Fig 1.ipynb

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    "import numpy as np\n",
    "from brian2 import *\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "# from functions import *\n",
    "\n",
    "# the following dictionary defines style parameters to resemble the style of the figures in the paper, however, uncommenting these 2 lines will not affect the results\n",
    "my_params = {'figure.dpi' : 300, 'figure.figsize' : [3,2],'font.size':8,'font.family':'Arial','axes.spines.top': False, 'axes.spines.right': False} #adjust some of the default style parameters\n",
    "plt.rcParams.update(my_params) # update the default style parameters with the style parameters defined above\n",
    "#To revert to the default style parameters, uncomment the following line\n",
    "# plt.rcParams.update(matplotlib.rcParamsDefault)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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    "#-------------------------------------------------------------------------\n",
    "# Helper functions\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "#Defines a number of helper functions that is used to build the network or to analyse the data\n",
    "\n",
    "def shuffled_idx(N_pre,N_post, conn_prob):\n",
    "    '''\n",
    "    Generates an array of pre and post-synaptic indices to connect neurons in a homogenous way, meaning that each neuron has the same number of incoming and outgoing connections \n",
    "    :param N_pre: The number of presynaptic neurons\n",
    "    :param N_post: The number of postsynaptic neurons\n",
    "    :param conn_prob: The connection probability (between 0 and 1) between pre and post synaptic neurons\n",
    "    :return: an array with indices for both the pre and post population\n",
    "    '''\n",
    "    indices_pre=[]\n",
    "    indices_post=[]\n",
    "    for i in range (int(N_post*conn_prob+0.5)):\n",
    "        indices_pre.append(np.arange(N_pre))\n",
    "    for j in range (int(N_pre*conn_prob+0.5)):\n",
    "        indices_post.append(np.arange(N_post))\n",
    "    indices_pre = np.concatenate(indices_pre)\n",
    "    indices_post = np.concatenate(indices_post)\n",
    "    np.random.shuffle(indices_pre)\n",
    "    np.random.shuffle(indices_post)\n",
    "    if len(indices_pre) != len(indices_post):\n",
    "        return print('Pre and postsynaptic indices have different lengths')\n",
    "    elif all(indices_pre==indices_post):\n",
    "        return print('ATTENTION, pre and post indices are the same, use a different seed to shuffle the indices')\n",
    "    else:\n",
    "        return indices_pre, indices_post\n",
    "    \n",
    "def raster_interval_idx(start_time, stop_time, spike_times, select='idx'):\n",
    "    '''function: Truncated spiketrains for defined intervals\n",
    "    :param start_time: float for the start of the interval in seconds\n",
    "    :param stop_time: float for the end of the interval in seconds\n",
    "    :param spike_times: list with the unitless spiketimes in seconds\n",
    "    :select: either 'spikes' or 'idx', when spikes then it gives the spiketrain and when idx then it gives the start_idx and stop_idx\n",
    "    :return: truncated spiketrain when 'spikes' and start or stop idx when 'idx'\n",
    "    '''\n",
    "        \n",
    "    if len(spike_times)==0 or start_time > spike_times[-1] or stop_time < spike_times[0]:\n",
    "        start_id='no_spikes'\n",
    "        stop_id = 'no_spikes'        \n",
    "    else:\n",
    "        start_id = next(x[0] for x in enumerate(spike_times) if x[1] >= start_time)\n",
    "        if stop_time > spike_times[-1]:\n",
    "            stop_id=len(spike_times)\n",
    "        else:\n",
    "            stop_id = next(x[0] for x in enumerate(spike_times) if x[1] >= stop_time)\n",
    "        \n",
    "    if select == 'spikes':\n",
    "        if start_id=='no_spikes':\n",
    "            return []\n",
    "        else:\n",
    "            return spike_times[start_id:stop_id]\n",
    "    if select == 'idx':\n",
    "        return start_id, stop_id\n",
    "    \n",
    "def spike_times_unitless(spike_times):\n",
    "    '''function: Function that removes the unit of seconds from\n",
    "    :param spike_times: dictionary returned by brian2 with spiketimes in seconds\n",
    "    :return: Dictionary with spike times without units'''\n",
    "    \n",
    "    spike_times_unitless = {}\n",
    "    for k, v in spike_times.items():  # items method returns a list of tuple pairs\n",
    "        spike_times_unitless[k] = v / second  # The for loop removes the seconds from the spiketrains\n",
    "    return spike_times_unitless"
   ]
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    "#-------------------------------------------------------------------------\n",
    "# Define the neuron models\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "#Define neuron models to simulate different neuron populations with the Brian2 simulator. Unless otherwise stated, \n",
    "#the neuron models are identical to the ones defined in Naud et al. 2018 (Sparse bursts optimize information transmission in a multiplexed neural code,PNAS)\n",
    "\n",
    "def pyramidal_model(N):\n",
    "    '''\n",
    "    Define a two-compartmental neuron model of L5 PCs.\n",
    "    The neurons are modeled in the Brian2 simulater using two separate compartments (soma and dend) with coupled variables.\n",
    "    Implementing two separate compartments allows monitoring somatic and burst activity online and simultaneously.\n",
    "    :param N: Number of neurons in the network\n",
    "    :return: The somatic and dendritic compartment of L5 PCs\n",
    "    '''\n",
    "    \n",
    "    # Differential equations that define the dynamics of the somatic compartment of L5 PCs\n",
    "    soma_eqs = '''\n",
    "    du_s/dt = - (u_s - EL)/tau_s + (g_s * m  + I_s + w_s) / C_s: volt (unless refractory) # Somatic voltage equation\n",
    "    dw_s/dt = - w_s / tau_w_s : amp # Somatic adapatation variable\n",
    "    \n",
    "    I_s = I_s_ext * pamp - g_gaba + g_ampa + I_s_bg: amp # The total somatic input current\n",
    "    dI_s_bg/dt = (mu_s - I_s_bg) / tau_ou_s + sig_ou_s * pamp * xi_s/sqrt(tau_ou_s/2) : amp # Noisy background input equation defined by Ornstein-Uhlenbeck process \n",
    "    dg_ampa/dt = -g_ampa/tau_ampa : amp # The excitatory synaptic current\n",
    "    dg_gaba/dt = -g_gaba/tau_gaba : amp # The inhibitory synaptic current\n",
    "    \n",
    "    #variables defined in the dendritic compartment but also used in the dendritic compartment\n",
    "    \n",
    "    start_trace_burst:1 (linked) \n",
    "    stop_trace_burst: 1 (linked) \n",
    "    u_d : volt (linked)\n",
    "    m: 1 (linked)\n",
    "    '''\n",
    "\n",
    "    #equations that define the dynamics of the dendritic compartment\n",
    "    # Note that start_trace_burst and stop_trace_burst don't define the neural model but allow for online detection of burst activity. Both variables decay exponentially but increase with 1 when a somatic spike is detected.\n",
    "    #A burst is detected as soon as the start_trace_burst > 1.1. This occurs when a second somatic spike follows a first somatic spike within 16 ms. Start_trace_burst is reset to zero as soon as a burst is detected\n",
    "    #and stays zero (in refractory mode) as long as the stop_burst_trace > 0.1. The stop_burst_trace will fall below 0.1 when there was more then 16 ms between spikes and this defines the end of a burst.\n",
    "    #tau_burst is related to the burst criterium (in our case 16 ms) and is computed mathematically (see params dendrites).\n",
    "    \n",
    "    dend_eqs = '''\n",
    "    du_d/dt = - (u_d - EL)/tau_d + (g_d * m + K + I_d + w_d) / C_d : volt # Dendritic voltage equation\n",
    "    dw_d/dt = (- w_d + a_w_d * (u_d - EL)) / tau_w_d : amp #Dendritic adaptation variable\n",
    "    \n",
    "    I_d = I_d_ext * pamp  - g_gaba + g_ampa + I_d_bg : amp # The total dendritic input current\n",
    "    \n",
    "    dI_d_bg/dt = (mu_d - I_d_bg) / tau_ou_d + sig_ou_d * pamp * xi_d/sqrt(tau_ou_d/2)  : amp # Noisy background input equation defined by Ornstein-Uhlenbeck process \n",
    "    dg_ampa/dt = -g_ampa/tau_ampa : amp # The excitatory synaptic current\n",
    "    dg_gaba/dt = -g_gaba/tau_gaba : amp # The inhibitory synaptic current\n",
    "    \n",
    "\n",
    "    dstart_trace_burst/dt=-start_trace_burst/tau_burst : 1 #Post-synaptic trace that helps to define the start of a burst\n",
    "    dstop_trace_burst/dt=-stop_trace_burst/tau_burst : 1 #Post-synaptic trace that helps to define the end of a burst\n",
    "\n",
    "    m = 1/(1 + exp(-(u_d - Ed) / Dm)) : 1 #Sigmoidal function defining the non-linearity in the dendritic compartment\n",
    "    Ca_spike = 1/(1 + exp(-(u_d - Ed2) / Dm2)) : 1 #filtered voltage of the dendritic voltage to isolate the calcium spikes\n",
    "    \n",
    "    lastspike_soma : second (linked)\n",
    "    K = (((t-lastspike_soma) > K1) * ((t-lastspike_soma)< K2)) * c_d : amp #Variable representing the back-propagating action potential. The injected current is a stepfunction with a value of 1 (* c_d) between 0.5 and 2.5 ms after a somatic spike and 0 otherwise.\n",
    "    dburst_trace/dt =  -burst_trace/tau_stdp: 1 #Post-synaptic trace representing burst activity. This trace updates the inhibitory learning rule that controls burst activity \n",
    "    '''\n",
    "\n",
    "    # Parameters somatic compartment\n",
    "    params_soma = {\n",
    "        'tau_s': 16.0 * msecond,\n",
    "        'C_s': 370.0 * pfarad,\n",
    "        'g_s': 1300.0 * pamp,\n",
    "        'b_w_s': -200 * pamp,\n",
    "        'tau_w_s': 100.0 * msecond,\n",
    "        'EL': -0.07 * volt,\n",
    "\n",
    "        'Ed': -0.038 * volt,\n",
    "        'Dm': 0.006 * volt,\n",
    "        'mu_s': 0 * pamp,\n",
    "        'tau_ou_s': 2.0 * msecond,\n",
    "        'tau_gaba': 10 * msecond,\n",
    "        'tau_ampa': 5 * msecond,\n",
    "                    }\n",
    "    \n",
    "    # Define the somatic compartment\n",
    "    soma = NeuronGroup(N_PC, model=soma_eqs, threshold='u_s> -0.05 * volt',\n",
    "                       reset='''\n",
    "                             w_s += b_w_s\n",
    "                             u_s = EL\n",
    "                             start_trace_burst += 1\n",
    "                             stop_trace_burst = 1''',\n",
    "                       refractory= 3 * msecond, namespace=params_soma, method='euler', dt = 0.1 * msecond)\n",
    "\n",
    "    soma.u_s = 'EL + rand() * 0.01 * volt'     #initialise the somatic voltage around the resting potential\n",
    "\n",
    "    # Parameters dendritic compartment\n",
    "    params_dend = {\n",
    "        'tau_d': 7.0 * msecond,\n",
    "        'C_d': 170.0 * pfarad,\n",
    "        'g_d': 1200.0 * pamp,\n",
    "        'c_d': 2600.0 * pamp,\n",
    "        'tau_w_d': 30.0 * msecond,\n",
    "        'a_w_d': -13 * nsiemens,\n",
    "        'EL': -0.07 * volt,\n",
    "        'Ed': -0.038 * volt,\n",
    "        'Dm': 0.006 * volt,\n",
    "        'Ed2': -0.02 * volt,\n",
    "        'Dm2': 0.00001 * volt,\n",
    "        'K1': 0.499 * msecond,\n",
    "        'K2': 2.501 * msecond,\n",
    "        'tau_burst' : - 16 / np.log(0.1) * msecond,  #0.1 comes from the criterium under which burst_stop_trace has to fall\n",
    "\n",
    "        'mu_d': 0 * pamp,\n",
    "        'tau_ou_d': 2.0 * msecond,\n",
    "        'tau_gaba': 10 * msecond,\n",
    "        'tau_ampa': 5 * msecond,\n",
    "                    }\n",
    "\n",
    "    # Define the dendritic compartment\n",
    "    dend = NeuronGroup(N, model=dend_eqs, threshold='start_trace_burst > 1.1',\n",
    "                       reset='''\n",
    "                       burst_trace +=1\n",
    "                       start_trace_burst = 0\n",
    "                       ''',\n",
    "                       refractory='stop_trace_burst>0.1', namespace=params_dend, method='euler', dt = 0.1 * msecond)\n",
    "\n",
    "    # initialise dendritic voltage\n",
    "    dend.u_d = 'EL + rand() * 0.01 * volt' #initialise the dendritic voltage around the resting potential\n",
    "\n",
    "    #link dendritic and somatic variables\n",
    "    soma.u_d = linked_var(dend.u_d)\n",
    "    soma.m = linked_var(dend.m)\n",
    "    dend.lastspike_soma = linked_var(soma.lastspike)\n",
    "    soma.start_trace_burst = linked_var(dend.start_trace_burst)\n",
    "    soma.stop_trace_burst = linked_var(dend.stop_trace_burst)\n",
    "    return soma, dend\n",
    "\n",
    "def SOM_interneurons(N):\n",
    "    '''\n",
    "    Neuron model of somatostatin positive interneurons (SOM) as defined in Naud et al. (2018).\n",
    "    :param N: Number of SOM interneurons in the network\n",
    "    :return: SOM interneuron population\n",
    "    '''\n",
    "    \n",
    "    inh_eqs = '''\n",
    "    du_i/dt = - (u_i - EL)/tau_SOM + (I_inh + w_i) / C_SOM: volt (unless refractory) # Voltage equation of SOMs\n",
    "    dw_i/dt = - w_i / tau_w_SOM : amp # Adaptation variable of SOM interneurons\n",
    "\n",
    "    I_inh = I_SOM_ext * pamp + g_ampa  - g_gaba + I_bg_SOM: amp # The total input current to the SOMs\n",
    "    \n",
    "    dI_bg_SOM/dt = (mu_SOM - I_bg_SOM) / tau_ou_SOM + sig_ou_SOM * pamp * xi/sqrt(tau_ou_SOM/2.) : amp #Noisy background input\n",
    "    dg_ampa/dt = -g_ampa/tau_ampa : amp # The excitatory synaptic current\n",
    "    dg_gaba/dt = -g_gaba/tau_gaba : amp # The inhibitory synaptic current\n",
    "    '''\n",
    "\n",
    "    params_SOM = {\n",
    "        'EL': -0.07 * volt,\n",
    "        'tau_SOM': 20 * msecond,\n",
    "        'C_SOM': 100.0 * pfarad,\n",
    "        'b_w_SOM': -150.0 * pamp,\n",
    "        'tau_w_SOM': 100 * msecond,\n",
    "\n",
    "        'mu_SOM': 0 * pamp,\n",
    "        'tau_ou_SOM': 2.0 * msecond,\n",
    "        'tau_gaba': 10 * msecond,\n",
    "        'tau_ampa': 5 * msecond,\n",
    "                    }\n",
    "\n",
    "    # Define the inhibitory population\n",
    "    inh = NeuronGroup(N, model=inh_eqs, threshold='u_i> -0.05 * volt',\n",
    "                      reset='''\n",
    "                            u_i = EL\n",
    "                            w_i += b_w_SOM\n",
    "                            ''',\n",
    "                      refractory=3*msecond,\n",
    "                      namespace=params_SOM, method='euler', dt = 0.1 * msecond)\n",
    "\n",
    "    inh.u_i = 'EL + rand() * 0.01 * volt' #initialise the voltage around the resting potential\n",
    "\n",
    "    return inh\n",
    "\n",
    "#-------------------------------------------------------------------------\n",
    "# Define the synapse models\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "#Defining inhibitory plasticity models used in Fig 1, S1 and S2\n",
    "\n",
    "def inh_plast_pre_post_burst(pre, post, conn_p, weight, eta):\n",
    "    '''\n",
    "    Function implementing inhibitory plasticity rule where the plasticity is dependent on the pre and post-synaptic activity (Fig 1).\n",
    "    :param pre: name of the presynaptic population\n",
    "    :param post: name of the postsynaptic population\n",
    "    :param conn_p: connection probability between pre and post populations\n",
    "    :param weight: initial weights of the pre post conneccction\n",
    "    :param eta: learning rate\n",
    "    :return: synapse object\n",
    "    '''\n",
    "\n",
    "    params = {\n",
    "            'max_weight': 1000,\n",
    "            'eta': eta\n",
    "                       }\n",
    "\n",
    "\n",
    "    pre_to_post = Synapses(pre, post,\n",
    "                               '''\n",
    "                               w : 1\n",
    "                               dApre/dt = -Apre/tau_stdp : 1 (event-driven)\n",
    "                               dApost/dt = -Apost/tau_stdp : 1 (event-driven)\n",
    "                               ''',\n",
    "                               on_pre='''\n",
    "                                     Apre += 1.\n",
    "                                     w = clip(w+eta*(Apost-target), 0, max_weight)\n",
    "                                     g_gaba += w * pamp''',\n",
    "                               on_post='''\n",
    "                                     Apost += 1.\n",
    "                                     w = clip(w+eta*Apre, 0, max_weight)\n",
    "                                     ''', namespace=params, method='linear')\n",
    "\n",
    "    pre_idx,post_idx = shuffled_idx(pre.N,post.N,conn_p) # Generates the indices of pre and post populations so that the connections are homogenous\n",
    "    pre_to_post.connect(i=pre_idx, j=post_idx) #Connects the populations\n",
    "    \n",
    "    pre_to_post.w = weight # Initialisation of the connection weights\n",
    "\n",
    "    return pre_to_post\n",
    "\n",
    "def inh_plast_pre_post_ca(pre, post, conn_p, weight, eta):\n",
    "    '''\n",
    "    #Inhibitory plasticity model to control the burst rate using dendritic calcium spike (voltage rule) as signal for the post-synaptic activity (Fig S1)\n",
    "    :param pre: name of the presynaptic population\n",
    "    :param post: name of the postsynaptic population\n",
    "    :param  target: target in a.u.\n",
    "    :param conn_p: connection probability between pre and post populations\n",
    "    :param weight: initial weights of the pre post conneccction\n",
    "    :return: synapse object\n",
    "    '''\n",
    "\n",
    "    params = {\n",
    "            'max_weight': 1000,\n",
    "            'Ed2': -0.02 * volt,\n",
    "            'Dm2': 0.00001 * volt,\n",
    "            'eta': eta\n",
    "                       }\n",
    "\n",
    "    pre_to_post = Synapses(pre, post,\n",
    "                               '''\n",
    "                               w:1\n",
    "                               dApre/dt=-Apre/tau_stdp : 1 (event-driven)\n",
    "                               ''',\n",
    "                               on_pre='''\n",
    "                               Apre += 1.\n",
    "                               w = clip(w+eta*(Ca_spike-target), 0, max_weight)\n",
    "                               g_gaba += w * pamp''',\n",
    "                               on_post = '''\n",
    "                               w = clip(w+eta*Apre, 0, max_weight)\n",
    "                               ''',\n",
    "                               namespace=params, method='linear')\n",
    "\n",
    "    pre_idx,post_idx = shuffled_idx(pre.N,post.N,conn_p)\n",
    "    pre_to_post.connect(i=pre_idx, j=post_idx)\n",
    "\n",
    "    pre_to_post.w = weight\n",
    "\n",
    "    return pre_to_post\n",
    "\n",
    "def inh_plast_post_burst(pre, post, conn_p, weight, eta):\n",
    "\n",
    "    '''\n",
    "    Inhibitory plasticity model to control the burst rate using bursts as signal for the post-synaptic activity but without presynaptic dependence (Fig S2)\n",
    "    :param pre: name of the presynaptic population\n",
    "    :param post: name of the postsynaptic population\n",
    "    :param  target: target rate in Hz\n",
    "    :param conn_p: connection probability between pre and post populations\n",
    "    :param weight: initial weights of the pre post conneccction\n",
    "    :return: synapse object\n",
    "    '''\n",
    "\n",
    "    params = {\n",
    "            'max_weight': 1000,\n",
    "            'eta': eta,\n",
    "            'update_rate_dend': 50 *  msecond\n",
    "                       }\n",
    "    \n",
    "    pre_to_post = Synapses(pre, post,\n",
    "                               '''\n",
    "                               w : 1\n",
    "                               ''',\n",
    "                               on_pre='''\n",
    "                               g_gaba += w * pamp''',\n",
    "                               namespace=params, method='linear')\n",
    "    \n",
    "    pre_to_post.run_regularly('w=clip(w+eta*(burst_trace-target),0.,max_weight)', dt = params['update_rate_dend'])\n",
    "\n",
    "    pre_idx,post_idx = shuffled_idx(pre.N,post.N,conn_p)\n",
    "    pre_to_post.connect(i=pre_idx, j=post_idx)\n",
    "    \n",
    "    pre_to_post.w = weight\n",
    "\n",
    "    return pre_to_post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining inhibitory plasticity models used in Fig 1, S1 and S2\n",
    "\n",
    "#Inhibitory plasticity model to control the burst rate using bursts as signal for the post-synaptic activity (Fig 1)\n",
    "def inh_plast_pre_post_burst(pre, post, conn_p, weight, eta):\n",
    "    '''\n",
    "    Function implementing inhibitory plasticity rule where the plasticity is dependent on the pre and post-synaptic activity.\n",
    "    :param pre: name of the presynaptic population\n",
    "    :param post: name of the postsynaptic population\n",
    "    :param conn_p: connection probability between pre and post populations\n",
    "    :param weight: initial weights of the pre post conneccction\n",
    "    :param eta: learning rate\n",
    "    :return: synapse object\n",
    "    '''\n",
    "\n",
    "    params = {\n",
    "            'max_weight': 1000,\n",
    "            'eta': eta\n",
    "                       }\n",
    "\n",
    "\n",
    "    pre_to_post = Synapses(pre, post,\n",
    "                               '''\n",
    "                               w : 1\n",
    "                               dApre/dt = -Apre/tau_stdp : 1 (event-driven)\n",
    "                               dApost/dt = -Apost/tau_stdp : 1 (event-driven)\n",
    "                               ''',\n",
    "                               on_pre='''\n",
    "                                     Apre += 1.\n",
    "                                     w = clip(w+eta*(Apost-target), 0, max_weight)\n",
    "                                     g_gaba += w * pamp''',\n",
    "                               on_post='''\n",
    "                                     Apost += 1.\n",
    "                                     w = clip(w+eta*Apre, 0, max_weight)\n",
    "                                     ''', namespace=params, method='linear')\n",
    "\n",
    "    pre_idx,post_idx = shuffled_idx(pre.N,post.N,conn_p) # Generates the indices of pre and post populations so that the connections are homogenous\n",
    "    pre_to_post.connect(i=pre_idx, j=post_idx) #Connects the populations\n",
    "    \n",
    "    pre_to_post.w = weight # Initialisation of the connection weights\n",
    "\n",
    "    return pre_to_post\n",
    "\n",
    "#Inhibitory plasticity model to control the burst rate using dendritic calcium spike (voltage rule) as signal for the post-synaptic activity (Fig S1)\n",
    "def inh_plast_post_burst(pre, post, conn_p, weight, eta):\n",
    "\n",
    "    '''\n",
    "    :param pre: name of the presynaptic population\n",
    "    :param post: name of the postsynaptic population\n",
    "    :param  target: target rate in Hz\n",
    "    :param conn_p: connection probability between pre and post populations\n",
    "    :param weight: initial weights of the pre post conneccction\n",
    "    :return: synapse object\n",
    "    '''\n",
    "\n",
    "    params = {\n",
    "            'max_weight': 1000,\n",
    "            'eta': eta,\n",
    "            'update_rate_dend': 50 *  msecond\n",
    "                       }\n",
    "    \n",
    "    pre_to_post = Synapses(pre, post,\n",
    "                               '''\n",
    "                               w : 1\n",
    "                               ''',\n",
    "                               on_pre='''\n",
    "                               g_gaba += w * pamp''',\n",
    "                               namespace=params, method='linear')\n",
    "    \n",
    "    pre_to_post.run_regularly('w=clip(w+eta*(burst_trace-target),0.,max_weight)', dt = params['update_rate_dend'])\n",
    "\n",
    "    pre_idx,post_idx = shuffled_idx(pre.N,post.N,conn_p)\n",
    "    pre_to_post.connect(i=pre_idx, j=post_idx)\n",
    "    \n",
    "    pre_to_post.w = weight\n",
    "\n",
    "    return pre_to_post\n",
    "\n",
    "#Inhibitory plasticity model to control the burst rate using bursts as signal for the post-synaptic activity but without presynaptic dependence (Fig S2)\n",
    "def inh_plast_pre_post_ca(pre, post, conn_p, weight, eta):\n",
    "\n",
    "    '''\n",
    "    :param pre: name of the presynaptic population\n",
    "    :param post: name of the postsynaptic population\n",
    "    :param  target: target in a.u.\n",
    "    :param conn_p: connection probability between pre and post populations\n",
    "    :param weight: initial weights of the pre post conneccction\n",
    "    :return: synapse object\n",
    "    '''\n",
    "\n",
    "    params = {\n",
    "            'max_weight': 1000,\n",
    "            'Ed2': -0.02 * volt,\n",
    "            'Dm2': 0.00001 * volt,\n",
    "            'eta': eta\n",
    "                       }\n",
    "\n",
    "    pre_to_post = Synapses(pre, post,\n",
    "                               '''\n",
    "                               w:1\n",
    "                               dApre/dt=-Apre/tau_stdp : 1 (event-driven)\n",
    "                               ''',\n",
    "                               on_pre='''\n",
    "                               Apre += 1.\n",
    "                               w = clip(w+eta*(Ca_spike-target), 0, max_weight)\n",
    "                               g_gaba += w * pamp''',\n",
    "                               on_post = '''\n",
    "                               w = clip(w+eta*Apre, 0, max_weight)\n",
    "                               ''',\n",
    "                               namespace=params, method='linear')\n",
    "\n",
    "    pre_idx,post_idx = shuffled_idx(pre.N,post.N,conn_p)\n",
    "    pre_to_post.connect(i=pre_idx, j=post_idx)\n",
    "\n",
    "    pre_to_post.w = weight\n",
    "\n",
    "    return pre_to_post"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In a first step we illustrate how to model L5 Pyramidal cells and plot the dendritic voltage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulation at t=0. s for a duration of 5. s\n",
      "5.0 (100%) simulated in 3s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-------------------------------------------------------------------------\n",
    "# Run the simulation\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "start_scope()#brian2 function, all objects created before this call will no longer be automatically included by the magic functions such as run()\n",
    "tau_stdp = 20 *msecond\n",
    "N_PC = 2 #Number of L5 PC cells\n",
    "I_s_ext = 400 #External somatic input current in pA \n",
    "I_d_ext = 500 #External dendritic input curren in pA\n",
    "sig_ou_s =  100 #Noise on the external somatic input current in pA\n",
    "sig_ou_d = 200 #Noise on the external dendritic input current in pA\n",
    "simulation_time = 5 #in seconds\n",
    "\n",
    "soma,dend= pyramidal_model(N_PC) #Function that defines the L5 pyramidal model \n",
    "var_dend=StateMonitor(dend,('u_d'), record = [0]) #Records the statevariable 'dendritic voltage u_d' during a run\n",
    "run(simulation_time*second,report='stdout',profile=True) #run the simulation\n",
    "\n",
    "#-------------------------------------------------------------------------\n",
    "# Plot the figure\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "fig = plt.figure(figsize = (4,2))\n",
    "plt.plot(var_dend.u_d[0]*1000, color ='k') # plot the dendritic voltage for the duration of the simulation\n",
    "plt.ylabel('Volt (mV)')\n",
    "plt.xlabel('time (0.1 ms)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In a second step a population of L5 cells was simulated for different dendritic input and noise levels. This illustrates how external noise influences the input-output function of the burst response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Simulation of L5 PCs with 0.0 pA of external noise\n",
      "I dend ext = -100000000000000.0\n",
      "I dend ext = 100000000000000.0\n",
      "I dend ext = 300000000000000.0\n",
      "- Simulation of L5 PCs with 200000000000000.0 pA of external noise\n",
      "I dend ext = -100000000000000.0\n",
      "I dend ext = 100000000000000.0\n",
      "I dend ext = 300000000000000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-------------------------------------------------------------------------\n",
    "# Run the simulation\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "start_scope() # #brian2 function, all objects created before this call will no longer be automatically included by the magic functions such as run()\n",
    "N_PC = 1600 # Number of L5 PC cells\n",
    "I_s_ext = 500  # External somatic input current in pA\n",
    "I_d_ext_val = np.arange(-100,400,200)  # Range of external dendritic inputs current in pA, in the paper we used smaller intervals for the dendritic values but to reduce the compute we took steps of 200 pA\n",
    "I_d_ext = I_d_ext_val[0] #First dendritic input value, Brian2 needs to have an explicit variable I_d_ext when building the L5 PC model\n",
    "sig_ou_s =  100 # Noise level of the external somatic input current in pA\n",
    "sig_ou_d_val = np.array([0,200])# Noise levels of different external dendritic input currents in pA\n",
    "sig_ou_d =  sig_ou_d_val[0] #First dendritic input value, Brian2 needs to have an explicit variable sig_ou_d when building the L5 PC model\n",
    "simulation_time = 2 #in seconds, should be bigger then 1 second so that the system can settle in the 1st second and then we can compute the burst rate from the 2nd second\n",
    "\n",
    "soma,dend=pyramidal_model(N_PC) #Function that defines the L5 pyramidal model \n",
    "dend_rate = PopulationRateMonitor(dend) # brian2 monitor recording time-varying firing rate of a population of neurons\n",
    "\n",
    "burst_rate = np.zeros((len(sig_ou_d_val),len(I_d_ext_val)))\n",
    "store() #Store the state of the network and all included objects.\n",
    "for i in range (len(sig_ou_d_val)): #The simulation is run multiple times for different noise levels\n",
    "    print('- Simulation of L5 PCs with {} pA of external noise'.format(sig_ou_d_val[i]/pamp))\n",
    "    for j in range (len(I_d_ext_val)):\n",
    "        restore() #To restore the state of the network and all included objects. This is important when running multiple simulations\n",
    "        print('I dend ext = {}'.format(I_d_ext_val[j]/pamp))\n",
    "        I_d_ext = I_d_ext_val[j] #define the dendritic input level\n",
    "        sig_ou_d = sig_ou_d_val[i] #define the dendritic noise level\n",
    "        run(simulation_time*second,profile=True) #run the simulation\n",
    "        burst_rate[i,j] = np.mean(dend_rate.rate[-1000:]/Hz) #take the mean burst rate across all L5 PCs of the last second\n",
    "        \n",
    "#-------------------------------------------------------------------------\n",
    "# Plot the figure\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "plt.figure(figsize = (2,2))\n",
    "plt.plot(I_d_ext_val,burst_rate[0,:], label = ('noise = {} pA'.format(sig_ou_d_val[0])))\n",
    "plt.plot(I_d_ext_val,burst_rate[1,:], label = ('noise = {} pA'.format(sig_ou_d_val[1])))\n",
    "plt.xlabel('input (pamp)')\n",
    "plt.ylabel('burst rate (Hz)')\n",
    "plt.legend(frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the following section we explore how inhibitory plasticity controls the burst rate.\n",
    "In the paper we explored the behaviour of 3 different inhibitory plasticity rules to control the burst rate of L5 PCs (in respectively Fig 1, FigS1 and FigS2)\n",
    "- For Fig 1 the inhibitory plasticity rule is spike-time dependent and conditioned on pre- and postsynaptic activity levels, where post-synaptic activity is defined by burst activity\n",
    "- For Fig S1 we used a voltage dependent variant of the plasticity rule where post-synaptic activity is defined by dendritic calcium spikes. This rule is also spike-time dependent\n",
    "- Fig Fig S2 the plasticity is only dependent on post-synaptic activity which is defined by burst activity\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> 250000000000000.0 pA of external dend input\n",
      "Starting simulation at t=0. s for a duration of 50. s\n",
      "2.4034 (4%) simulated in 10s, estimated 3m 18s remaining.\n",
      "4.843100000000001 (9%) simulated in 20s, estimated 3m 6s remaining.\n",
      "7.2854 (14%) simulated in 30s, estimated 2m 56s remaining.\n",
      "9.7462 (19%) simulated in 40s, estimated 2m 45s remaining.\n",
      "12.2093 (24%) simulated in 50s, estimated 2m 35s remaining.\n",
      "14.663400000000001 (29%) simulated in 1m 0s, estimated 2m 25s remaining.\n",
      "17.1223 (34%) simulated in 1m 10s, estimated 2m 14s remaining.\n",
      "19.561500000000002 (39%) simulated in 1m 20s, estimated 2m 4s remaining.\n",
      "21.9995 (43%) simulated in 1m 30s, estimated 1m 55s remaining.\n",
      "24.4415 (48%) simulated in 1m 40s, estimated 1m 45s remaining.\n",
      "26.886200000000006 (53%) simulated in 1m 50s, estimated 1m 35s remaining.\n",
      "29.3267 (58%) simulated in 2m 0s, estimated 1m 25s remaining.\n",
      "31.7664 (63%) simulated in 2m 10s, estimated 1m 15s remaining.\n",
      "34.202400000000004 (68%) simulated in 2m 20s, estimated 1m 5s remaining.\n",
      "36.627700000000004 (73%) simulated in 2m 30s, estimated 55s remaining.\n",
      "39.0536 (78%) simulated in 2m 40s, estimated 45s remaining.\n",
      "41.486200000000004 (82%) simulated in 2m 50s, estimated 35s remaining.\n",
      "43.905100000000004 (87%) simulated in 3m 0s, estimated 25s remaining.\n",
      "46.3277 (92%) simulated in 3m 10s, estimated 15s remaining.\n",
      "48.748400000000004 (97%) simulated in 3m 20s, estimated 5s remaining.\n",
      "50.0 (100%) simulated in 3m 25s\n",
      "-> 650000000000000.0 pA of external dend input\n",
      "Starting simulation at t=0. s for a duration of 50. s\n",
      "2.3397 (4%) simulated in 10s, estimated 3m 24s remaining.\n",
      "4.7041 (9%) simulated in 20s, estimated 3m 13s remaining.\n",
      "7.0826 (14%) simulated in 30s, estimated 3m 2s remaining.\n",
      "9.470600000000001 (18%) simulated in 40s, estimated 2m 51s remaining.\n",
      "11.8701 (23%) simulated in 50s, estimated 2m 41s remaining.\n",
      "14.221400000000001 (28%) simulated in 1m 0s, estimated 2m 31s remaining.\n",
      "16.6244 (33%) simulated in 1m 10s, estimated 2m 21s remaining.\n",
      "19.005100000000002 (38%) simulated in 1m 20s, estimated 2m 10s remaining.\n",
      "21.3873 (42%) simulated in 1m 30s, estimated 2m 0s remaining.\n",
      "23.6655 (47%) simulated in 1m 40s, estimated 1m 51s remaining.\n",
      "25.836200000000005 (51%) simulated in 1m 50s, estimated 1m 43s remaining.\n",
      "28.139999999999997 (56%) simulated in 2m 0s, estimated 1m 33s remaining.\n",
      "30.4265 (60%) simulated in 2m 10s, estimated 1m 24s remaining.\n",
      "32.7839 (65%) simulated in 2m 20s, estimated 1m 14s remaining.\n",
      "35.1517 (70%) simulated in 2m 30s, estimated 1m 3s remaining.\n",
      "37.515100000000004 (75%) simulated in 2m 40s, estimated 53s remaining.\n",
      "39.8888 (79%) simulated in 2m 50s, estimated 43s remaining.\n",
      "42.2646 (84%) simulated in 3m 0s, estimated 33s remaining.\n",
      "44.6237 (89%) simulated in 3m 10s, estimated 23s remaining.\n",
      "46.9685 (93%) simulated in 3m 20s, estimated 13s remaining.\n",
      "49.3667 (98%) simulated in 3m 30s, estimated 3s remaining.\n",
      "50.0 (100%) simulated in 3m 32s\n"
     ]
    }
   ],
   "source": [
    "#-------------------------------------------------------------------------\n",
    "# Run the simulation\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "start_scope() #brian2 function, all objects created before this call will no longer be automatically included by the magic functions such as run()\n",
    "simulation_time = 50\n",
    "\n",
    "# With the variable plast_rule we define the inhibitory plasticity rule to control the burst rate of PCs.\n",
    "plast_rule = 'pre_post_burst' # Fig 1\n",
    "# plast_rule ='pre_post_ca' # Fig S1\n",
    "# plast_rule = 'post_burst' # Fig S2\n",
    "\n",
    "#Create the PC population\n",
    "N_PC = 1600 #Number of L5 PC cells\n",
    "I_s_ext = 500 #External somatic input current in pA \n",
    "I_d_ext_val = [250, 650] #Low and high external dendritic input current in pA\n",
    "I_d_ext = I_d_ext_val[0] # Low dendritic input current, Brian2 needs to have an explicit variable I_d_ext when building the L5 PC model\n",
    "sig_ou_s = 100 #Noise on the external somatic input current in pA\n",
    "sig_ou_d = 100 #Noise on the external dendritic input current in pA\n",
    "soma,dend= pyramidal_model(N_PC)  #Function that defines the L5 pyramidal model \n",
    "dend_spikemonitor = SpikeMonitor(dend) # brian2 monitor recording spikes from a Neuron group\n",
    "dend_rate = PopulationRateMonitor(dend) # brian2 monitor recording time-varying firing rate of a population of neurons\n",
    "\n",
    "#Create the SOM population\n",
    "I_SOM_ext = 90 #External input current to the SOM population in pA \n",
    "sig_ou_SOM = 400 #Noise on the external input current of SOM population in pA\n",
    "N_SOM = 400 # Number of SOM neurons\n",
    "\n",
    "SOM = SOM_interneurons(N_SOM) #Function that defines the SOM neuronal model\n",
    "\n",
    "#Connect SOMs with dendritic compartment of PCs\n",
    "#The learning rate eta was increased to reduce the simulation time without qualitative change of results\n",
    "tau_stdp = 20 * msecond #time constant of synaptic trace of post synaptic activity\n",
    "if plast_rule == 'pre_post_burst':\n",
    "    burst_target = 1 * Hz # target burst rate rho_0\n",
    "    target = 2 * burst_target * tau_stdp  #Similar to the inhibitory rule described in Vogels et al. 2011, the target burst rate relates mathematically with the target (target = 2 * target burst rate * tau_stdp)\n",
    "    SOM_dend = inh_plast_pre_post_burst(SOM, dend, conn_p = 1, weight = 10, eta = 0.5) #eta in paper = 0.1\n",
    "elif plast_rule == 'pre_post_ca':\n",
    "    target = 0.05 #a.u. The target value that corresponds with a burst rate of 1 Hz was determined empirically\n",
    "    SOM_dend = inh_plast_pre_post_ca(SOM, dend, conn_p = 1, weight = 10, eta = 0.5) #eta in paper = 0.1\n",
    "elif plast_rule == 'post_burst':\n",
    "    burst_target = 1*Hz\n",
    "    target = burst_target * tau_stdp # The target value relates to the target rate (target  = burst_target * tau_stdp)\n",
    "    SOM_dend = inh_plast_post_burst(SOM, dend, conn_p = 1, weight = 10, eta = 0.5) #eta in paper = 0.1\n",
    "\n",
    "var_SOM_dend=StateMonitor(SOM_dend, ('w'),record=np.arange(25),dt=100*msecond) # brian2 monitor recording state variables of the synapse object, in this case the first 25 connections each 100 ms.\n",
    "\n",
    "#Run the simulations multiple times for strong and weak input currents and store the data for plotting\n",
    "store() #Store the state of the network and all included objects.\n",
    "burst_rate = {}\n",
    "w_SOM_dend = {}\n",
    "burst_times = {}\n",
    "for j in range (len(I_d_ext_val)):\n",
    "    print('-> {} pA of external dend input'.format(I_d_ext_val[j]/pamp))\n",
    "    restore() #Restore the state of the network and all included objects.\n",
    "    I_d_ext = I_d_ext_val[j] #define dendritic input level\n",
    "    run((simulation_time)*second,report='stdout',profile=True) #run the simulation\n",
    "    #store the data after running the simulation\n",
    "    burst_rate[j] = dend_rate.smooth_rate(window='flat', width = 1000 * ms)/Hz #store the burst rate\n",
    "    w_SOM_dend[j] = var_SOM_dend.w #store the weights\n",
    "    burst_times[j] = dend_spikemonitor.spike_trains() #store the burst times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#---------------------------------------------------------------------------------------------\n",
    "# Plot the burst and weight dynamics during learning\n",
    "#---------------------------------------------------------------------------------------------\n",
    "\n",
    "colors = ['#F68E71','#A31E22']\n",
    "\n",
    "#Figure plotting the burst rate and weights for low and high dendritic input levels.\n",
    "#Note that the burst rate for the first and last time points is lower, this is an artefact caused by the smoothening function of brian2.\n",
    "plt.figure(figsize = (4,2))\n",
    "\n",
    "plt.subplot(121)\n",
    "for j in range (len(I_d_ext_val)):\n",
    "    time = np.arange(0, len(burst_rate[j])/10000,0.0001) #sampling rate of the burst rate is 0.1 ms so the step size of the numpy array is 0.0001 seconds\n",
    "    plt.plot(time,burst_rate[j],colors[j])\n",
    "plt.ylabel('burst rate (Hz)', fontname='Arial', fontsize=8)\n",
    "plt.xlabel('time (s)', fontname='Arial', fontsize=8)\n",
    "\n",
    "plt.subplot(122)\n",
    " #weights were sampled every 100 ms\n",
    "for j in range (len(I_d_ext_val)):\n",
    "    for i in range (25):\n",
    "        time = np.arange(0, len(w_SOM_dend[j][i])/10, 0.1) #sampling rate of the weight values rate is 100 ms (to reduce memory needs) so the step size of the numpy array is 0.1 seconds\n",
    "        plt.plot(time, w_SOM_dend[j][i],color = colors[j], alpha = 0.2) #plot individual weight traces\n",
    "    average_weights = np.mean(w_SOM_dend[j], axis=0) # compute the mean weight\n",
    "    plt.plot(time,average_weights,color = colors[j]) #plot the mean weight of the 25 traces\n",
    "plt.ylabel(r'$w^{\\mathrm{{SOM}}\\rightarrow \\mathrm{dend}}$ (pA)', fontname='Arial', fontsize=8)\n",
    "plt.xlabel('time (s)', fontname='Arial', fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1140x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#---------------------------------------------------------------------------------------------\n",
    "#Plot burst rasters before and after learning for low and high dendritic input levels\n",
    "#---------------------------------------------------------------------------------------------\n",
    "def raster_bursts(burst_times_dict, start_time, stop_time, colors, ax):\n",
    "    '''\n",
    "    Function plotting the raster plots of burst activity\n",
    "    :param burst_times_dict: cictionary with the burst times for low and high dendritic input values\n",
    "    :param start_time:  start time of plotting bursts\n",
    "    :param stop_time: stop time of plotting bursts\n",
    "    :param colors: colors for low and high dendritic input currents\n",
    "    :param ax: plotting axis\n",
    "    :return: synapse object\n",
    "    '''\n",
    "    \n",
    "    y_pos = 0\n",
    "    for j in range(len(burst_times_dict)):\n",
    "        burst_times= spike_times_unitless(burst_times_dict[j])\n",
    "        for i in range(10):\n",
    "            start_idx, stop_idx = raster_interval_idx(start_time, stop_time, burst_times[i], 'idx') # retrieve the indices of the spikes\n",
    "            if start_idx != 'no_spikes':\n",
    "                plt.vlines(burst_times[i][start_idx:stop_idx], y_pos, y_pos + 0.85, color=colors[j], linewidth=1)\n",
    "            y_pos += 1\n",
    "            if y_pos == 10:\n",
    "                plt.hlines(y_pos, start_time , stop_time, linestyle='--', color = 'k',alpha=0.5, linewidth=1)\n",
    "    \n",
    "    plt.ylim(0,20)\n",
    "    plt.xlabel('time (s)')\n",
    "    ax.spines['left'].set_visible(False) # remove the spines of the y-axis\n",
    "    ax.get_yaxis().set_visible(False)   \n",
    "\n",
    "fig_raster = plt.figure(figsize=(3.8, 1.6)) \n",
    "ax_1 = fig_raster.add_subplot(121)\n",
    "ax_1.set_title('before')\n",
    "raster_bursts(burst_times, 0, 5, colors, ax_1)\n",
    "ax_2 = fig_raster.add_subplot(122)\n",
    "ax_2.set_title('after')\n",
    "raster_bursts(burst_times, simulation_time-5, simulation_time, colors, ax_2) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#---------------------------------------------------------------------------------------------\n",
    "# Plot the interburst intervals before and after learning for low and high dendritic input levels\n",
    "#---------------------------------------------------------------------------------------------\n",
    "fig_IBI = plt.figure(figsize=(3, 1.6))\n",
    "colors = ['#F68E71','#A31E22']\n",
    "def histo_bursts(burst_times_dict, start, stop, colors, ax):\n",
    "    for j in range(len(burst_times_dict)):\n",
    "        burst_times= spike_times_unitless(burst_times_dict[j])\n",
    "        ISI = []\n",
    "        for i in range(len(burst_times)):\n",
    "            burst_times_interval = raster_interval_idx(start, stop, burst_times[i], 'spikes')\n",
    "            ISI.append(np.diff(burst_times_interval))\n",
    "        ISI_all = [item for sublist in ISI for item in sublist] #concatenate all the lists with interburst values into a single list\n",
    "        \n",
    "        hist = np.histogram(ISI_all, 200, range=(0, 3),density=False)\n",
    "        hist_values=hist[0]\n",
    "        hist_low_boundary = hist[1][1:]\n",
    "        plt.step(hist_low_boundary, hist_values, color=colors[j])\n",
    "    \n",
    "    plt.ylabel('count')\n",
    "    plt.xlabel('inter burst interval (s)')\n",
    "\n",
    "ax_1 = fig_IBI.add_subplot(121)\n",
    "ax_1.set_title('before')\n",
    "histo_bursts(burst_times, 1, 6, colors, ax_1)\n",
    "ax_2 = fig_IBI.add_subplot(122)\n",
    "ax_2.set_title('after')\n",
    "histo_bursts(burst_times, 45, 50, colors, ax_2) \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In a final step we demonstrate that the plasticity rule works for different target levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Target = 0.01\n",
      "Starting simulation at t=0. s for a duration of 50. s\n",
      "2.9129 (5%) simulated in 10s, estimated 2m 42s remaining.\n",
      "5.8683000000000005 (11%) simulated in 20s, estimated 2m 30s remaining.\n",
      "8.7573 (17%) simulated in 30s, estimated 2m 21s remaining.\n",
      "11.7241 (23%) simulated in 40s, estimated 2m 11s remaining.\n",
      "14.6403 (29%) simulated in 50s, estimated 2m 1s remaining.\n",
      "17.5612 (35%) simulated in 1m 0s, estimated 1m 51s remaining.\n",
      "20.474 (40%) simulated in 1m 10s, estimated 1m 41s remaining.\n",
      "23.4582 (46%) simulated in 1m 20s, estimated 1m 31s remaining.\n",
      "26.410099999999996 (52%) simulated in 1m 30s, estimated 1m 20s remaining.\n",
      "29.282300000000006 (58%) simulated in 1m 40s, estimated 1m 11s remaining.\n",
      "32.2172 (64%) simulated in 1m 50s, estimated 1m 1s remaining.\n",
      "35.1017 (70%) simulated in 2m 0s, estimated 51s remaining.\n",
      "38.0379 (76%) simulated in 2m 10s, estimated 41s remaining.\n",
      "40.9268 (81%) simulated in 2m 20s, estimated 31s remaining.\n",
      "43.8868 (87%) simulated in 2m 30s, estimated 21s remaining.\n",
      "46.8923 (93%) simulated in 2m 40s, estimated 11s remaining.\n",
      "49.9104 (99%) simulated in 2m 50s, estimated < 1s remaining.\n",
      "50.0 (100%) simulated in 2m 50s\n",
      "- Target = 0.05\n",
      "Starting simulation at t=0. s for a duration of 50. s\n",
      "2.871 (5%) simulated in 10s, estimated 2m 44s remaining.\n",
      "5.8371 (11%) simulated in 20s, estimated 2m 31s remaining.\n",
      "8.7348 (17%) simulated in 30s, estimated 2m 22s remaining.\n",
      "11.6234 (23%) simulated in 40s, estimated 2m 12s remaining.\n",
      "14.5372 (29%) simulated in 50s, estimated 2m 2s remaining.\n",
      "17.442600000000002 (34%) simulated in 1m 0s, estimated 1m 52s remaining.\n",
      "20.2984 (40%) simulated in 1m 10s, estimated 1m 42s remaining.\n",
      "23.1857 (46%) simulated in 1m 20s, estimated 1m 33s remaining.\n",
      "26.059500000000003 (52%) simulated in 1m 30s, estimated 1m 23s remaining.\n",
      "28.998900000000006 (57%) simulated in 1m 40s, estimated 1m 12s remaining.\n",
      "32.0125 (64%) simulated in 1m 50s, estimated 1m 2s remaining.\n",
      "34.9322 (69%) simulated in 2m 0s, estimated 52s remaining.\n",
      "37.8365 (75%) simulated in 2m 10s, estimated 42s remaining.\n",
      "40.7441 (81%) simulated in 2m 20s, estimated 32s remaining.\n",
      "43.7623 (87%) simulated in 2m 30s, estimated 21s remaining.\n",
      "46.6769 (93%) simulated in 2m 40s, estimated 11s remaining.\n",
      "49.5784 (99%) simulated in 2m 50s, estimated 1s remaining.\n",
      "50.0 (100%) simulated in 2m 51s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_scope()\n",
    "simulation_time = 50\n",
    "\n",
    "# With the variable plast_rule we define the inhibitory plasticity rule to control the burst rate of PCs.\n",
    "plast_rule = 'pre_post_burst' # Fig 1\n",
    "# plast_rule ='pre_post_ca' # Fig S1\n",
    "# plast_rule = 'post_burst' # Fig S2\n",
    "\n",
    "#create L5 PCs\n",
    "N_PC = 1600 #Number of L5 PC cells\n",
    "I_s_ext = 500 #External somatic input current in pA \n",
    "I_d_ext = 650 #External dendritic input current in pA \n",
    "sig_ou_s= 100  #Noise on the externa somatic input current in pA\n",
    "sig_ou_d= 100  #Noise on the externa dendritic input current in pA\n",
    "soma,dend= pyramidal_model(N_PC) #Function that defines the L5 pyramidal model \n",
    "dend_rate = PopulationRateMonitor(dend) # brian2 monitor recording time-varying firing rate of a population of neurons\n",
    "\n",
    "#create SOM population\n",
    "N_SOM=400 \n",
    "I_SOM_ext = 90 #External input current to the SOM population in pA \n",
    "sig_ou_SOM = 400 #Noise on the external input current of SOM population in pA\n",
    "SOM = SOM_interneurons(N_SOM) #Function that defines the SOM population\n",
    "\n",
    "#Connect SOMs with dendritic compartment of PCs\n",
    "#The learning rate eta was increased to reduce the simulation time without qualitative change of resultstau_stdp = 20 * msecond\n",
    "tau_stdp = 20 * msecond #time constant of synaptic trace of post synaptic activity\n",
    "if plast_rule == 'pre_post_burst':\n",
    "    target_val = [0.5, 2.5] * Hz * 2 * tau_stdp #Similar to the inhibitory rule described in Vogels et al. 2011, the target burst rate relates mathematically with the target (target = 2 * target burst rate * tau_stdp)\n",
    "    target = target_val[0]\n",
    "    SOM_dend = inh_plast_pre_post_burst(SOM, dend,conn_p = 1, weight = 10, eta = 0.5) \n",
    "    var_SOM_dend=StateMonitor(SOM_dend, ('w'),record=np.arange(25),dt=1*msecond)\n",
    "elif plast_rule == 'pre_post_ca':\n",
    "    target_val = [0.02,0.1] # The target values (a.u.) that corresponds with a burst rate of respectively 0.5 and 2 Hz\n",
    "    SOM_dend = inh_plast_pre_post_ca(SOM, dend, conn_p = 1, weight = 10, eta = 0.5)\n",
    "    var_SOM_dend=StateMonitor(SOM_dend, ('w'),record=np.arange(25),dt=1*msecond)\n",
    "elif plast_rule == 'post_burst':\n",
    "    target_val = [0.5,2.5] * Hz * tau_stdp # The target value relates to the target rate (target  = burst target rate * tau_stdp)\n",
    "    target = target_val[0]\n",
    "    SOM_dend = inh_plast_post_burst(SOM, dend, conn_p = 1, weight = 10, eta = 0.5)\n",
    "    var_SOM_dend=StateMonitor(SOM_dend, ('w'),record=np.arange(25),dt=1*msecond)\n",
    "\n",
    "burst_rate = []\n",
    "store() #Store the state of the network and all included objects.\n",
    "for i in range (len(target_val)):\n",
    "    print('- Target = {}'.format(target_val[i]))\n",
    "    restore() #To restore the state of the network and all included objects. This is important when running multiple simulations\n",
    "    target = target_val[i]\n",
    "    run(simulation_time*second,report='stdout',profile=True) #run the simulation\n",
    "    burst_rate.append(np.mean((dend_rate.rate/Hz)[-10000:])) #take the mean burst rate across all L5 PCs of the last second of the simulation\n",
    "    \n",
    "#---------------------------------------------------------------------------------------------\n",
    "# Plot the burst rate for different target values\n",
    "#---------------------------------------------------------------------------------------------\n",
    "\n",
    "plt.figure(figsize = (2,2))\n",
    "if plast_rule == 'pre_post_burst':\n",
    "    target_values = target_val/(Hz * 2 * tau_stdp)\n",
    "    x_label = 'target rate (Hz)'\n",
    "elif plast_rule == 'pre_post_ca':\n",
    "    target_values = target_val\n",
    "    xlabel = 'target (a.u.)'\n",
    "elif plast_rule == 'post_burst':\n",
    "    target_values = target_val/(Hz * tau_stdp)\n",
    "    x_label = 'target rate (Hz)'\n",
    "    \n",
    "plt.scatter(target_values,burst_rate, color = '#A31E22')\n",
    "plt.ylabel('burst rate (Hz)')\n",
    "plt.xlabel(x_label)\n",
    "plt.xticks(target_values)\n",
    "plt.ylim(0,4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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