{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# *(Joglekar, et. al, 2018)*: Inter-areal Balanced Amplification Figure 1\n", "\n", "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/large_scale_modeling/Joglekar_2018_InterAreal_Balanced_Amplification_figure1.ipynb)\n", "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/large_scale_modeling/Joglekar_2018_InterAreal_Balanced_Amplification_figure1.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Implementation of the figure 1 of:\n", "\n", "- Joglekar, Madhura R., et al. \"Inter-areal balanced amplification enhances signal propagation in a large-scale circuit model of the primate cortex.\" Neuron 98.1 (2018): 222-234." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.319303800Z", "start_time": "2023-07-22T05:51:44.303521100Z" } }, "outputs": [], "source": [ "import brainpy as bp\n", "import brainpy.math as bm\n", "from jax import vmap, jit\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.333840400Z", "start_time": "2023-07-22T05:51:44.319303800Z" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'2.4.3'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp.__version__" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.349511300Z", "start_time": "2023-07-22T05:51:44.333840400Z" } }, "outputs": [], "source": [ "wIE = 4 + 2.0 / 7.0 # synaptic weight E to I\n", "wII = wIE * 1.1 # synaptic weight I to I" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.396793900Z", "start_time": "2023-07-22T05:51:44.349511300Z" } }, "outputs": [], "source": [ "class LocalCircuit(bp.DynamicalSystem):\n", " r\"\"\"The model is given by:\n", "\n", " .. math::\n", "\n", " \\tau_E \\frac{dv_E}{dt} = -v_E + a1 * [I_E]_+ + a2 * [I_I]_+ \\\\\n", " \\tau_I \\frac{dv_I}{dt} = -v_I + a2 * [I_E]_+ + a4 * [I_I]_+\n", "\n", " where :math:`[I_E]_+=max(I_E, 0)`. :math:`v_E` and :math:`v_I` denote the firing rates\n", " of the excitatory and inhibitory populations respectively, :math:`\\tau_E` and\n", " :math:`\\tau_I` are the corresponding intrinsic time constants.\n", " \"\"\"\n", "\n", " def __init__(self, wEE, wEI, tau_e=0.02, tau_i=0.02):\n", " super(LocalCircuit, self).__init__()\n", " # parameters\n", " self.gc = bm.asarray([[wEE, -wEI],\n", " [wIE, -wII]])\n", " self.tau = bm.asarray([tau_e, tau_i]) # time constant [s]\n", " # variables\n", " self.state = bm.Variable(bm.asarray([1., 0.]))\n", "\n", " def update(self):\n", " self.state += (-self.state + self.gc @ self.state) / self.tau * bp.share['dt']\n", " self.state.value = bm.maximum(self.state, 0.)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.412559500Z", "start_time": "2023-07-22T05:51:44.366123600Z" } }, "outputs": [], "source": [ "def simulate(wEE, wEI, duration, dt=0.0001, numpy_mon_after_run=True):\n", " model = LocalCircuit(wEE=wEE, wEI=wEI)\n", " runner = bp.DSRunner(model, monitors=['state'], dt=dt,\n", " numpy_mon_after_run=numpy_mon_after_run,\n", " progress_bar=False)\n", " runner.run(duration)\n", " return runner.mon.state\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.413500700Z", "start_time": "2023-07-22T05:51:44.396793900Z" } }, "outputs": [], "source": [ "\n", "@jit\n", "@vmap\n", "def get_max_amplitude(wEE, wEI):\n", " states = simulate(wEE, wEI, duration=2., dt=0.0001, numpy_mon_after_run=False)\n", " return states[:, 0].max()\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.413500700Z", "start_time": "2023-07-22T05:51:44.412559500Z" } }, "outputs": [], "source": [ "@jit\n", "@vmap\n", "def get_eigen_value(wEE, wEI):\n", " A = bm.array([[wEE, -wEI], [wIE, -wII]])\n", " w, _ = bm.linalg.eig(A)\n", " return w.real.max()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:44.564206600Z", "start_time": "2023-07-22T05:51:44.413500700Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# =================== Figure 1B in the paper ==================================#\n", "\n", "length, step = 0.6, 0.0001\n", "wEEweak = 4.45 # synaptic weight E to E weak LBA\n", "wEIweak = 4.7 # synaptic weight I to E weak LBA\n", "wEEstrong = 6 # synaptic weight E to E strong LBA\n", "wEIstrong = 6.7 # synaptic weight I to E strong LBA\n", "weak = simulate(wEEweak, wEIweak, duration=length, dt=step)\n", "strong = simulate(wEEstrong, wEIstrong, duration=length, dt=step)\n", "\n", "fig, gs = bp.visualize.get_figure(1, 1, 5, 8)\n", "ax = fig.add_subplot(gs[0, 0])\n", "ax.plot(np.arange(step, length, step), weak[:, 0], 'g')\n", "ax.plot(np.arange(step, length, step), strong[:, 0], 'm')\n", "ax.set_ylabel('Excitatory rate (Hz)', fontsize='x-large')\n", "ax.set_xlabel('Time (s)', fontsize='x-large')\n", "ax.set_ylim([0, 6])\n", "ax.set_xlim([0, length])\n", "ax.set_yticks([0, 2, 4, 6])\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "ax.legend(['weak LBA', 'strong LBA'], prop={'size': 15}, frameon=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2023-07-22T05:51:45.987165900Z", "start_time": "2023-07-22T05:51:44.564206600Z" } }, "outputs": [ { "data": { "image/png": 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BpVOF11vi7vsMOBg9KwAAeJWbG9CODioA4oWeFQCAuxu1CMzNdeq0oBJwLZUl5f/K3+KTci2WCUC1CCsAkOzc3KiF9zghqITZm8KjioHYI6wAAOA1bg2gbgkqrE4PxA1hBQCSmVsbtQjOrXXqkqBCbwoQX4QVAEhWbm3UIji31qkTgkoIhBQgMQgrAAB4AUHFn5X7EWgiPUO+gIQirABAMnJrwxaBubU+nRBUqnniF70pQOIRVgAg2bi1YYvA3FqfTgoqlRBSAOcgrABAMnFrwxYIR7RBhSFfgOMQVgAAcCu3hs9Y9KpEei8qDvsipACORVgBgGTh1oYtAnNrfTohqFTAkC/A2QgrAJAM3NqwRWBurU8nBRV6UwBXIKwAAOAmBJWLrNwLQgrgKilWTqpRo0ZYPzVr1tQVV1yhgQMH6h//+IfdZQcAhMOtjVtU5da6TGRQaZR1cTL9YklTY1AWADFjKaw0bdpUzZo1k2ma5T/16tVT3bp1/bY1atRIhw8f1rvvvqt7771Xt956q86ePWv3ewAABOPWxi2qcmtdJjqoAHA1S2Hlq6++UocOHdS8eXPNnDlTR48e1aFDh3T48GEdPXpUM2fOVIsWLdShQwcVFxdr9erVuu666/TOO+/olVdesfs9AAACcWvjFt7htKDSJoLzATiCpbDy/PPP64MPPtCKFSs0bNgw1alTp3xfnTp1NGzYMOXn5+uDDz7QxIkT1aVLF7311lvKyMjQnDlzbCs8AABJwY0NbKcFFXpZAFeyFFZmzZqlvn376oorrgh6TJMmTdSvXz+98cYbkqTc3Fx17txZmzZtslZSAED43Ni4RWBurEunBZUK8vv6oi4KgPixFFYKCwuVkhL61JSUFBUWFpb/3qRJE506dcrKJQEA4XJj4xaBubEuE/3Ur6JDgX9WHeIpYIALWXp0cZMmTfT+++/r4MGDatiwYcBjDhw4oPfff19NmjQp33bw4EHVr1/fWkkBAEgmBJXzIulRqbgqfRkeVQy4mqWelWHDhqmkpES9evXS3LlzdebMmfJ9Z86c0dy5c5WXl6ejR49q2LBh5ds/+eQTtW/f3paCAwACcGMDF1W5sR4THVQCyN/iI6gALmepZ+Xxxx/X2rVrtXDhQt11111KSUlRdna2DMNQUVGRzp07J9M0dcstt+jxxx+XJG3evFmdO3fW8OHDbX0DAIAL3NjARVVurMdEBZVqQgoAbzBM0zStnjx79mxNmzZN69atK5+LkpaWphtuuEEjRozQvffea1tBY62kpER169ZV8R1SZnqiSwMAFrixkQt/bqzDRM9RKdMoi3kpgIscP35cAwcOVHFxsTIzM4MeF1VYKXPmzBkdOnR+nGhWVpZSUy112CQUYQWAq7mxkYuq3FaPTgkqojcFcJtww4otqSI1NVXZ2dl2vBQAIFJua+AiMLfVo0OCCiEF8Db3dYEAAC5yWwMXgbmtHu0OKlbeP0/5ApKC5bDy5Zdf6g9/+IOWLVum/fv3B10/xTAMv6eFAQCACggqkSGkAEnFUlhZvXq1brrpJn333XeSzs9TueSSS2wtGAAgBLc1clGV2+owwUGFIV9A8rEUVsaNG6fvvvtOo0eP1pNPPslCjwAQb25r5KIqt9VhIoMKvSlA0rIUVtatW6cOHTropZdesrs8AAB4H0ElPIQUIOlZCivp6elq3bq13WUBAITDbQ1duFuCggpDvgBIUoqVk2688UZ99tlndpcFABAKQcX93FSHiQgqBZKm2nxdAK5lKaxMnDhRe/bs0Z/+9Ce7ywMACMZNjVwE5qY6TOQcFTfdJwAxZWkY2IYNGzR8+HA99thjevvtt/WDH/xATZo0kWEYAY+/7777oiokAACu56YGeCKCSqOsC/84JG2Tev9/ecp/wGdzQQC4jaWwMmzYMBmGIdM0tWzZMi1fvjzgcaZpyjAMwgoARMtNDV1U5ab6S2hQAQB/lsLK008/HbQXBQBgMzc1dFFVMtdfFEGFXhUAksWwMn78eJuLAQCAB7ktqNjZqxLuey86dPHfZY8qzrWxHABczVJYAQDEidsau7jIbXWXiKBSAY8qBhAIYQUAnMptjV24VyKDCgs/AqhGWGHl/vvvl2EYmjhxorKzs3X//feHfQHDMPT3v//dcgEBICkRVNzNTfWXqKBCSAEQBsM0TTPUQSkpKTIMQ5s2bdKVV16plJTwl2cxDENnz56NqpDxUFJSorp166r4DikzPdGlAZD03NTYhT831V2CggpDvgAcP35cAwcOVHFxsTIzM4MeF1bPytKlSyVJzZo18/sdABADbmrswp+b6i4RQWWxlJ/rs/HCALwurLDSu3fvan8HANjETY1d+HNT3cU7qPCULwAWMcEeAIBoJWNQCfM9M+QLQDQIKwDgFG5q8OIiN9Wb3avTV4OQAsAOYYWVvn37Wr6AYRh6//33LZ8PAEnBTQ1eXOSmerO7R6Ug+CE85QuAXcIKKz6fz/IFDMOwfC4AJAU3NXjhTnEa+kVIAWC3sMLKjh07Yl0OAADcxS0hMw5BhZACIFbCCivNmzePdTkAIDm5pcELf26pN4IKAJcLf3VHAIC93NLghT+31Fs8hn4ttukaABBEVE8DO3XqlBYsWKAVK1aosLBQktS4cWP16NFDt912mzIyMmwpJAB4jlsavPDnlnqL8+OJASBWLIeV999/X8OGDVNhYaFM0/Tb98orrygnJ0evvvqqfvCDH0RdSAAAEs4tDXeCCgAPsRRW1qxZo4EDB+rUqVPq0qWLBg8erNzcXJmmqd27d+vNN9/Uhx9+qFtuuUX5+fnq0qWL3eUGAPeiEeg+bqkzggoAj7EUVp566imdPn1aU6dO1YgRI6rs/9WvfqXp06dr5MiRevrpp7V4MYNaAUASjUA3ckudJSCo5Pf12XRRAAjMMCuP4QpD3bp1dfXVV2vNmjXVHtelSxdt3rxZxcXFlgsYLyUlJapbt66K75Ay0xNdGgCe5ZaGLy5yQ53FOaiwOj2AaB0/flwDBw5UcXGxMjMzgx5n6WlgKSkpat26dcjjWrduzaKQAFDGDY1e+HNDncUzqBQQVADEl6VhYN///vf16aefhjzu008/1fe//30rlwAAb3FDoxf+3FBn8QoqBaynAiAxLPWsPPvss9q2bZuefvppnTt3rsp+0zT1zDPPaNu2bXr22WejLiQAuJobGr3w54Y6i1NQyd/iI6gASBhLPSubN2/W0KFD9fzzz2v27Nm6/fbby1e537Vrl+bPn69du3bpwQcf1JYtW7Rlyxa/8++7777oSw4AQCwkU1CRpILguwgpABLN0gT7lJQUGYbht75K2dyUQNvKmKYpwzB09uxZq+WNGSbYA4gJNzR8cZEb6suOoFL2PoMEFUIKgFgLd4K9pZ6Vp59+monzABCKGxq+uMgN9WVnUAmAkALAaSyFlfHjx9tcDADwGDc0fHGRG+orlkFlm5Sf67PhAgBgL0thBQAAz0iWoBJE/mmflBu71weAaFh6Glg87Nu3T/fcc4+ysrJUu3ZtdejQQevXr6/2nPz8fHXq1Ek1a9ZUy5YtNW3atDiVFgAqcEPjF+5h92T6Cz885QuAG1jqWenbt2/YxxqGoffffz+i1//222/Vo0cP9enTR++++64aNmyo7du3q169ekHP2bFjhwYMGKAHH3xQs2fP1sqVK/XQQw+pQYMGuv322yO6PgBYRlBxF6fXVyx6VBjyBcBFLD8NLOQLX3hamJWnf/32t7/VypUrtXz58rDPefzxx7Vw4UJt2rSpfNvIkSP1ySefaPXq1SHP52lgAGzh9MYvLnJ6XcUgqNCTAsApwn0amKVhYDt27Aj4s337dvl8Po0bN061atXSb37zGxUUVPMA9yAWLlyozp07a9CgQWrYsKE6duyoGTNmVHvO6tWr1b9/f79tP/zhD7Vu3TqdPn26yvGlpaUqKSnx+wGAqDi98YuLnF5Xdk+m30ZQAeBOloaBlS0AGUiLFi3Uq1cv9enTRzfffLO6du1a7fGBFBQUaOrUqXr00Uf1xBNP6KOPPtKoUaOUkZERdEHJoqIiZWdn+23Lzs7WmTNn9M033ygnJ8dv36RJk/S73/0uonIBQFBOb/ziIqfXlZ1BpaWkxZLek/SADa8LAHEWswn2N910kzp16qTf//73EZ977tw5XX/99Zo4caI6duyoESNG6MEHH9TUqVOrPS/QIpSBtkvSuHHjVFxcXP6zZ8+eiMsJAJKc3/jFRU6vqxivowIAbhPTp4E1bdpUX3zxRcTn5eTk6JprrvHb1rZtW+3evTvoOY0aNVJRUZHftoMHDyo1NVVZWVlVjs/IyFBmZqbfDwDAw5zeiI82qLSU898jAEQoZmHlu+++09q1a1WzZs2Iz+3Ro4e2bNnit23r1q3VDifr1q2b/vd//9dv25IlS9S5c2elpaVFXAYACAuNQ3dwej3ZEVQAwIMszVmprofj2LFj2rp1q/70pz9pz549Gjx4cMSvP2bMGHXv3l0TJ07UHXfcoY8++kjTp0/X9OnTy48ZN26c9u3bp1mzZkk6/+Svv/zlL3r00Uf14IMPavXq1fr73/+uN998M/I3CADhoIHoDk6vpxgu+Fgm/wFf7C8CADFgKazk5uYGnAdSkWmauuqqq/THP/4x4te/4YYbtGDBAo0bN04TJkxQixYtNHnyZA0ZMqT8mP379/uFphYtWmjRokUaM2aM/vrXv6px48aaMmUKa6wAiA2nN4DhDnYFlSAP3mR1egBuZ2mdlby8vKBhJT09XTk5Oerdu7cGDx5saRhYIrDOCoCIEFbcwcn1ZNfQrwBBhccUA3C6cNdZsdSz4vP5rJYLANzPyQ1gXOTkeorRHBVCCgCvienTwADAc5zcAMZFTq4nggoAhM1Sz0pFZ86c0SeffKLCwkIZhqGcnBxdd911Sk2N+qUBAIicl4OKVGXYFyEFgJdZThSlpaV65plnNG3aNB09etRv36WXXqqRI0dq/PjxrpmzAgAhObkRjPOcXEc2P/WLkAIgGVgKK6WlperXr59Wr14tSfre976n3NxcSdKuXbv0ySef6I9//KNWrFih999/XxkZGbYVGAASwsmNYJzn5DoiqACAJZbCyp///GetWrVKN954o1555RVde+21fvs///xz/fKXv9Ty5cs1efJkPf7447YUFgASwsmNYJzn5DqyMagQUgAkG0uPLr7uuutUVFSk7du365JLLgl4zLFjx9SqVStlZ2fr008/jbqgscajiwEE5eSGMJxdPzYFFUIKAK8J99HFlp4G9tVXXykvLy9oUJGkSy65RHl5edq+fbuVSwCAMzi5IQxn148dQWUbQQVAcrM0DCw1NVUnTpwIedyJEyd4KhgA93JyQxjOZkNQYfV5ALAYVtq3b68PPvhAO3bsUIsWLQIes2PHDn3wwQe6/vrroyogACQEQcX5nFpHUQYVelIA4CJLw8BGjBih7777Tnl5eXr99dd16tSp8n2lpaV67bXXlJeXp5MnT2rkyJG2FRYAAEneDCoM+QKAKiz1rNx7771asWKFZsyYofvvv18///nPlZ2dLcMwVFRUJNM0ZZqmRowYoSFDhthdZgCILac2hHGeU+sn2qFfBWLYFwBUYqlnRZL+9re/ae7cubrxxhuVmpqq/fv3q7CwUKmpqerZs6fmzp2rqVOn2llWAIg9pzaEcZ5T6yeaoOLU9wQADmDp0cWVnTlzRocOHZIkZWVluXJSPY8uBkCj0eGcWj92BJUCSYvP/zf/AV/URQIApwv30cW2pIrU1FRlZ2fb8VIAAFTl5aACAAjK0jCwL774QhMmTNDGjRuDHrNx40ZNmDBBmzZtslw4AIgbGo7O5dS6sWOOSgX5fX30qgBAJZbCyssvv6znn39ejRo1CnpMo0aN9Nxzz+k///M/LRcOAOLCqY1hOLdubFqZXgVS/hYfTwEDgCAshZX8/Hx17NhROTk5QY/JycnR9ddfr6VLl1ouHADEnFMbw3Bu3dgVVHhUMQCEZCms7N27N+hikBXl5uZq7969Vi4BAIDz2BRU8k/7lJ/rs+fFAMDDLE2wT09P19GjR0Med+zYMRmGYeUSABB7Tv3LPZxZNzYEFXpSACAylnpW2rVrp+XLl+vIkSNBj/n222+1fPlytW3b1mrZACB2nNgYxnlOrJtogwpDvgDAEkth5e6779bRo0c1aNAg7d+/v8r+/fv3684779SxY8dYwR4AED4PBhWGfAGAdZYWhTxz5oz69u2rFStWqHbt2howYIBatWolwzD01VdfadGiRTpx4oS6d++upUuXKi0tLRZltxWLQgJJxIkNYjizXqIIKvSkAEBwMV0UMjU1Vf/zP/+jUaNG6fXXX9e8efP89teoUUPDhw/Xyy+/7IqgAiCJOLFBDGfWi8WgQkgBAPtY6lmpqKioSEuXLtWePXskSU2bNlVeXl61jzV2InpWgCTgxAYxnFkvBBUAiKmY9qxU1KhRIw0ePDjalwEAJCOPBBVCCgDERtRhBQBcwYmN4mTnxDqJMKgQUgAgtiw9DQwAXMWJjeJk58Q6iSSo8ChiAIgLwgoAb3NioxjOE0lQWSweRQwAcUJYAQDEl9MCZIQ9KgCA+GHOCgDvclqjGM6qk2hXpQcAxBw9KwC8yUmNYpznpDohqACAKxBWAHiPkxrFOM9JdRJNUCHkAEBchRVWli1bpq1bt0b84u+9956mTJkS8XkAAA/xSlDR+SeA5ff12VIUAEBoYYWVvLw8vfDCCwH31a9fX7/61a8C7pszZ47GjBljvXQAECknNYzhrPqIJqjwqGIASIiwJ9ibphlw+5EjR3T8+HHbCgQAljmpYQxn1UcUQSX/tE/KtasgAIBI8DQwAN7gpIYxnFUfFoMKPSkAkHhMsAcA2MvtQYUhXwDgGPSsAHA/JzWOk52T6sJCUGHIFwA4C2EFgLs5qXGc7JxUFxEGFXpSAMCZGAYGwL2c1DhOdk6qi0iCCkO+AMDRwu5ZWbFihe6///6I9q1YscJ6yQAAiFQEQYUhXwDgfGGHla+++kpfffVVxPsMw7BWMgCojpP+kp/snFIXYQYVelIAwD3CCiuvvvpqrMsBAOFzSuMYzqmLcILKNik/1xfrkgAAbBRWWBk6dGisywEA4XFK4xjOqYswggpDvgDAnXgaGAAgci4JKgz5AgB342lgANzDKQ3kZOeUeqguqGyTNDVeBQEAxAphBYA7OKWBnOycUg9WVqYHALgOYQWA8zmlgZzsnFIPBBUASBqEFQBAaAQVAEACEFYAOJtTGsnJzCl1QFABgKTD08AAOJdTGsnJzCl1EGFQyc/1SQ/EpCQAgDgirABwJqc0kpOZU+oggqDCo4oBwFuiCitff/21Xn31VS1fvlyFhYUyDEM5OTnq1auXhg4dqoYNG9pVTgBAMgozqBBSAMCbLIeV+fPn6+c//7mOHj0q0zT99i1atEjPP/+8Zs6cqZ/+9KdRFxJAknHKX/STmRPqIJygsu3CkC8AgCdZCivr1q3T4MGDde7cOd1222269957lZubK0natWuX3njjDS1YsECDBw/WypUr1blzZzvLDMDLnNBITnZOqIMwgkr+aZ+UG+uCAAASyVJYmTRpks6ePau5c+dW6Tm57rrrdOutt+rf//63fvrTn+r3v/+95s2bZ0thAXicExrJyS7RdVAxpGwLfAg9KQCQPCw9unjFihXq3r17tUO8fvKTn6hHjx5avny55cIBAOLISUElCIIKACQXSz0rxcXFatasWcjjmjVrprVr11q5BIBkk+iGcrJL9P0PEVQIKQCQnCyFlUaNGunjjz8OedzHH3+sRo0aWbkEgGSS6IZyskv0/a8mqBBSACC5WRoG9sMf/lCbN2/WU089VeVJYJJkmqaefPJJbd68WT/60Y+iLiQAD0t0QznZOeH+bwv8Q1ABABhmoLQRwt69e9WxY0cdPnxYLVu21B133KHc3FwZhqEdO3boX//6l3bs2KGsrCxt2LBBTZo0iUXZbVVSUqK6deuq+A4pMz3RpQGSiBMay8nKofeekAIA3nf8+HENHDhQxcXFyszMDHqcpWFgTZo00QcffKAhQ4bo888/16RJk2QYhiSV97S0b99ec+bMcUVQAZAgDm0sJ4VE3/s2qvK0L0IKAKAyy4tCtm/fXp9++ql8Pl/5CvaS1LhxY/Xs2VN5eXl2lRGAFyW6sZzMEn3v21T474XAQlABAARiOayUycvLI5gAiEyiG8vJLNH3vvJk+gIpv68vESUBALiApQn2ffv21R/+8IeQx7344ovq27evlUsAAOzmtKACAEAIlnpWfD6fcnNzQx63ZcsW5efnW7kEAK9KdIM5WSX6vhNUAAAWWOpZCdfJkyeVmhr1SDMAXpHoBjMSI0RQ6f3/5cWlGAAA94lZWCkpKdGqVauUk5MTq0sAcBOCSuIk8t6H0aOS/4Av5sUAALhT2N0eLVv6/7/dvHnz5PP5Ah575swZHThwQGfOnNEvf/nLqAoIwAMIKomT6KCyLfju/FyfxLRGAEA1wg4rO3fuLP+3YRg6duyYjh07FvDYtLQ0NW7cWLfeeqsmTZoUdSEBABYkOqhUg0cVAwDCEXZYOXfuXPm/U1JSNGzYMM2cOTMmhQLgIfSqJEai7jshBQBgI0uz31999VW1bt3a7rIA8BqCSmI4MKgQUgAAVlgKK0OHDrW7HAC8hqCSGA687wQVAIBVPFcYALwi0UGl0mT6/FyflJuIggAAvIKwAsB+iW40JyMH3XN6UgAAdonpopAAkpCDGs1JI5FzVCrNUyGoAADsRM8KAPsQVOLPIfeckAIAiAVH9qyMHz9ehmH4/TRq1Cjo8T6fr8rxhmFo8+bNcSw1AMRZooPKNin/tI+gAgCIGcf2rLRr107vvfde+e81atQIec6WLVuUmZlZ/nuDBg1iUjYAASS64ZxsHHC/CSkAgFhzbFhJTU2ttjclkIYNG6pevXqxKRCA4BzQcE4qCb7fhBQAQLyEFVYmTJhg+QKGYeipp56K+Lxt27apcePGysjIUJcuXTRx4kS1bFn9/0N37NhRJ0+e1DXXXKMnn3xSffr0CXpsaWmpSktLy38vKSmJuIwAlPCGc9JJ4P0mpAAA4s0wTdMMdVBKSooMw1AYh1584QvHG4ahs2fPRlSod999VydOnNCVV16pAwcO6LnnntPmzZv1xRdfKCsrq8rxW7Zs0bJly9SpUyeVlpbqjTfe0LRp0+Tz+dSrV6+A1xg/frx+97vfVdlefIeUmR5RcYHkRliJn0Te6/ek/Ad8CSwAAMBLjh8/roEDB6q4uNhvGkdlYYWVQI36SDzzzDNRnX/8+HG1atVKjz32mB599NGwzrnllltkGIYWLlwYcH+gnpWmTZsSVoBIEFTiJ5GPJ94mwgoAwFbhhpWwhoFFGzaiVadOHbVv317btm0LffAFXbt21ezZs4Puz8jIUEZGhh3FA5ITQSV+EnGvK62fQn0DABLBkY8urqy0tFSbNm1STk5O2Ods3LgxouMBRICGq7dVDioAACSII58GNnbsWN1yyy1q1qyZDh48qOeee04lJSUaOnSoJGncuHHat2+fZs2aJUmaPHmycnNz1a5dO506dUqzZ8/W/PnzNX/+/ES+DcCbCCrx5ZD7nd/Xl+giAACSkCPDyt69ezV48GB98803atCggbp27aoPP/xQzZs3lyTt379fu3fvLj/+1KlTGjt2rPbt26datWqpXbt2eueddzRgwIBEvQUAiF6igkqlEbc8BQwAkChhTbBPBiUlJapbty4T7IHqOOSv/EnBAfeakAIAiBVbJ9gDgBMaz0mDRR8BAJDkkgn2ABKMoBI/8b7XbeQ3oZ6gAgBwEnpWAMApEhEKL8xPIaQAAJyIsAKgevSqxEeC7jMhBQDgZAwDAxAcQSU+CCoAAAQUVs9Ky5bW/5/UMAxt377d8vkAEoSgEh8JuM+EFACAW4QVVnbu3BnjYgBwFIJKfMT5PhNSAABuE1ZYOXfuXKzLAQDJhaACAEBITLAH4I9eldiL4z0mpAAA3IywAuAigkrsxekeE1IAAF5gS1g5cuSIjh49KtM0A+5v1qyZHZcBEEsEldgjqAAAEBHLYaWoqEhPPvmk/vu//1uHDx8OepxhGDpz5ozVywCAN8QjqBRc+HkgDtcCACAOLIWV/fv364YbblBhYaGuuOIKNWjQQAcPHlS3bt1UUFCgAwcOyDAMdevWTWlpaXaXGYDd6FWJrXjd35Y6H1YAAPAIS4tCPvfccyosLNSECRO0Z88e3XzzzTIMQytXrtT+/fvl8/l09dVXyzAMvfvuu3aXGYCdCCqxxf0FAMAyS2Hlf/7nf9SiRQs9+eSTAff36tVLS5Ys0caNG/Xss89GVUAAMURDOrbieX/bxPFaAADEiaWwsm/fPnXo0KH89xo1akiSSktLy7ddccUV6tOnj/7rv/4ruhICgBslKKjkP+CL44UBAIgtS2ElMzPT78lf9erVk3Q+xFRUs2bNKtsAOAS9KrET73u7Tco/7eMpYAAAz7EUVpo1a6adO3eW/37ttddKkhYtWlS+7cSJE1q5cqVycnKiKyEA+xFUYicB95aQAgDwKktPA+vbt68mT56sAwcOKDs7W7feeqvq1KmjsWPHas+ePWrSpIlmz56tAwcO6Be/+IXdZQYQDYJK7MT53hJSAABeZymsDBkyRHv27NGmTZuUnZ2t+vXr629/+5uGDx+uP/7xjzIMQ6Zpql27dnr++eftLjMAqwgqsRPHe0tIAQAkC8MMtuy8Bbt379aiRYv07bff6sorr9Stt97qmnVWSkpKVLduXRXfIWWmJ7o0QIwQVmKH1ekBAAjb8ePHNXDgQBUXFyszMzPocZZXsA+kWbNmGjlypJ0vCcAuBJXYicO9JaQAAJKRrWEFgEMRVGInxveWkAIASGaWngY2ZcoU1ahRw+/pX5W9++67qlGjhl555RXLhQNgA4JK7MTw3ubn8ihiAAAshZX58+ercePGGjBgQNBjfvSjHyknJ0fz5s2zXDgAUSKoxE6MgwoAALA4DGzLli3q2LFjtccYhqH27dvrk08+sVQwAHCsGAUVQgoAAP4shZUjR46ofv36IY+77LLLdPjwYSuXABAtelViIwb3lZACAEBgloaBNWrUSJ999lnI4z7//HNdfvnlVi4BIBoEldggqAAAEFeWwkqfPn30xRdfaP78+UGPeeutt/T555+rT58+lgsHwAKCSmzYfF+ZQA8AQGiWwspjjz2m9PR0DRkyRKNHj9aXX36pkydPqrS0VF9++aVGjx6tu+++W+np6XrsscfsLjMAxJfdAbDA5tcDAMCjLK9gP3fuXA0dOlSlpaUXX8wwZJqmTNNUzZo1NXPmTN111122FTaWWMEenkCviv1icU8LJC2W8l/wxeDFAQBwvnBXsLfUsyJJgwYN0qeffqoRI0aodevWysjIUHp6ulq3bq1f/OIX+uSTT1wTVABPIKjYj3sKAEBCRbWCfevWrVn0EXACGtX2i/U9bRPj1wcAwAOiCisAHICgYr8Y39P8vj6pb2yvAQCAF0QVVk6fPq0FCxZo+fLlKiwslGEYysnJUc+ePXXbbbcpLS3NrnICQHzEOqjwBDAAAMJmOaysXLlSd999t/bu3avKc/RfeeUVNW3aVP/4xz/UvXv3qAsJIAh6VewVw/tJSAEAIHKWwsrWrVt1880369ixY+rUqZPuuece5ebmSpJ27dql2bNna926dbr55pu1bt06tWnD4GzAdgQVe8XofhJSAACwzlJYef7553Xs2DH9+c9/1iOPPFJl/6hRozRlyhSNHj1azz//vF577bVoywmgIoKKvQgqAAA4kqV1Vpo0aaLs7GytX7++2uM6deqkAwcOaO/evZYLGC+sswJXIazYJwb3kpACAED1wl1nxVLPytdff63evXuHPO7qq6/WF198YeUSAIIhqNjH5ntJSAEAwF6WFoXMysrS1q1bQx63detW1a9f38olAARCULEPQQUAAMezFFb69OmjDRs2aMaMGUGPmTFjhtavX6++fVlMALAFQcU+Nt7L/FwfQQUAgBixNGdl06ZN6ty5s06ePKlevXrp7rvvVm5urgzD0I4dOzRnzhwtX75ctWrV0tq1a9W2bdtYlN1WzFmBoxFU7GPTvSSgAABgXUznrLRt21YLFy7UkCFDlJ+fr2XLlvntN01T2dnZmjNnjiuCCoAkQVABAMBVLC8K2a9fPxUUFOi//uu/ylewl6TGjRurZ8+euuOOO1S7dm3bCgokLXpV7GHDfSSkAAAQX5aGgYVr7ty52r9/v0aNGhWrS9iGYWBwJIKKPaK8j4QUAADsFe4wMEsT7MP10ksvacyYMbG8BOBdBBV7EFQAAHCtmIYVABYRVOwR7X18z5ZSAAAAiwgrALyJwAcAgOsRVgCnoZEdPbvuIXUBAEBCEVYAJ6FxHD3uIQAAnkFYAZyCRnb0bL6H+X199r4gAACIiOV1VgDYiKASPRvvIU8AAwDAGcIKKzVq1Ih1OQDAOlamBwDAk8IKK9GsG2kYhuVzgaRAr0p0CCoAAHhWWGHl3LlzsS4HkJwIKtGx4f4RUgAAcC7mrACJQlCJDivTAwDgeTwNDEgEgkp0CCoAACQFelYAuEsUQYWQAgCAuxBWgHijV8U6i/eOkAIAgDsxDAyIJ4KKdQQVAACSDj0rQLwQVKyzcO8IKQAAuB9hBYgHgop1Ed47QgoAAN7BMDAAzkVQAQAgqRFWgFijV8WaSO5bAUEFAAAvIqwAsURQsSbCoKKCWBUEAAAkEmEFiBWCijXcNwAAcAFhBYgFGtzWWLlv3GsAADyLsALAGQgdAACgEh5dDNiNRnfkorhn+bk+6QHbSgIAAByEnhXATgSVyEUbVAAAgGfRswLYhaASOYv3jJACAEByIKwAdiCoRM7CPSOkAACQXBgGBiD+CCoAACAM9KwA0aJXJTIR3i9CCgAAyYuwAkSDoBKZCO4XIQUAADAMDLCKoBIZggoAAIgQPSuAFQSVyIR5vwgpAACgIsIKECmCSmTK7ldB8EPy+/riURIAAOAyjhwGNn78eBmG4ffTqFGjas/Jz89Xp06dVLNmTbVs2VLTpk2LU2kBBBVGsCOoAACAYBzbs9KuXTu999575b/XqFEj6LE7duzQgAED9OCDD2r27NlauXKlHnroITVo0EC33357PIqLZEGvSvhC3CtCCgAACMWxYSU1NTVkb0qZadOmqVmzZpo8ebIkqW3btlq3bp1efPHFoGGltLRUpaWl5b+XlJREXWZ4HEElfNXcK0IKAAAIlyOHgUnStm3b1LhxY7Vo0UJ33XWXCgqCD3hfvXq1+vfv77fthz/8odatW6fTp08HPGfSpEmqW7du+U/Tpk1tLT88hqASvuruVTXzVgAAACpzZFjp0qWLZs2apcWLF2vGjBkqKipS9+7ddejQoYDHFxUVKTs7229bdna2zpw5o2+++SbgOePGjVNxcXH5z549e2x/H/AIgkr4Qt2rbXEpBQAA8AhHDgO7+eaby//dvn17devWTa1atdLrr7+uRx99NOA5hmH4/W6aZsDtZTIyMpSRkWFTiQEQ6gAAgN0c2bNSWZ06ddS+fXtt2xb4z7KNGjVSUVGR37aDBw8qNTVVWVlZ8SgivIoGeHi4TwAAIAZcEVZKS0u1adMm5eTkBNzfrVs3/e///q/ftiVLlqhz585KS0uLRxHhRTTAwxPJyvQv+GJWDAAA4D2ODCtjx45Vfn6+duzYoTVr1uhnP/uZSkpKNHToUEnn55vcd9995cePHDlSu3bt0qOPPqpNmzZp5syZ+vvf/66xY8cm6i3A7Qgq4YkkqPAUMAAAECFHzlnZu3evBg8erG+++UYNGjRQ165d9eGHH6p58+aSpP3792v37t3lx7do0UKLFi3SmDFj9Ne//lWNGzfWlClTWGMF1hBUwhPmfcrP9cW0GAAAwLsMs2wmepIrKSlR3bp1VXyHlJme6NIgYQgq4QlnZXpCCgAACOL48eMaOHCgiouLlZmZGfQ4Rw4DA+BgBBUAABAnjhwGBiQEvSqhhbhHhBQAAGAnwgogEVRCKbs/QVagZ/I8AACIBYaBAQSV6oXqTSGoAACAGKFnBcmNoFK9au4PIQUAAMQaYQVAYEGCCiEFAADEC2EFyYteleoFmJ9CUAEAAPFEWEFyIqhEhJACAAASgbCC5ENQCRshBQAAJBJPA0NyIagE11J+94egAgAAEo2wguRBUAmu0r0hqAAAACcgrADJrnKIC7LwIwAAQLwRVpAc6FUJjPsCAAAcjLAC76NBHliw+8L9AgAADkFYgbfR8A6M+wIAAFyARxfDu2iQBxdkXkp+X5+UG8+CAAAABEfPCryJoBJYNfeFJ4ABAACnoWcFSBZBggohBQAAOBVhBd5Dr0pVAe4JIQUAADgdw8DgLQSVqggqAADApehZgXcQVAKrMJmeCfQAAMBNCCvwBoJKtehJAQAAbsQwMMDjCCoAAMCt6FmB+9GrEhAhBQAAuB1hBe5GUKmCkAIAALyCYWBwL4JKFQQVAADgJfSswJ0IKn4IKQAAwIsIK3Afgko5QgoAAPAyhoHBXQgqFxWEPgQAAMDNCCsAAAAAHImwAvegV+U87gMAAEgShBW4Aw3081pW+i8AAICHEVbgfDTMz6t0H/If8CWkGAAAAPHC08DgbASViypMqOcpYAAAIBkQVuBcBJUqCCkAACCZEFYAFyCkAACAZMScFTgTvSrlCCoAACBZ0bMC5yGoSCKkAAAAEFbgLAQVQgoAAMAFDAODcxBUCCoAAAAV0LMCZ0jyoEJIAQAAqIqwAiQQIQUAACA4hoEh8RzWqzLekJ4Nsu/ZC/vtQFABAACoHj0rSCyHBRVJqmFKT6dIOic9VWH7szq/fcK56F6fkAIAABAewgoSx4FBRboQUM75B5aKQeWpas8OjpACAAAQGcIKEsOhQaVMxcDynCmdMggqAAAA8cacFcSfw4NKmackpV8IKumm9aCixTYWCgAAIIkQVoAgntXFoHKqmkn3IbWxsVAAAABJhLCC+HJJr0rFOSql5vn/Pp0SRWABAABAxJizgvhxYVApG/oVaNI9AAAAYouwgvhwSVCRpLNBJtOXBZazhiQz/NfLf8BnW9kAAACSCWEFseeioCJJ46sJIk9JYQcVngAGAAAQHeasILZcFlTsQlABAACIHj0riJ0kDCqEFAAAAPsQVgAbEFIAAADsxzAwxEYS9aoQVAAAAGKDnhXYL0mCCiEFAAAgtggrsFcSBBVCCgAAQHwwDAz2IagAAADARvSswB4eDyqEFAAAgPgjrCB6Hg4qhBQAAIDEYRgYEARBBQAAILHoWUF0PNirQkgBAABwBsIKrPNYUCGkAAAAOAvDwGCNx4KKChJdAAAAAFRGWEHkvBZUJG++JwAAAJcjrCAyNOoBAAAQJ4QVhI+gAgAAgDgirABicj0AAIATEVYQHg/3qhBUAAAAnIlHFyM0jwYVQgoAAICzEVZQPQ8GFUIKAACAOzAMDMERVAAAAJBA9KwgMI8FFUIKAACA+xBWUJWHggohBQAAwL0YBgZ/BBUAAAA4BD0r8BxCCgAAgDcQVnCRy3tVCCkAAADe4vhhYJMmTZJhGBo9enTQY3w+nwzDqPKzefPm+BXU7QgqAAAAcBhH96ysXbtW06dP1/e+972wjt+yZYsyMzPLf2/QoEGsiuYtLg4qhBQAAADvcmxYOXbsmIYMGaIZM2boueeeC+uchg0bql69erEtmNe4NKgQUgAAALzPsWHl4Ycf1sCBA3XTTTeFHVY6duyokydP6pprrtGTTz6pPn36BD22tLRUpaWl5b8XFxdLkkpOR1du1ykNfYjTrOj1jnT8eKKLAQAAAItOnDghSTJNs9rjHBlW/vnPf2rDhg1au3ZtWMfn5ORo+vTp6tSpk0pLS/XGG2+oX79+8vl86tWrV8BzJk2apN/97ndVtjddEFXREQ8vDEx0CQAAAGCDo0ePqm7dukH3G2aoOBNne/bsUefOnbVkyRJdd911kqS8vDx16NBBkydPDvt1brnlFhmGoYULFwbcX7ln5ciRI2revLl2795d7Q0DwlVSUqKmTZtqz549fnOpACv4PMFOfJ5gNz5TiJRpmjp69KgaN26slJTgz/xyXM/K+vXrdfDgQXXq1Kl829mzZ7Vs2TL95S9/UWlpqWrUqBHydbp27arZs2cH3Z+RkaGMjIwq2+vWrcv/yGCrzMxMPlOwDZ8n2InPE+zGZwqRCKeDwHFhpV+/fvrss8/8tg0fPlxXX321Hn/88bCCiiRt3LhROTk5sSgiAAAAgDhwXFi59NJLde211/ptq1OnjrKyssq3jxs3Tvv27dOsWbMkSZMnT1Zubq7atWunU6dOafbs2Zo/f77mz58f9/IDAAAAsIfjwko49u/fr927d5f/furUKY0dO1b79u1TrVq11K5dO73zzjsaMGBA2K+ZkZGhZ555JuDQMMAKPlOwE58n2InPE+zGZwqx4rgJ9gAAAAAgScGn3gMAAABAAhFWAAAAADgSYQUAAACAIxFWAAAAADhSUoaVSZMmyTAMjR49utrj8vPz1alTJ9WsWVMtW7bUtGnT4lNAuEo4nyefzyfDMKr8bN68OX4FhWONHz++ymejUaNG1Z7D9xOCifTzxPcTwrFv3z7dc889ysrKUu3atdWhQwetX7++2nP4noIdXPno4misXbtW06dP1/e+971qj9uxY4cGDBigBx98ULNnz9bKlSv10EMPqUGDBrr99tvjVFo4XbifpzJbtmzxW9m3QYMGsSoaXKZdu3Z67733yn+vbgFcvp8QSiSfpzJ8PyGYb7/9Vj169FCfPn307rvvqmHDhtq+fbvq1asX9By+p2CXpAorx44d05AhQzRjxgw999xz1R47bdo0NWvWTJMnT5YktW3bVuvWrdOLL77I/8ggKbLPU5mGDRtW++WO5JWamhqyN6UM308IJZLPUxm+nxDMCy+8oKZNm+rVV18t35abm1vtOXxPwS5JNQzs4Ycf1sCBA3XTTTeFPHb16tXq37+/37Yf/vCHWrdunU6fPh2rIsJFIvk8lenYsaNycnLUr18/LV26NIalg9ts27ZNjRs3VosWLXTXXXepoKAg6LF8PyGUSD5PZfh+QjALFy5U586dNWjQIDVs2FAdO3bUjBkzqj2H7ynYJWnCyj//+U9t2LBBkyZNCuv4oqIiZWdn+23Lzs7WmTNn9M0338SiiHCRSD9POTk5mj59uubPn6+33npLV111lfr166dly5bFuKRwgy5dumjWrFlavHixZsyYoaKiInXv3l2HDh0KeDzfT6hOpJ8nvp8QSkFBgaZOnao2bdpo8eLFGjlypEaNGqVZs2YFPYfvKdglKYaB7dmzR4888oiWLFmimjVrhn2eYRh+v5umGXA7kouVz9NVV12lq666qvz3bt26ac+ePXrxxRfVq1evWBUVLnHzzTeX/7t9+/bq1q2bWrVqpddff12PPvpowHP4fkIwkX6e+H5CKOfOnVPnzp01ceJESed74b744gtNnTpV9913X9Dz+J6CHZKiZ2X9+vU6ePCgOnXqpNTUVKWmpio/P19TpkxRamqqzp49W+WcRo0aqaioyG/bwYMHlZqaqqysrHgVHQ5k5fMUSNeuXbVt27YYlxZuVKdOHbVv3z7o54PvJ0Qi1OcpEL6fUFFOTo6uueYav21t27bV7t27g57D9xTskhQ9K/369dNnn33mt2348OG6+uqr9fjjjwd8Skq3bt309ttv+21bsmSJOnfurLS0tJiWF85m5fMUyMaNG5WTkxOLIsLlSktLtWnTJvXs2TPgfr6fEIlQn6dA+H5CRT169NCWLVv8tm3dulXNmzcPeg7fU7CNmaR69+5tPvLII+W///a3vzXvvffe8t8LCgrM2rVrm2PGjDG//PJL8+9//7uZlpZmzps3LwGlhdOF+jz9+c9/NhcsWGBu3brV/Pzzz83f/va3piRz/vz5CSgtnObXv/616fP5zIKCAvPDDz80f/zjH5uXXnqpuXPnTtM0+X5CZCL9PPH9hFA++ugjMzU11Xz++efNbdu2mXPmzDFr165tzp49u/wYvqcQK0nRsxKO/fv3+3VntmjRQosWLdKYMWP017/+VY0bN9aUKVN43B7CUvnzdOrUKY0dO1b79u1TrVq11K5dO73zzjsaMGBAAksJp9i7d68GDx6sb775Rg0aNFDXrl314Ycflv/Vku8nRCLSzxPfTwjlhhtu0IIFCzRu3DhNmDBBLVq00OTJkzVkyJDyY/ieQqwYpnlhthMAAAAAOEhSTLAHAAAA4D6EFQAAAACORFgBAAAA4EiEFQAAAACORFgBAAAA4EiEFQAAAACORFgBAAAA4EiEFQAAAACORFgBgGoYhiHDMBJdDMsMw1Bubm7Yx48fP778PVf3s3PnzpiV2SqfzxdW2V977bVEFzXuAn0Odu7cKcMwlJeXl5AyAUA4UhNdAACA81x33XXq0KFD0P2XXHJJWK+Tm5urXbt2yTRNm0oWWnZ2tn70ox8F3d+6deu4lQUAEB3CCgCgip/85CcaP358oothydVXX52UvSeRuuKKK7Rp0ybVrl070UUBgKAIKwAAJKG0tDRdffXViS4GAFSLOSsAYKMvv/xSQ4YMUU5OjtLT03XFFVfovvvu05YtW6o9Z/jw4WrevLkyMjKUnZ2tXr166eWXX/Y77uOPP9Zjjz2mTp06qUGDBsrIyFDLli310EMPqbCwMNZvLSJl80d27dolSX5zRirPnTh06JB+85vfqE2bNqpZs6bq16+vH/3oR1qyZEkCSu7PNE3NmTNH/fr1U1ZWlmrWrKmWLVvq7rvv1sqVK6scv2jRIv3gBz/QZZddppo1a+qqq67Sb3/7Wx05cqTKscOGDZNhGPL5fAGvHehevfbaazIMQ+PHj9fWrVt1++23KysrS3Xq1FGPHj20aNGisN9bsDkrFa+xe/du3X333WrQoIFq1aqlzp076+233w74eqZpaurUqbr22mtVs2ZNNWnSRKNHj9bRo0eVl5fn2LlOAJyNnhUAsMn777+vW265Rd99952uv/565eXlafPmzXrjjTe0YMECLVq0SD179vQ7Z+7cubr33ntVWlqqdu3aqXv37jp8+LA+//xzjR49Wo888kj5sb///e81b948XXvtterRo4cMw9DHH3+sqVOn6t///rfWrVunxo0bx/ttB9SoUSMNHTpU8+bN0/HjxzV06NDyfZdffnn5v/ft26devXqpoKBAzZo1009+8hN9/fXXeu+997R48WK99NJLGjNmTCLegs6ePau77rpL8+bNU0ZGhm688UZdfvnl2r17txYsWKD09HT16NGj/PhJkybpiSeeUGpqqnr37q3LL79cK1eu1AsvvKAFCxZo2bJlys7OtqVs27dv1/e//33Vr19f/fv3V2FhoZYvX64f//jHmjlzpoYNGxb1NXbu3KkbbrhBNWvW1I033qgDBw5o9erV+slPfqJ3331X/fv39zt+9OjRmjJlijIyMtSvXz/VqlVLb7zxhlatWqXUVJobACwyAQBBSTLD+ao8duyYmZ2dbUoyp06d6rfvpZdeMiWZTZo0MU+ePFm+fevWrWbNmjXNtLQ081//+pffOWfPnjXffvttv23vv/++WVhYWOW43/3ud6Ykc/jw4QHL37x585DlL/PMM8+Yksxnnnkm7HOq07x582rv349//GNTknnvvfeap06dKt++fPlys3bt2maNGjXMTz75JKxrLV261JRk9u7dO9pim6Zpms8++6wpyWzfvr25c+dOv32HDh0yV6xYUf77Rx99ZKakpJiXXnqpuWbNmvLtJ0+eNAcNGmRKMgcNGuT3GkOHDjUlmUuXLg14/UB19+qrr5Z/Ju+77z7z9OnT5fvefvtts0aNGmadOnWqfE4CvdaOHTsC3q+K1/jVr37ld43JkyebksyePXv6nbN8+XJTknn55ZebX375Zfn2w4cPm506dSp/vR07dgR8rwAQDGEFAKoRbliZOXNmwEZcmbIG25tvvlm+7Re/+IUpyfzlL38ZdTmvuOIKs379+lW2Ww0r1f1cd911Yb9edWFl+/btpiQzMzPT/Pbbb6vsf/TRR01J5ogRI8K6VllYCfUT6FqVlZaWmvXq1TMNwzDXrl0b8vj77rvPlGQ+9dRTVfYdOHDArFWrlpmSkmLu3bu3fHs0YeWSSy4xDx8+XOWcO++805RkTpw4MeRrhQorLVu29AuQpmmap0+fNi+77DIzLS3NLC0tLd8+ZMgQU5I5adKkKmX64IMPCCsALKNfFgBssHz5cknSkCFDAu6/5557tH79ei1fvlx33XWXJOm9996TJI0YMSLs6xw6dEgLFy7U559/riNHjujs2bOSpNOnT+vw4cM6fPiw6tevH81bkVT9o4ubNWsW9etL0ooVKyRJAwYMUL169arsv/fee/XSSy+V39twhXp0cXp6esjXWLdunY4cOaJOnTqpc+fOIY+vrv4bNmyo/v3767//+7+1atUqDRo0KOTrhdK/f39ddtllVbYPHjxY//rXv8rvbTTy8vKUlpbmty01NVUtW7bU+vXrdejQIeXk5EiSVq1aJUkB31ufPn2UlZWlQ4cORV0mAMmHsAIANiib4B5sAcay7RUnwu/Zs0eS1LJly7Cu8eabb+o//uM/dOzYsaDHHD161JawEo9HF1u5Z+Gw49HFZXXTqlWrsI4vLCyUYRhq3rx5wP1W30sw8bhOkyZNAm4vW2OntLS0fFvZ+w92TrNmzQgrACzhaWAAYKNQq91X3l/2hKxQdu3apWHDhqm0tFSTJ0/Wtm3bdOLECZnnh/OqW7dukhTXxRftEuz9l20P5/7Eit3XDvf1zp07Z+n17ax/O9+7Gz+XAJyBsAIANih7CteOHTsC7i97hG/ZsBlJatq0qUzT1Pbt20O+/qJFi3Tq1CmNGjVKjzzyiFq3bq1atWqV7y8oKIim+AkR6p6VPea24j2Ll6ZNm0qSvvrqq7COb9y4sUzTLK/nygLVf9lwtEA9ZWU9O8EEu87u3bvLyxNPOTk5Mk1Te/fuDbg/2HYACIWwAgA2KHsk8Zw5cwLuL9te8dHFN910kyRp+vTpIV//22+/lXSxEV3RsmXLdODAgcgKHCdlDfIzZ85U2XfjjTdKkt55552A65DMnj1bkqo87jkeOnfurHr16mnDhg1av359yOOrq/+vv/5aS5YsUUpKirp3716+vSy4bN26tco5odaYWbJkScB79uabb0qS3yOV46Hsfc2bN6/KPp/Pp2+++Sau5QHgHYQVALDBHXfcoezsbC1fvrxK+JgyZYrWrl2rJk2a6LbbbivfPnr0aNWsWVPTpk3T/Pnz/c45d+6c3wJ/V155paTzDfjjx4+Xb9+3b59GjhwZi7dki7K/8AdaFLNly5YaOHCgjh49qkceeUSnT58u37d69WpNnTpVNWrU0EMPPRS38pZJT0/XmDFjZJqmfv7zn1fp6Th8+LDfopAPP/ywUlJS9PLLL2vdunXl20+dOqVf/epXOnHihH7605/qiiuuKN/Xu3dvSdLUqVP95nNs2LBBTz31VLXlO3bsmB599FG/ELho0SLNnTtXtWvX9lvXJh7+4z/+Q5L0pz/9ya+ujxw5osceeyyuZQHgLUywB4AwdO3aNei+MWPG6M4779ScOXN0yy23aMSIEZo+fbquvPJKbd68WRs3blSdOnX0j3/8QxkZGeXnXXnllZo5c6aGDh2qn/3sZ7r22mt17bXX6ttvv9Vnn32mwsLC8rH+t956q9q1a6d169apdevW6tGjh06ePKmlS5eqQ4cO6t69e/kTmezw73//u9rVxkeNGqXrr78+5Ovceuutys/PV79+/dSnTx/VqVNHl19+uX7/+99Lkv72t7+pZ8+emjVrlvLz89WtWzd9/fXX8vl8Onv2rP70pz/pe9/7XkRl37x5c7WLIvbv31933313yNd54okntHHjRv373/9WmzZt1LNnz/JFITds2KA777yzvAfj+9//vp599ln93//7f9WtWzfl5eWVLwq5Z88etWnTRn/5y1/8Xr9Pnz7q3bu38vPzdc0116hHjx76+uuvtWbNGj3yyCN68cUXg5ZtyJAheuutt+Tz+dSlSxft379fy5Ytk2maevnll/1CUTz07t1bDz/8sP7617+qQ4cO5YtCLl26VLm5ueratas+/PDDsJ7EBgB+EvTIZABwBYWxbsef//zn8uM///xzc/DgwWZ2draZlpZm5uTkmPfcc4+5efPmoNf4+OOPzbvvvtvMyckx09LSzOzsbLN3797mlClT/I47fPiw+Ytf/MLMzc01MzIyzJYtW5qPP/64efz4cbN3794B17FQDNZZkWQuWLAgrNc7ffq0+eSTT5qtWrUy09LSApbnm2++MX/961+brVq1MtPT08169eqZ/fv3NxcvXhx2uU0z/HVWHnnkkbBf8+zZs+bMmTPNG2+80czMzDRr1qxptmjRwhwyZIi5atWqKsf/v//3/8x+/fqZdevWNdPT083WrVubjz32WMA1UUzTNI8cOWKOHDnSzM7ONjMyMsx27dqVLyoa6F6VrYHyzDPPmF9++aX5f/7P/zEvu+wys1atWma3bt2qLCRaJtBrhVpnJdjCoME+a+fOnTP/8z//02zbtq2Znp5uNm7c2Hz44YfN4uJis3Xr1qZhGOaJEycCviYABGOYJo/oAADADV577TUNHz5czzzzTMwfLW2Xffv2KTc3V61bt9amTZsSXRwALsOcFQAAELXNmzfru+++89v29ddfa/jw4Tpz5kxYQ+8AoDLmrAAAgKhNnjxZ//jHP9SxY0fl5OTowIED2rBhg0pKSnT99ddr7NixiS4iABcirAAAgKj99Kc/VWFhoTZs2KA1a9aoRo0aatWqlW6//Xb9+te/9lsXCADCxZwVAAAAAI7EnBUAAAAAjkRYAQAAAOBIhBUAAAAAjkRYAQAAAOBIhBUAAAAAjkRYAQAAAOBIhBUAAAAAjkRYAQAAAOBI/z+UpR1da90YnAAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# =================== Figure 1C in the paper ==================================#\n", "\n", "all_wEE = bm.arange(4, 6.5, .025)\n", "all_wEI = bm.arange(4.5, 7, .025)\n", "shape = (len(all_wEE), len(all_wEI))\n", "max_amplitude = bm.ones(shape) * -5\n", "all_wEE, all_wEI = bm.meshgrid(all_wEE, all_wEI)\n", "# select all parameters lead to stable model\n", "max_eigen_values = get_eigen_value(all_wEE.flatten(), all_wEI.flatten())\n", "selected_ids = bm.where(max_eigen_values.reshape(shape) < 1)\n", "# get maximum amplitude of each stable model\n", "num = 100\n", "for i in range(0, selected_ids[0].size, num):\n", " ids = (selected_ids[0][i: i + num], selected_ids[1][i: i + num])\n", " max_amps = get_max_amplitude(all_wEE[ids], all_wEI[ids])\n", " max_amplitude[ids] = max_amps\n", "\n", "fig, gs = bp.visualize.get_figure(1, 1, 5, 8)\n", "ax = fig.add_subplot(gs[0, 0])\n", "X, Y = bm.as_numpy(all_wEE), bm.as_numpy(all_wEI)\n", "levels = np.linspace(-5, 8, 20)\n", "plt.contourf(X, Y, max_amplitude.numpy(), levels=levels, cmap='hot')\n", "plt.contourf(X, Y, max_amplitude.numpy(), levels=np.linspace(-5, 0), colors='silver')\n", "plt.plot(wEEstrong, wEIstrong, 'mx')\n", "plt.plot(wEEweak, wEIweak, 'gx')\n", "plt.ylabel('Local I to E coupling', fontsize=15)\n", "plt.xlabel('Local E to E coupling', fontsize=15)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "brainpy", "language": "python", "name": "brainpy" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 1 }