皮质微电路模型

我实现了一个简单的皮质微电路模型的缩放版本代码如下

void crete_cm(double n_scale, double k_scale)
{
    float conn_threshold = 0.0f;

    int n_full[8] = { 20683, 5834, 21915, 5479, 4850, 1065, 14395, 2948 };

    std::vector pops;
    //创建种群
    int group1 = kernel().conn_manger.create(n_full[0] * n_scale);
    pops.push_back(group1);
    int group2 = kernel().conn_manger.create(n_full[1] * n_scale);
    pops.push_back(group2);
    int group3 = kernel().conn_manger.create(n_full[2] * n_scale);
    pops.push_back(group3);
    int group4 = kernel().conn_manger.create(n_full[3] * n_scale);
    pops.push_back(group4);
    int group5 = kernel().conn_manger.create(n_full[4] * n_scale);
    pops.push_back(group5);
    int group6 = kernel().conn_manger.create(n_full[5] * n_scale);
    pops.push_back(group6);
    int group7 = kernel().conn_manger.create(n_full[6] * n_scale);
    pops.push_back(group7);
    int group8 = kernel().conn_manger.create(n_full[7] * n_scale);
    pops.push_back(group8);


    // 不同种群的连接概率
    float conn_probs[8][8] = { {0.1009f, 0.1689f, 0.0437f, 0.0818f, 0.0323f, 0.f, 0.0076f, 0.f},
                              {0.1346f, 0.1371f, 0.0316f, 0.0515f, 0.0755f, 0.f, 0.0042f, 0.f},
                              {0.0077f, 0.0059f, 0.0497f, 0.135f, 0.0067f, 0.0003f, 0.0453f, 0.f},
                              {0.0691f, 0.0029f, 0.0794f, 0.1597f, 0.0033f, 0.f, 0.1057f, 0.f},
                              {0.1004f, 0.0622f, 0.0505f, 0.0057f, 0.0831f, 0.3726f, 0.0204f, 0.f},
                              {0.0548f, 0.0269f, 0.0257f, 0.0022f, 0.06f, 0.3158f, 0.0086f, 0.f},
                              {0.0156f, 0.0066f, 0.0211f, 0.0166f, 0.0572f, 0.0197f, 0.0396f, 0.2252f},
                              {0.0364f, 0.001f, 0.0034f, 0.0005f, 0.0277f, 0.008f, 0.0658f, 0.1443f} };

    // cap connection probabilities
    for (int ii = 0; ii < 8; ii++)
    {
        for (int jj = 0; jj < 8; jj++)
        {
            if (conn_probs[ii][jj] < conn_threshold)
                conn_probs[ii][jj] = 0;
        }
    }

    // 定义出度概率
    float k_full[8][8] = { 0 };
    for (int ii = 0; ii < pops.size(); ii++)
    {
        for (int jj = 0; jj < pops.size(); jj++)
        {
            k_full[ii][jj] = roundf(log(1.f - conn_probs[ii][jj]) / (log((double)((n_full[ii] * n_full[jj] - 1)) / (double)((n_full[ii] * n_full[jj]))))) / n_full[ii];
        }
    }
    // 建立连接
    for (int ii = 0; ii < pops.size(); ii++)
    {
        for (int jj = 0; jj < pops.size(); jj++)
        {
            kernel().conn_manger.connect(pops[ii], pops[jj], k_scale * k_full[jj][ii] * kernel().conn_manger.get_population_nums(pops[jj]) / (float)(kernel().conn_manger.get_population_nums(pops[jj]) * kernel().conn_manger.get_population_nums(pops[ii])));
        }
    }
}