A computational model of systems memory consolidation and reconsolidation (Helfer & Shultz 2019)

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A neural-network framework for modeling systems memory consolidation and reconsolidation.
1 . Helfer P, Shultz TR (2019) A computational model of systems memory consolidation and reconsolidation. Hippocampus [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network; Synapse;
Brain Region(s)/Organism: Hippocampus; Neocortex;
Cell Type(s): Abstract integrate-and-fire neuron;
Gap Junctions:
Receptor(s): AMPA;
Transmitter(s): Glutamate;
Simulation Environment: C or C++ program;
Model Concept(s): Memory; Synaptic Plasticity;
Implementer(s): Helfer, Peter [peter.helfer at mail.mcgill.ca];
Search NeuronDB for information about:  AMPA; Glutamate;
#ifndef NS_UNIT_HH
#define NS_UNIT_HH

#include <stdlib.h>
#include <string>

using std::string;

class NsLayer;
class NsConnection;

class NsUnit {
    NsUnit(const NsLayer *layer, uint index);
    bool activationFunction(double netInput);
    void computeNewActivation();
    void applyNewActivation();
    void setFrozen(bool state);
    void maintain();
    static void printStateHdr();
    void printState() const;
    string toStr(uint iLvl = 0, const string &iStr = "   ") const;

    const NsLayer *layer;
    const string id;
    double actFuncK;
    double actThreshold;
    bool isFrozen;
    bool isActive;
    bool newIsActive;
    double lastNetInput;
    vector<NsConnection *> inConnections;