to Simulate.
    struct Variables_ { 
       * Impulse to add to DG_EXC on spike arrival to evoke unit-amplitude
       * conductance excursion.
      nest::double_t PSConInit_E; 
       * Impulse to add to DG_INH on spike arrival to evoke unit-amplitude
       * conductance excursion.
      nest::double_t PSConInit_I;    
      //! refractory time in steps
      nest::int_t    RefractoryCounts;
    // Access functions for UniversalDataLogger -------------------------------
    //! Read out state vector elements, used by UniversalDataLogger
    template <State_::StateVecElems elem>
    nest::double_t get_y_elem_() const { return S_.y[elem]; }
    //! Read out remaining refractory time, used by UniversalDataLogger
    nest::double_t get_r_() const { return nest::Time::get_resolution().get_ms() * S_.r; }

    //! Read out Bias, used by UniversalDataLogger
    nest::double_t get_bias_() const { return S_.bias; }
    nest::double_t get_epsilon_() const { return S_.epsilon; }
    nest::double_t get_kappa_() const { return S_.kappa; }
    // Data members ----------------------------------------------------------- 

    // keep the order of these lines, seems to give best performance
    Parameters_ P_;
    State_      S_;
    Variables_  V_;
    Buffers_    B_;

    //! Mapping of recordables names to access functions
    static nest::RecordablesMap<iaf_cond_alpha_bias> recordablesMap_;

  // Boilerplate inline function definitions ----------------------------------

  nest::port mynest::iaf_cond_alpha_bias::check_connection(nest::Connection& c, nest::port receptor_type)
    nest::SpikeEvent e;
    return c.get_target()->connect_sender(e, receptor_type);

  nest::port mynest::iaf_cond_alpha_bias::connect_sender(nest::SpikeEvent&, nest::port receptor_type)
    if (receptor_type != 0)
      throw nest::UnknownReceptorType(receptor_type, get_name());
    return 0;
  nest::port mynest::iaf_cond_alpha_bias::connect_sender(nest::CurrentEvent&, nest::port receptor_type)
    if (receptor_type != 0)
      throw nest::UnknownReceptorType(receptor_type, get_name());
    return 0;
  nest::port mynest::iaf_cond_alpha_bias::connect_sender(nest::DataLoggingRequest& dlr, 
				      nest::port receptor_type)
    if (receptor_type != 0)
      throw nest::UnknownReceptorType(receptor_type, get_name());
    return B_.logger_.connect_logging_device(dlr, recordablesMap_);

  void iaf_cond_alpha_bias::get_status(DictionaryDatum &d) const

    (*d)[nest::names::recordables] = recordablesMap_.get_list();

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

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

} // namespace


#endif //HAVE_GSL