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    "info": {
        "author": "Oren Amsalem, Guy Eyal, Noa Rogozinski, Michael Gevaert, Idan Segev",
        "author_email": "oren.amsalem1@mail.huji.ac.il",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3"
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        "description": "Introduction\n===========\n\nNeuron_Reduce provides an analytical method for reducing neuron model complexity. It enables the mapping of synapses and active ion channels to a computationally simpler model while accelerating simulation speed by up to 200-fold for inputs consisting of thousands of dendritic synapses. Full details are available in the accompanied paper - https://doi.org/10.1101/506485\n\nInstallation\n===========\n\n``` pip install --user neuron_reduce```\n\nQuick Start\n===========\nThe following code show the main function that is used to reduce a complex cell. \n```python\ncomplex_cell  # The model cell\nsynapses_list # A list of all synapse on this cell\nnetcon_list   # A list of all netcons for the synapses on the cell\n\nimport neuron_reduce\nreduced_cell, synapses_list, netcons_list =  neuron_reduce.subtree_reductor(complex_cell, synapses_list, netcons_list)\n```\n\nDetailed example\n===========\n\nCopy example folder from github\n```bash\ngit clone https://github.com/orena1/neuron_reduce.git\n```\n\nGo to example folder\n```bash\ncd neuron_reduce\ncd example\nnrnivmodl mod #compile the mod files\n```\n\nOpen python and run the following code\n\n\n```python\nfrom __future__ import division\nfrom neuron import gui,h\nimport numpy as np\nimport neuron_reduce\nimport time\nimport matplotlib.pyplot as plt\n\n\n\n#Create a L5_PC model\nh.load_file('L5PCbiophys3.hoc')\nh.load_file(\"import3d.hoc\")\nh.load_file('L5PCtemplate.hoc')\ncomplex_cell = h.L5PCtemplate('cell1.asc')\nh.celsius = 37\nh.v_init = complex_cell.soma[0].e_pas\n\n\n#Add synapses to the model\nsynapses_list, netstims_list, netcons_list, randoms_list = [], [], [] ,[]\n\nall_segments = [i for j in map(list,list(complex_cell.apical)) for i in j] + [i for j in map(list,list(complex_cell.basal)) for i in j]\nlen_per_segment = np.array([seg.sec.L/seg.sec.nseg for seg in all_segments])\nrnd = np.random.RandomState(10)\nfor i in range(10000):\n    seg_for_synapse = rnd.choice(all_segments,   p=len_per_segment/sum(len_per_segment))\n    synapses_list.append(h.Exp2Syn(seg_for_synapse))\n    if rnd.uniform()<0.85:\n        e_syn, tau1, tau2, spike_interval, syn_weight = 0, 0.3, 1.8,  1000/2.5, 0.0016\n    else:\n        e_syn, tau1, tau2, spike_interval, syn_weight = -86, 1,   8,   1000/15.0, 0.0008\n    #set synaptic varibales\n    synapses_list[i].e, synapses_list[i].tau1, synapses_list[i].tau2 = e_syn, tau1, tau2\n    #set netstim variables\n    netstims_list.append(h.NetStim())\n    netstims_list[i].interval, netstims_list[i].number, netstims_list[i].start, netstims_list[i].noise = spike_interval, 9e9, 100, 1\n    #set random\n    randoms_list.append(h.Random())\n    randoms_list[i].Random123(i)\n    randoms_list[i].negexp(1)\n    netstims_list[i].noiseFromRandom(randoms_list[i])       \n    #set netcon varibales \n    netcons_list.append(h.NetCon(netstims_list[i], synapses_list[i] ))\n    netcons_list[i].delay, netcons_list[i].weight[0] = 0, syn_weight\n\n#Simulate the full neuron for 1 seconds\nsoma_v = h.Vector()\nsoma_v.record(complex_cell.soma[0](0.5)._ref_v)\n\ntime_v = h.Vector()\ntime_v.record(h._ref_t)\n\nh.tstop = 1000\nst = time.time()\nh.run()\nprint('complex cell simulation time {:.4f}'.format(time.time()-st))\ncomplex_cell_v = list(soma_v)\n\n\n\n#apply Neuron_Reduce to simplify the cell\nreduced_cell, synapses_list, netcons_list = neuron_reduce.subtree_reductor(complex_cell, synapses_list, netcons_list, reduction_frequency=0, total_segments_manual=-1)\nfor r in randoms_list:r.seq(1) #reset random\n\n\n#Running the simulation again but now on the reduced cell\nst = time.time()\nh.run()\nprint('reduced cell simulation time {:.4f}'.format(time.time()-st))\nreduced_celll_v = list(soma_v)\n\n#plotting the results\nplt.figure()\n\nplt.plot(time_v, complex_cell_v, label='complex cell')\nplt.plot(time_v, reduced_celll_v,  label='redcued cell')\nplt.show()\n```\n\nCitation\n===========\nhttps://doi.org/10.1101/506485\n\n\n",
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