NeuronL5Pyramidal.java

/* 
 * Copyright (c) 2005–2012 Goethe Center for Scientific Computing - Simulation and Modelling (G-CSC Frankfurt)
 * Copyright (c) 2012-2015 Goethe Center for Scientific Computing - Computational Neuroscience (G-CSC Frankfurt)
 * 
 * This file is part of NeuGen.
 *
 * NeuGen is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License version 3
 * as published by the Free Software Foundation.
 * 
 * see: http://opensource.org/licenses/LGPL-3.0
 *      file://path/to/NeuGen/LICENSE
 *
 * NeuGen 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 Lesser General Public License for more details.
 *
 * This version of NeuGen includes copyright notice and attribution requirements.
 * According to the LGPL this information must be displayed even if you modify
 * the source code of NeuGen. The copyright statement/attribution may not be removed.
 *
 * Attribution Requirements:
 *
 * If you create derived work you must do the following regarding copyright
 * notice and author attribution.
 *
 * Add an additional notice, stating that you modified NeuGen. In addition
 * you must cite the publications listed below. A suitable notice might read
 * "NeuGen source code modified by YourName 2012".
 * 
 * Note, that these requirements are in full accordance with the LGPL v3
 * (see 7. Additional Terms, b).
 *
 * Publications:
 *
 * S. Wolf, S. Grein, G. Queisser. NeuGen 2.0 -
 * Employing NeuGen 2.0 to automatically generate realistic
 * morphologies of hippocapal neurons and neural networks in 3D.
 * Neuroinformatics, 2013, 11(2), pp. 137-148, doi: 10.1007/s12021-012-9170-1
 *
 *
 * J. P. Eberhard, A. Wanner, G. Wittum. NeuGen -
 * A tool for the generation of realistic morphology 
 * of cortical neurons and neural networks in 3D.
 * Neurocomputing, 70(1-3), pp. 327-343, doi: 10.1016/j.neucom.2006.01.028
 *
 */
package org.neugen.datastructures.neuron;

import java.io.Serializable;
import javax.vecmath.Point3f;
import javax.vecmath.Vector3f;
import org.neugen.datastructures.Dendrite;
import org.neugen.datastructures.parameter.KeyIdentificable;
import org.neugen.datastructures.parameter.ParameterConstants;
import org.neugen.gui.Trigger;
import org.neugen.utils.Vrand;

/**
 *
 * File: L5PyramidalNeuron.java
 *
 * @author Jens Eberhard
 * @author Alexander Wanner
 *
 * Created on 14.12.2009, 09:36:31
 *
 * Subclass for a L5 pyramidal neuron.
 */
public class NeuronL5Pyramidal extends NeuronPyramidal implements Serializable {

    public static class L5PyramidalParam extends PyramidalParam {

        private static L5PyramidalParam instance;

        /** Constructs contained parameters. */
        public L5PyramidalParam(String lastKey) {
            super(PyramidalParam.getInstance(), lastKey);
        }

        public L5PyramidalParam(KeyIdentificable parent, String lastKey) {
            super(parent, lastKey);
        }

        public static void setInstance(L5PyramidalParam instance) {
            L5PyramidalParam.instance = instance;
        }

        /** Returns instance. */
        public static L5PyramidalParam getInstance() {
            if (instance == null) {
                L5PyramidalParam param = new L5PyramidalParam(ParameterConstants.SUFFIX_PATH_L5PYRAMIDAL_PARAM);
                param.setApicalParam(ParameterConstants.LAST_KEY_APICAL);
                param.setBasalParam(ParameterConstants.LAST_KEY_BASAL);
                L5PyramidalParam.setInstance(param);
            }
            return instance;
        }
    }
    private static final long serialVersionUID = -8689338926398031243L;

    /** Constructor. */
    public NeuronL5Pyramidal() {
        super();
    }

    @Override
    public L5PyramidalParam getParam() {
        return L5PyramidalParam.getInstance();
    }

    /**
     * Function for setting a pyramidal neuron.
     * It sets the axon and creates the dendrites.
     */
    @Override
    public void setNeuron() {
        String mes = "set for " + getType() + " neuron";
        Trigger trigger = Trigger.getInstance();
        trigger.outPrintln();
        trigger.outPrintln(mes);

        Point3f somaMid = new Point3f(soma.getMid());
        Point3f axonEnd = new Point3f(somaMid);
        Point3f axonStart = new Point3f(somaMid);

        float somaRadius = soma.getMeanRadius();
        Vector3f deviation = new Vector3f(getParam().getDeviation().getX(), getParam().getDeviation().getY(), getParam().getDeviation().getZ());
        deviation.scale(somaRadius);
        int up_down = -1;
      
        axonEnd.x += getParam().getAxonParam().getFirstGen().getLenParam().getX() * drawNumber.fpm_onedraw();
        axonEnd.y += getParam().getAxonParam().getFirstGen().getLenParam().getY() * drawNumber.fpm_onedraw();
        axonEnd.z = somaMid.z + up_down * getParam().getAxonParam().getFirstGen().getLenParam().getZ() * (drawNumber.fdraw() + 0.5f);
        axonStart.z += up_down * somaRadius;

        soma.cylindricRepresentant();
        axon.set(axonStart, axonEnd, getParam().getAxonParam());
        somaMid.z += somaRadius;
        //logger.info("set dendirte");
        int npyramidaldendrite = getParam().getNumberOfApicalDendrites();
        Vrand dendriteRandomNumber = new Vrand(getParam().getDendriteParam().getSeedValue());
        Vrand apicalRandomNumber = new Vrand(getParam().getApicalParam().getSeedValue());
        for (int i = 0; i < getParam().getNumberOfDendrites(); ++i) {
            if (i < npyramidaldendrite) {
                Dendrite dendrite = new Dendrite();
                dendrite.setDrawNumber(apicalRandomNumber);
                dendrite.setPyramidalDendrite(getParam().getApicalParam(), soma, deviation, getParam().getApicalParam().getNumOblique());
                dendrites.add(dendrite);
            } else {
                Dendrite dendrite = new Dendrite();
                dendrite.setDrawNumber(dendriteRandomNumber);
                dendrite.setDendrite(getParam().getDendriteParam(), soma, deviation, false);
                dendrites.add(dendrite);
            }
        }
    }
}