Fig 2.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from brian2 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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-------------------------------------------------------------------------\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",
    "def PV_interneurons(N):\n",
    "    '''\n",
    "    Neuron model of Parvalbumin positive interneurons (PV) as defined in Naud et al. (2018). \n",
    "    :param N: Number of PV interneurons in the network\n",
    "    :return: PV interneuron population\n",
    "    '''\n",
    "    \n",
    "    inh_eqs = '''\n",
    "    du_i/dt = - (u_i - EL)/tau_PV + (I_inh) / C_PV: volt (unless refractory) # Voltage equation of PVs\n",
    "\n",
    "    I_inh = I_PV_ext * pamp + g_ampa  - g_gaba + I_bg_PV: amp #The total input current to the PVs\n",
    "\n",
    "    dI_bg_PV/dt = (mu_PV - I_bg_PV) / tau_ou_PV + sig_ou_PV * pamp * xi/sqrt(tau_ou_PV/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_PV = {\n",
    "        'tau_PV': 10 * msecond,\n",
    "        'C_PV': 100.0 * pfarad,\n",
    "        'EL': -0.07 * volt,\n",
    "\n",
    "\n",
    "        'mu_PV': 0 * pamp,\n",
    "        'tau_ou_PV': 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",
    "                            ''',\n",
    "                      refractory= 3 * msecond,\n",
    "                      namespace=params_PV, 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",
    "def inh_plast_pre_post(pre, post, target, 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 target: target value to set the burst and/or burst rate\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",
    "            'target':target\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"
   ]
  },
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   "source": [
    "### In this section we explore how inhibitory plasticity rules can control the burst rate and firing rate simulatenously"
   ]
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     "text": [
      "-> 700 pA of external somatic input\n",
      "Starting simulation at t=0. s for a duration of 50. s\n",
      "1.5765999999999998 (3%) simulated in 10s, estimated 5m 7s remaining.\n",
      "3.1282000000000005 (6%) simulated in 20s, estimated 5m 0s remaining.\n",
      "4.5989 (9%) simulated in 30s, estimated 4m 56s remaining.\n",
      "5.9739 (11%) simulated in 40s, estimated 4m 55s remaining.\n",
      "7.4373000000000005 (14%) simulated in 50s, estimated 4m 46s remaining.\n",
      "8.93 (17%) simulated in 1m 0s, estimated 4m 36s remaining.\n",
      "10.4192 (20%) simulated in 1m 10s, estimated 4m 26s remaining.\n",
      "11.9162 (23%) simulated in 1m 20s, estimated 4m 16s remaining.\n",
      "13.412500000000001 (26%) simulated in 1m 30s, estimated 4m 6s remaining.\n",
      "14.893600000000001 (29%) simulated in 1m 40s, estimated 3m 56s remaining.\n",
      "16.4003 (32%) simulated in 1m 50s, estimated 3m 45s remaining.\n",
      "17.9323 (35%) simulated in 2m 0s, estimated 3m 35s remaining.\n",
      "19.4581 (38%) simulated in 2m 10s, estimated 3m 24s remaining.\n",
      "20.973300000000002 (41%) simulated in 2m 20s, estimated 3m 14s remaining.\n",
      "22.447100000000002 (44%) simulated in 2m 30s, estimated 3m 4s remaining.\n",
      "23.9459 (47%) simulated in 2m 40s, estimated 2m 54s remaining.\n",
      "25.3485 (50%) simulated in 2m 50s, estimated 2m 45s remaining.\n",
      "26.7755 (53%) simulated in 3m 0s, estimated 2m 36s remaining.\n",
      "28.300800000000002 (56%) simulated in 3m 10s, estimated 2m 26s remaining.\n",
      "29.8256 (59%) simulated in 3m 20s, estimated 2m 15s remaining.\n",
      "31.3591 (62%) simulated in 3m 30s, estimated 2m 5s remaining.\n",
      "32.858000000000004 (65%) simulated in 3m 40s, estimated 1m 55s remaining.\n",
      "34.278600000000004 (68%) simulated in 3m 50s, estimated 1m 45s remaining.\n",
      "35.726400000000005 (71%) simulated in 4m 0s, estimated 1m 36s remaining.\n",
      "37.167100000000005 (74%) simulated in 4m 10s, estimated 1m 26s remaining.\n",
      "38.5902 (77%) simulated in 4m 20s, estimated 1m 17s remaining.\n",
      "40.0202 (80%) simulated in 4m 30s, estimated 1m 7s remaining.\n",
      "41.547000000000004 (83%) simulated in 4m 40s, estimated 57s remaining.\n",
      "42.967600000000004 (85%) simulated in 4m 50s, estimated 47s remaining.\n",
      "44.4486 (88%) simulated in 5m 0s, estimated 37s remaining.\n",
      "45.907700000000006 (91%) simulated in 5m 10s, estimated 28s remaining.\n",
      "47.3436 (94%) simulated in 5m 20s, estimated 18s remaining.\n",
      "48.7896 (97%) simulated in 5m 30s, estimated 8s remaining.\n",
      "50.0 (100%) simulated in 5m 38s\n",
      "-> 1100 pA of external somatic input\n",
      "Starting simulation at t=0. s for a duration of 50. s\n",
      "1.2866 (2%) simulated in 10s, estimated 6m 19s remaining.\n",
      "2.6671 (5%) simulated in 20s, estimated 5m 55s remaining.\n",
      "4.0174 (8%) simulated in 30s, estimated 5m 43s remaining.\n",
      "5.4085 (10%) simulated in 40s, estimated 5m 30s remaining.\n",
      "6.739599999999999 (13%) simulated in 50s, estimated 5m 21s remaining.\n",
      "8.1875 (16%) simulated in 1m 0s, estimated 5m 6s remaining.\n",
      "9.619 (19%) simulated in 1m 10s, estimated 4m 54s remaining.\n",
      "11.0594 (22%) simulated in 1m 20s, estimated 4m 42s remaining.\n",
      "12.4639 (24%) simulated in 1m 30s, estimated 4m 31s remaining.\n",
      "13.892300000000002 (27%) simulated in 1m 40s, estimated 4m 20s remaining.\n",
      "15.3029 (30%) simulated in 1m 50s, estimated 4m 9s remaining.\n",
      "16.7421 (33%) simulated in 2m 0s, estimated 3m 58s remaining.\n",
      "18.1827 (36%) simulated in 2m 10s, estimated 3m 47s remaining.\n",
      "19.62 (39%) simulated in 2m 20s, estimated 3m 37s remaining.\n",
      "21.1098 (42%) simulated in 2m 30s, estimated 3m 25s remaining.\n",
      "22.525000000000002 (45%) simulated in 2m 40s, estimated 3m 15s remaining.\n",
      "23.9402 (47%) simulated in 2m 50s, estimated 3m 5s remaining.\n",
      "25.505500000000005 (51%) simulated in 3m 0s, estimated 2m 53s remaining.\n",
      "27.070400000000006 (54%) simulated in 3m 10s, estimated 2m 41s remaining.\n",
      "28.587500000000006 (57%) simulated in 3m 20s, estimated 2m 30s remaining.\n",
      "30.102899999999998 (60%) simulated in 3m 30s, estimated 2m 19s remaining.\n",
      "31.69 (63%) simulated in 3m 40s, estimated 2m 7s remaining.\n",
      "33.2887 (66%) simulated in 3m 50s, estimated 1m 55s remaining.\n",
      "34.8293 (69%) simulated in 4m 0s, estimated 1m 45s remaining.\n",
      "36.1466 (72%) simulated in 4m 10s, estimated 1m 36s remaining.\n",
      "37.4976 (74%) simulated in 4m 20s, estimated 1m 27s remaining.\n",
      "39.0538 (78%) simulated in 4m 30s, estimated 1m 16s remaining.\n",
      "40.575 (81%) simulated in 4m 40s, estimated 1m 5s remaining.\n",
      "42.0171 (84%) simulated in 4m 50s, estimated 55s remaining.\n",
      "43.4514 (86%) simulated in 5m 0s, estimated 45s remaining.\n",
      "44.9551 (89%) simulated in 5m 10s, estimated 35s remaining.\n",
      "46.5065 (93%) simulated in 5m 20s, estimated 24s remaining.\n",
      "48.064600000000006 (96%) simulated in 5m 30s, estimated 13s remaining.\n",
      "49.705000000000005 (99%) simulated in 5m 40s, estimated 2s remaining.\n",
      "50.0 (100%) simulated in 5m 41s\n"
     ]
    }
   ],
   "source": [
    "start_scope() #\n",
    "simulation_time = 50\n",
    "tau_stdp = 20 * msecond\n",
    "\n",
    "#-------------------------------------------------------------------------\n",
    "# Create the neuronal populations\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "#Define the L5 Pyramidal neurons\n",
    "N_PC = 1600 #Number of L5 PC cells\n",
    "I_s_ext_val = [700,1100] #Low and high external somatic input current in pA\n",
    "I_s_ext = I_s_ext_val[0] # Low somatic input current, Brian2 needs to have an explicit variable I_s_ext when building the L5 PC model\n",
    "I_d_ext = 650 #External dendritic input current in pA \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",
    "soma_rate = PopulationRateMonitor(soma) # brian2 monitor recording time-varying firing rate of a population of neurons\n",
    "dend_rate = PopulationRateMonitor(dend) # brian2 monitor recording time-varying firing rate of a population of neurons\n",
    "\n",
    "#Define the SOM interneurons\n",
    "N_SOM = 400 # Number of SOM neurons\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 neuronal model\n",
    "# SOM_rate = PopulationRateMonitor(SOM)  # brian2 monitor recording time-varying firing rate of a population of neurons\n",
    "\n",
    "#Define the PV interneurons\n",
    "N_PV = 400 # Number of PV neurons\n",
    "I_PV_ext = -45 #External input current to the PV population in pA \n",
    "sig_ou_PV = 400 #Noise on the external input current of PV population in pA\n",
    "PV = PV_interneurons(N_PV) #Function that defines the PV neuronal model\n",
    "# PV_rate = PopulationRateMonitor(PV)\n",
    "\n",
    "#-------------------------------------------------------------------------\n",
    "# Connect the interneurons with the PCs\n",
    "#-------------------------------------------------------------------------\n",
    "#The learning rate eta was increased to reduce the simulation time without qualitative change of results\n",
    "\n",
    "#Connect SOMs with the dendrites\n",
    "burst_target_rate = 1 * Hz\n",
    "burst_target =  2 *burst_target_rate * tau_stdp # burst rate in Hz\n",
    "SOM_dend = inh_plast_pre_post(SOM, dend, target  = burst_target ,  conn_p = 1, weight = 10, eta = 1) #eta in paper is 0.1\n",
    "var_SOM_dend=StateMonitor(SOM_dend, ('w'),record=np.arange(25),dt=100*msecond)\n",
    "\n",
    "#Connect PVs with somata\n",
    "firing_target_rate = 10 * Hz\n",
    "firing_target =  2 * firing_target_rate * tau_stdp # firing rate in Hz\n",
    "PV_soma = inh_plast_pre_post(PV, soma, target = firing_target, conn_p = 1, weight = 10, eta = 0.1) #eta in paper is 0.01\n",
    "var_PV_soma=StateMonitor(PV_soma, ('w'),record=np.arange(25),dt=100*msecond)\n",
    "\n",
    "#-------------------------------------------------------------------------\n",
    "#Run the simulations for strong and weak input currents\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "store()\n",
    "burst_rate = {}\n",
    "firing_rate = {}\n",
    "w_SOM_dend = {}\n",
    "w_PV_soma = {}\n",
    "for j in range (len(I_s_ext_val)):\n",
    "    print('-> {} pA of external somatic input'.format(I_s_ext_val[j]))\n",
    "    restore()\n",
    "    I_s_ext = I_s_ext_val[j]\n",
    "    run((simulation_time)*second,report='stdout',profile=True)\n",
    "    burst_rate[j] = dend_rate.smooth_rate(window='flat', width = 1000 * ms)/Hz\n",
    "    firing_rate[j] = soma_rate.smooth_rate(window='flat', width = 1000 * ms)/Hz\n",
    "    w_SOM_dend[j] = var_SOM_dend.w\n",
    "    w_PV_soma[j] = var_PV_soma.w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-------------------------------------------------------------------------\n",
    "# Plot the figure\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "fig_rate_weights = plt.figure(figsize=(1.4 * 2, 1.2*2), dpi=300)\n",
    "\n",
    "colors_dend = ['#F78E6F','#A00E14']\n",
    "colors_soma = ['#A9A9A9','#000000']\n",
    "\n",
    "ax_1 = fig_rate_weights.add_subplot(221)\n",
    "for j in range (len(I_s_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_dend[j], alpha = 0.2) #plot individual weigth traces\n",
    "    average_weights = np.mean(w_SOM_dend[j], axis=0)  #plot mean weight of the recorded weights\n",
    "    plt.plot(time,average_weights,color = colors_dend[j])\n",
    "plt.ylabel(r'$w^{\\mathrm{{SOM}}\\rightarrow \\mathrm{dend}}$ (pA)', fontname='Arial', fontsize=8)\n",
    "\n",
    "ax_2 = fig_rate_weights.add_subplot(222)\n",
    "for j in range (len(I_s_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_dend[j])\n",
    "plt.hlines(burst_target/(2*Hz*tau_stdp), 0, simulation_time, linestyles=(0, (1, 1)), color='darkgrey') # plot the target firing rate\n",
    "plt.ylabel('burst rate (Hz)', fontname='Arial', fontsize=8)\n",
    "\n",
    "ax_3 = fig_rate_weights.add_subplot(223)\n",
    "for j in range (len(I_s_ext_val)):\n",
    "    for i in range (25):\n",
    "        time = np.arange(0, len(w_PV_soma[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_PV_soma[j][i],color = colors_soma[j], alpha = 0.2) #plot individual weigth traces\n",
    "    average_weights = np.mean(w_PV_soma[j], axis=0) #plot mean weight of the recorded weights\n",
    "    plt.plot(time,average_weights,color = colors_soma[j])\n",
    "plt.ylabel(r'$w^{\\mathrm{{PV}}\\rightarrow \\mathrm{soma}}$ (pA)', fontname='Arial', fontsize=8)\n",
    "plt.xlabel('time (s)', fontname='Arial', fontsize=8)\n",
    "\n",
    "ax_4 = fig_rate_weights.add_subplot(224)\n",
    "for j in range (len(I_s_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,firing_rate[j],colors_soma[j])\n",
    "plt.hlines(firing_target/(2*Hz*tau_stdp), 0, simulation_time, linestyles=(0, (1, 1)), color='darkgrey') # plot the target burst rate\n",
    "plt.ylabel('firing rate (Hz)', fontname='Arial', fontsize=8)\n",
    "plt.xlabel('time (s)', fontname='Arial', fontsize=8)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}