tutorials/W3D1_RealNeurons/W3D1_Tutorial2.ipynb

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    "<a href=\"https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D1_RealNeurons/W3D1_Tutorial2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "# Neuromatch Academy: Week 3, Day 1, Tutorial 2\n",
    "# Real Neurons: Effects of Input Correlation\n",
    "__Content creators:__ Qinglong Gu, Songtin Li, John Murray, Richard Naud, Arvind Kumar\n",
    "\n",
    "__Content reviewers:__ Spiros Chavlis, Lorenzo Fontolan, Richard Gao, Matthew Krause"
   ]
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   "source": [
    "---\n",
    "# Tutorial Objectives\n",
    "In this tutorial, we will use the leaky integrate-and-fire (LIF) neuron model (see Tutorial 1) to study how they transform input correlations to output properties (transfer of correlations). In particular, we are going to write a few lines of code to:\n",
    "\n",
    "- inject correlated GWN in a pair of neurons\n",
    "\n",
    "- measure correlations between the spiking activity of the two neurons\n",
    "\n",
    "- study how the transfer of correlation depends on the statistics of the input, i.e., mean and standard deviation."
   ]
  },
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   "source": [
    "---\n",
    "# Setup"
   ]
  },
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   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# @title Figure Settings\n",
    "import ipywidgets as widgets       # interactive display\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "# use NMA plot style\n",
    "plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content/master/nma.mplstyle\")\n",
    "my_layout = widgets.Layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "# @title Helper functions\n",
    "def default_pars(**kwargs):\n",
    "  pars = {}\n",
    "\n",
    "  ### typical neuron parameters###\n",
    "  pars['V_th'] = -55.     # spike threshold [mV]\n",
    "  pars['V_reset'] = -75.  # reset potential [mV]\n",
    "  pars['tau_m'] = 10.     # membrane time constant [ms]\n",
    "  pars['g_L'] = 10.       # leak conductance [nS]\n",
    "  pars['V_init'] = -75.   # initial potential [mV]\n",
    "  pars['V_L'] = -75.      # leak reversal potential [mV]\n",
    "  pars['tref'] = 2.       # refractory time (ms)\n",
    "\n",
    "  ### simulation parameters ###\n",
    "  pars['T'] = 400. # Total duration of simulation [ms]\n",
    "  pars['dt'] = .1  # Simulation time step [ms]\n",
    "\n",
    "  ### external parameters if any ###\n",
    "  for k in kwargs:\n",
    "    pars[k] = kwargs[k]\n",
    "\n",
    "  pars['range_t'] = np.arange(0, pars['T'], pars['dt'])  # Vector of discretized\n",
    "                                                         # time points [ms]\n",
    "  return pars\n",
    "\n",
    "\n",
    "def run_LIF(pars, Iinj):\n",
    "  \"\"\"\n",
    "  Simulate the LIF dynamics with external input current\n",
    "\n",
    "  Args:\n",
    "    pars       : parameter dictionary\n",
    "    Iinj       : input current [pA]. The injected current here can be a value or an array\n",
    "\n",
    "  Returns:\n",
    "    rec_spikes : spike times\n",
    "    rec_v      : mebrane potential\n",
    "  \"\"\"\n",
    "\n",
    "  # Set parameters\n",
    "  V_th, V_reset = pars['V_th'], pars['V_reset']\n",
    "  tau_m, g_L = pars['tau_m'], pars['g_L']\n",
    "  V_init, V_L = pars['V_init'], pars['V_L']\n",
    "  dt, range_t = pars['dt'], pars['range_t']\n",
    "  Lt = range_t.size\n",
    "  tref = pars['tref']\n",
    "\n",
    "  # Initialize voltage and current\n",
    "  v = np.zeros(Lt)\n",
    "  v[0] = V_init\n",
    "  Iinj = Iinj * np.ones(Lt)\n",
    "  tr = 0.\n",
    "\n",
    "  # simulate the LIF dynamics\n",
    "  rec_spikes = []   # record spike times\n",
    "  for it in range(Lt - 1):\n",
    "    if tr > 0:\n",
    "      v[it] = V_reset\n",
    "      tr = tr - 1\n",
    "    elif v[it] >= V_th:  # reset voltage and record spike event\n",
    "      rec_spikes.append(it)\n",
    "      v[it] = V_reset\n",
    "      tr = tref / dt\n",
    "\n",
    "    # calculate the increment of the membrane potential\n",
    "    dv = (-(v[it] - V_L) + Iinj[it] / g_L) * (dt / tau_m)\n",
    "\n",
    "    # update the membrane potential\n",
    "    v[it + 1] = v[it] + dv\n",
    "\n",
    "  rec_spikes = np.array(rec_spikes) * dt\n",
    "\n",
    "  return v, rec_spikes\n",
    "\n",
    "\n",
    "def my_GWN(pars, sig, myseed=False):\n",
    "  \"\"\"\n",
    "  Function that calculates Gaussian white noise inputs\n",
    "\n",
    "  Args:\n",
    "    pars       : parameter dictionary\n",
    "    mu         : noise baseline (mean)\n",
    "    sig        : noise amplitute (standard deviation)\n",
    "    myseed     : random seed. int or boolean\n",
    "                 the same seed will give the same random number sequence\n",
    "\n",
    "  Returns:\n",
    "    I          : Gaussian white noise input\n",
    "  \"\"\"\n",
    "\n",
    "  # Retrieve simulation parameters\n",
    "  dt, range_t = pars['dt'], pars['range_t']\n",
    "  Lt = range_t.size\n",
    "\n",
    "  # Set random seed. You can fix the seed of the random number generator so\n",
    "  # that the results are reliable however, when you want to generate multiple\n",
    "  # realization make sure that you change the seed for each new realization\n",
    "  if myseed:\n",
    "      np.random.seed(seed=myseed)\n",
    "  else:\n",
    "      np.random.seed()\n",
    "\n",
    "  # generate GWN\n",
    "  # we divide here by 1000 to convert units to sec.\n",
    "  I_GWN = sig * np.random.randn(Lt) * np.sqrt(pars['tau_m'] / dt)\n",
    "\n",
    "  return I_GWN\n",
    "\n",
    "\n",
    "def Poisson_generator(pars, rate, n, myseed=False):\n",
    "  \"\"\"\n",
    "  Generates poisson trains\n",
    "\n",
    "  Args:\n",
    "    pars       : parameter dictionary\n",
    "    rate       : noise amplitute [Hz]\n",
    "    n          : number of Poisson trains\n",
    "    myseed     : random seed. int or boolean\n",
    "\n",
    "  Returns:\n",
    "    pre_spike_train : spike train matrix, ith row represents whether\n",
    "                      there is a spike in ith spike train over time\n",
    "                      (1 if spike, 0 otherwise)\n",
    "  \"\"\"\n",
    "\n",
    "  # Retrieve simulation parameters\n",
    "  dt, range_t = pars['dt'], pars['range_t']\n",
    "  Lt = range_t.size\n",
    "\n",
    "  # set random seed\n",
    "  if myseed:\n",
    "      np.random.seed(seed=myseed)\n",
    "  else:\n",
    "      np.random.seed()\n",
    "\n",
    "  # generate uniformly distributed random variables\n",
    "  u_rand = np.random.rand(n, Lt)\n",
    "\n",
    "  # generate Poisson train\n",
    "  poisson_train = 1. * (u_rand < rate * (dt / 1000.))\n",
    "\n",
    "  return poisson_train"
   ]
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   "source": [
    "The helper function contains the:\n",
    "\n",
    "- Parameter dictionary: `default_pars( **kwargs)`\n",
    "- LIF simulator: `run_LIF`\n",
    "- Gaussian white noise generator: `my_GWN(pars, sig, myseed=False)`\n",
    "- Poisson type spike train genertor: `Poisson_generator(pars, rate, n, myseed=False)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L66i6gE6Tdll"
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   "source": [
    "---\n",
    "# Section 1: Correlations (Synchrony)\n",
    "Correlation or synchrony in neuronal activity can be described for any readout of brain activity. Here, we are concerned with the spiking activity of neurons. \n",
    "\n",
    "In the simplest way, correlation/synchrony refers to coincident spiking of neurons, i.e., when two neurons spike together, they are firing in **synchrony** or are **correlated**. Neurons can be synchronous in their instantaneous activity, i.e., they spike together with some probability. However, it is also possible that spiking of a neuron at time $t$ is correlated with the spikes of another neuron with a delay (time-delayed synchrony). \n",
    "\n",
    "## Origin of synchronous neuronal activity:\n",
    "- Common inputs, i.e., two neurons, are receiving input from the same sources. The degree of correlation of the shared inputs is proportional to their output correlation.\n",
    "- Pooling from the same sources. Neurons do not share the same input neurons but are receiving inputs from neurons which themselves are correlated.\n",
    "- Neurons are connected to each other (uni- or bi-directionally): This will only give rise to time-delayed synchrony. Neurons could also be connected via gap-junctions.\n",
    "- Neurons have similar parameters and initial conditions.\n",
    "\n",
    "## Implications of synchrony\n",
    "When neurons spike together, they can have a stronger impact on downstream neurons. Synapses in the brain are sensitive to the temporal correlations (i.e., delay) between pre- and postsynaptic activity, and this, in turn, can lead to the formation of functional neuronal networks - the basis of unsupervised learning (we will study some of these concepts in a forthcoming tutorial).\n",
    "\n",
    "Synchrony implies a reduction in the dimensionality of the system. In addition, correlations, in many cases, can impair the decoding of neuronal activity."
   ]
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     "text": [
      "Video available at https://youtube.com/watch?v=nsAYFBcAkes\n"
     ]
    },
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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"854\"\n",
       "            height=\"480\"\n",
       "            src=\"https://www.youtube.com/embed/nsAYFBcAkes?fs=1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fbac249ea50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# @title Video 1: Input Correlations\n",
    "from IPython.display import YouTubeVideo\n",
    "video = YouTubeVideo(id=\"nsAYFBcAkes\", width=854, height=480, fs=1)\n",
    "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
    "video"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Is8piETq2_J0"
   },
   "source": [
    "## How to study the emergence of correlations\n",
    "A simple model to study the emergence of correlations is to inject common inputs to a pair of neurons and measure the output correlation as a function of the fraction of common inputs. \n",
    "\n",
    "Here, we are going to investigate the transfer of correlations by computing the correlation coefficient of spike trains recorded from two unconnected LIF neurons, which received correlated inputs.\n",
    "\n",
    "\n",
    "The input current to LIF neuron $i$ $(i=1,2)$ is:\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{I_i}{g_L} =\\mu_i + \\sigma_i (\\sqrt{1-c}\\xi_i + \\sqrt{c}\\xi_c) \\quad (1)\n",
    "\\end{equation}\n",
    "\n",
    "where $\\mu_i$ is the temporal average of the current. The Gaussian white noise $\\xi_i$ is independent for each neuron, while $\\xi_c$ is common to all neurons. The variable $c$ ($0\\le c\\le1$) controls the fraction of common and independent inputs. $\\sigma_i$ shows the variance of the total input.\n",
    "\n",
    "So, first, we will generate correlated inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 242
    },
    "colab_type": "code",
    "id": "ZLLRFa2YJOQD",
    "outputId": "32670f1d-3730-47ce-df70-01da1a150095"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function correlate_input in module __main__:\n",
      "\n",
      "correlate_input(pars, mu=20.0, sig=7.5, c=0.3)\n",
      "    Args:\n",
      "      pars       : parameter dictionary\n",
      "      mu         : noise baseline (mean)\n",
      "      sig        : noise amplitute (standard deviation)\n",
      "      c.         : correlation coefficient ~[0, 1]\n",
      "    \n",
      "    Returns:\n",
      "      I1gL, I2gL : two correlated inputs with corr. coe. c\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# @title Function for generating correlated GWN inputs\n",
    "def correlate_input(pars, mu=20., sig=7.5, c=0.3):\n",
    "  \"\"\"\n",
    "  Args:\n",
    "    pars       : parameter dictionary\n",
    "    mu         : noise baseline (mean)\n",
    "    sig        : noise amplitute (standard deviation)\n",
    "    c.         : correlation coefficient ~[0, 1]\n",
    "\n",
    "  Returns:\n",
    "    I1gL, I2gL : two correlated inputs with corr. coe. c\n",
    "  \"\"\"\n",
    "\n",
    "  # generate Gaussian whute noise xi_1, xi_2, xi_c\n",
    "  xi_1 = my_GWN(pars, sig)\n",
    "  xi_2 = my_GWN(pars, sig)\n",
    "  xi_c = my_GWN(pars, sig)\n",
    "\n",
    "  # Generate two correlated inputs by Equation. (1)\n",
    "  I1gL = mu + np.sqrt(1. - c) * xi_1 + np.sqrt(c) * xi_c\n",
    "  I2gL = mu + np.sqrt(1. - c) * xi_2 + np.sqrt(c) * xi_c\n",
    "\n",
    "  return I1gL, I2gL\n",
    "\n",
    "\n",
    "def example_plot_myCC():\n",
    "  pars = default_pars(T=50000, dt=.1)\n",
    "\n",
    "  c = np.arange(10) * 0.1\n",
    "  r12 = np.zeros(10)\n",
    "  for i in range(10):\n",
    "    I1gL, I2gL = correlate_input(pars, mu=20.0, sig=7.5, c=c[i])\n",
    "    r12[i] = my_CC(I1gL, I2gL)\n",
    "\n",
    "  plt.figure()\n",
    "  plt.plot(c, r12, 'bo', alpha=0.7, label='Simulation', zorder=2)\n",
    "  plt.plot([-0.05, 0.95], [-0.05, 0.95], 'k--', label='y=x',\n",
    "           dashes=(2, 2), zorder=1)\n",
    "  plt.xlabel('True CC')\n",
    "  plt.ylabel('Sample CC')\n",
    "  plt.legend(loc='best')\n",
    "\n",
    "\n",
    "print(help(correlate_input))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q8CfqV7g4XAG"
   },
   "source": [
    "### Exercise 1: Compute the correlation\n",
    "\n",
    "From the inputs $I_i(t)$ ($i=1,\\ 2$), we can obtain $\\{ I_i^1, ..., I_i^L \\}$ on the time-grid of step size $dt$, where $I_i^k = I_i(k\\cdot dt)$. The _sample correlation coefficient_:  \n",
    "\n",
    "$$r_{ij} = \\frac{\\displaystyle{\\sum_{k=1}^L{(I_i^k-\\bar{I}_i)}{(I^k_j-\\bar{I}_j)}}}{\\sqrt{\\displaystyle{\\sum_{k=1}^L{(I_i^k-\\bar{I}_i)^2}}} \\sqrt{\\displaystyle{\\sum_{k=1}^L{(I_j^k-\\bar{I}_j)^2}}}} \\quad (2)$$\n",
    "where $\\bar{I}_i$ is the sample mean. The _sample correlation coefficient_ may also be referred to as the _sample Pearson correlation coefficient_.\n",
    "\n",
    "Here, is a beautiful paper that explains multiple ways to calculate and understand correlations [Rodgers and Nicewander 1988](https://www.stat.berkeley.edu/~rabbee/correlation.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cNRakP99Hlqr"
   },
   "outputs": [],
   "source": [
    "def my_CC(x, y):\n",
    "  \"\"\"\n",
    "  Args:\n",
    "    x, y  : two time series with the same length\n",
    "\n",
    "  Returns:\n",
    "    rxy   : correlation coefficient\n",
    "  \"\"\"\n",
    "  ########################################################################\n",
    "  ## TODO for students: compute rxy, then remove the NotImplementedError #\n",
    "  # Tip1: array([a1, a2, a3])*array([b1, b2, b3]) = array([a1*b1, a2*b2, a3*b3])\n",
    "  # Tip2: np.sum(array([a1, a2, a3])) = a1+a2+a3\n",
    "  # Tip3: square root, np.sqrt()\n",
    "  # Fill out function and remove\n",
    "  raise NotImplementedError(\"Student excercise: compute the sample correlation coefficient\")\n",
    "  ########################################################################\n",
    "  # Calculate the covariance of x and y\n",
    "  xy = ...\n",
    "  # Calculate the covariance of x\n",
    "  xx = ...\n",
    "  # Calculate the covariance of y\n",
    "  yy = ...\n",
    "  # Calculate the correlation coefficient\n",
    "  rxy = ...\n",
    "\n",
    "  return rxy\n",
    "\n",
    "\n",
    "# Uncomment the line after completing the my_CC function\n",
    "# example_plot_myCC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 431
    },
    "colab_type": "code",
    "id": "oFsWkq0P4WHg",
    "outputId": "fe533100-0755-49a1-923a-3d7494454a37"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 558
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to_remove solution\n",
    "def my_CC(x, y):\n",
    "  \"\"\"\n",
    "  Args:\n",
    "    x, y  : two time series with the same length\n",
    "\n",
    "  Returns:\n",
    "    rxy   : correlation coefficient\n",
    "  \"\"\"\n",
    "  # Calculate the covariance of x and y\n",
    "  xy = ((x - x.mean()) * (y - y.mean())).sum()\n",
    "  # Calculate the covariance of x\n",
    "  xx = ((x - x.mean()) * (x - x.mean())).sum()\n",
    "  # Calculate the covariance of y\n",
    "  yy = ((y - y.mean())*(y - y.mean())).sum()\n",
    "  # Calculate the correlation coefficient\n",
    "  rxy = xy / np.sqrt(xx * yy)\n",
    "\n",
    "  return rxy\n",
    "\n",
    "\n",
    "with plt.xkcd():\n",
    "  example_plot_myCC()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SgKLPRqwM5_z"
   },
   "source": [
    "### Exercise 2: Measure the correlation between spike trains\n",
    "\n",
    "After recording the spike times of the two neurons, how can we estimate their correlation coefficient? \n",
    "\n",
    "In order to find this, we need to bin the spike times and obtain two time series. Each data point in the time series is the number of spikes in the corresponding time bin. You can use `np.histogram()` to bin the spike times.\n",
    "\n",
    "Complete the code below to bin the spike times and calculate the correlation coefficient for two Poisson spike trains. Note that `c` here is the ground-truth correlation coefficient that we define.\n",
    "\n",
    "Note: please execute the below cell first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 242
    },
    "colab_type": "code",
    "id": "U9MU6wgtPpuU",
    "outputId": "f457fb31-ce51-4d57-f05c-aad117ad3f7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function generate_corr_Poisson in module __main__:\n",
      "\n",
      "generate_corr_Poisson(pars, poi_rate, c, myseed=False)\n",
      "    function to generate correlated Poisson type spike trains\n",
      "    Args:\n",
      "      pars       : parameter dictionary\n",
      "      poi_rate   : rate of the Poisson train\n",
      "      c.         : correlation coefficient ~[0, 1]\n",
      "    \n",
      "    Returns:\n",
      "      sp1, sp2   : two correlated spike time trains with corr. coe. c\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# @title\n",
    "\n",
    "# @markdown Execute this cell to generate correlated Poisson inputs!\n",
    "\n",
    "\n",
    "def generate_corr_Poisson(pars, poi_rate, c, myseed=False):\n",
    "  \"\"\"\n",
    "  function to generate correlated Poisson type spike trains\n",
    "  Args:\n",
    "    pars       : parameter dictionary\n",
    "    poi_rate   : rate of the Poisson train\n",
    "    c.         : correlation coefficient ~[0, 1]\n",
    "\n",
    "  Returns:\n",
    "    sp1, sp2   : two correlated spike time trains with corr. coe. c\n",
    "  \"\"\"\n",
    "\n",
    "  range_t = pars['range_t']\n",
    "\n",
    "  mother_rate = poi_rate / c\n",
    "  mother_spike_train = Poisson_generator(pars, rate=mother_rate,\n",
    "                                         n=1, myseed=myseed)[0]\n",
    "  sp_mother = range_t[mother_spike_train > 0]\n",
    "\n",
    "  L_sp_mother = len(sp_mother)\n",
    "  sp_mother_id = np.arange(L_sp_mother)\n",
    "  L_sp_corr = int(L_sp_mother * c)\n",
    "\n",
    "  np.random.shuffle(sp_mother_id)\n",
    "  sp1 = np.sort(sp_mother[sp_mother_id[:L_sp_corr]])\n",
    "\n",
    "  np.random.shuffle(sp_mother_id)\n",
    "  sp2 = np.sort(sp_mother[sp_mother_id[:L_sp_corr]])\n",
    "\n",
    "  return sp1, sp2\n",
    "\n",
    "\n",
    "print(help(generate_corr_Poisson))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oEPDC5YGSXPc"
   },
   "outputs": [],
   "source": [
    "def corr_coeff_pairs(pars, rate, c, trials, bins):\n",
    "  \"\"\"\n",
    "  Calculate the correlation coefficient of two spike trains, for different\n",
    "  realizations\n",
    "\n",
    "  Args:\n",
    "      pars   : parameter dictionary\n",
    "      rate   : rate of poisson inputs\n",
    "      c      : correlation coefficient ~ [0, 1]\n",
    "      trais  : number of realizations\n",
    "      bins   : vector with bins for time discretization\n",
    "\n",
    "  Returns:\n",
    "    r12      : correlation coefficient of a pair of inputs\n",
    "  \"\"\"\n",
    "\n",
    "  r12 = np.zeros(n_trials)\n",
    "\n",
    "  for i in range(n_trials):\n",
    "    ##############################################################\n",
    "    ## TODO for students: Use np.histogram to bin the spike time #\n",
    "    ##   e.g., sp1_count, _= np.histogram(...)\n",
    "    # Use my_CC() compute corr coe, compare with c\n",
    "    # Note that you can run multiple realizations and compute their r_12(diff_trials)\n",
    "    # with the defined function above. The average r_12 over trials can get close to c.\n",
    "    # Note: change seed to generate different input per trial\n",
    "    # Fill out function and remove\n",
    "    raise NotImplementedError(\"Student excercise: compute the correlation coefficient\")\n",
    "    ##############################################################\n",
    "    # Generate correlated Poisson inputs\n",
    "    sp1, sp2 = generate_corr_Poisson(pars, ..., ..., myseed=2020+i)\n",
    "    # Bin the spike times of the first input\n",
    "    sp1_count, _ = np.histogram(..., ...)\n",
    "    # Bin the spike times of the second input\n",
    "    sp2_count, _ = np.histogram(..., bins=...)\n",
    "    # Calculate the correlation coefficient\n",
    "    r12[i] = my_CC(..., ...)\n",
    "\n",
    "  return r12\n",
    "\n",
    "\n",
    "poi_rate = 20.\n",
    "c = 0.2  # set true correlation\n",
    "pars = default_pars(T=10000)\n",
    "\n",
    "# bin the spike time\n",
    "bin_size = 20  # [ms]\n",
    "my_bin = np.arange(0, pars['T'], bin_size)\n",
    "n_trials = 100  # 100 realizations\n",
    "\n",
    "# Uncomment to test your function\n",
    "# r12 = corr_coeff_pairs(pars, rate=poi_rate, c=c, trials=n_trials, bins=my_bin)\n",
    "# print(f'True corr coe = {c:.3f}')\n",
    "# print(f'Simu corr coe = {r12.mean():.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1puI4yGdwktJ"
   },
   "source": [
    "Sample output\n",
    "\n",
    "```\n",
    "True corr coe = 0.200\n",
    "Simu corr coe = 0.197\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "Mim54TJNSk9x",
    "outputId": "ed474663-91f1-4f36-82d6-4365e3b476f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True corr coe = 0.200\n",
      "Simu corr coe = 0.197\n"
     ]
    }
   ],
   "source": [
    "# to_remove solution\n",
    "def corr_coeff_pairs(pars, rate, c, trials, bins):\n",
    "  \"\"\"\n",
    "  Calculate the correlation coefficient of two spike trains, for different\n",
    "  realizations\n",
    "\n",
    "  Args:\n",
    "      pars   : parameter dictionary\n",
    "      rate   : rate of poisson inputs\n",
    "      c      : correlation coefficient ~ [0, 1]\n",
    "      trais  : number of realizations\n",
    "      bins   : vector with bins for time discretization\n",
    "\n",
    "  Returns:\n",
    "    r12      : correlation coefficient of a pair of inputs\n",
    "  \"\"\"\n",
    "\n",
    "  r12 = np.zeros(n_trials)\n",
    "\n",
    "  for i in range(n_trials):\n",
    "    # Generate correlated Poisson inputs\n",
    "    sp1, sp2 = generate_corr_Poisson(pars, poi_rate, c, myseed=2020+i)\n",
    "    # Bin the spike times of the first input\n",
    "    sp1_count, _ = np.histogram(sp1, bins=my_bin)\n",
    "    # Bin the spike times of the second input\n",
    "    sp2_count, _ = np.histogram(sp2, bins=my_bin)\n",
    "    # Calculate the correlation coefficient\n",
    "    r12[i] = my_CC(sp1_count, sp2_count)\n",
    "\n",
    "  return r12\n",
    "\n",
    "\n",
    "poi_rate = 20.\n",
    "c = 0.2  # set true correlation\n",
    "pars = default_pars(T=10000)\n",
    "# bin the spike time\n",
    "bin_size = 20  # [ms]\n",
    "my_bin = np.arange(0, pars['T'], bin_size)\n",
    "n_trials = 100  # 100 realizations\n",
    "\n",
    "r12 = corr_coeff_pairs(pars, rate=poi_rate, c=c, trials=n_trials, bins=my_bin)\n",
    "print(f'True corr coe = {c:.3f}')\n",
    "print(f'Simu corr coe = {r12.mean():.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6_PutaGqPC9_"
   },
   "source": [
    "---\n",
    "# Section 2: Investigate the effect of input correlation on the output correlation\n",
    "\n",
    "Now let's combine the aforementioned two procedures. We first generate the correlated inputs by Equation (1) (i.e., Exercise 1). Then we inject the correlated inputs $I_1, I_2$ into a pair of neurons and record their output spike times. We continue measuring the correlation between the output and \n",
    "investigate the relationship between the input correlation and the output correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "colab_type": "code",
    "id": "vaoCgDptcV-G",
    "outputId": "514c3039-9c4a-4e95-972e-89c1151f84d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function LIF_output_cc in module __main__:\n",
      "\n",
      "LIF_output_cc(pars, mu, sig, c, bin_size, n_trials=20)\n",
      "    Expects:\n",
      "    pars       : parameter dictionary\n",
      "    mu         : noise baseline (mean)\n",
      "    sig        : noise amplitute (standard deviation)\n",
      "    c          : correlation coefficient ~[0, 1]\n",
      "    bin_size   : bin size used for time series\n",
      "    n_trials   : total simulation trials\n",
      "    \n",
      "    Returns:\n",
      "    r          : output corr. coe.\n",
      "    sp_rate    : spike rate\n",
      "    sp1        : spike times of neuron 1 in the last trial\n",
      "    sp2        : spike times of neuron 2 in the last trial\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# @title LIF neurons with correlated input\n",
    "\n",
    "def LIF_output_cc(pars, mu, sig, c, bin_size, n_trials=20):\n",
    "  \"\"\"\n",
    "  Expects:\n",
    "  pars       : parameter dictionary\n",
    "  mu         : noise baseline (mean)\n",
    "  sig        : noise amplitute (standard deviation)\n",
    "  c          : correlation coefficient ~[0, 1]\n",
    "  bin_size   : bin size used for time series\n",
    "  n_trials   : total simulation trials\n",
    "\n",
    "  Returns:\n",
    "  r          : output corr. coe.\n",
    "  sp_rate    : spike rate\n",
    "  sp1        : spike times of neuron 1 in the last trial\n",
    "  sp2        : spike times of neuron 2 in the last trial\n",
    "  \"\"\"\n",
    "\n",
    "  r12 = np.zeros(n_trials)\n",
    "  sp_rate = np.zeros(n_trials)\n",
    "  for i_trial in range(n_trials):\n",
    "    I1gL, I2gL = correlate_input(pars, mu, sig, c)\n",
    "    _, sp1 = run_LIF(pars, pars['g_L'] * I1gL)\n",
    "    _, sp2 = run_LIF(pars, pars['g_L'] * I2gL)\n",
    "\n",
    "    my_bin = np.arange(0, pars['T'], bin_size)\n",
    "\n",
    "    sp1_count, _ = np.histogram(sp1, bins=my_bin)\n",
    "    sp2_count, _ = np.histogram(sp2, bins=my_bin)\n",
    "\n",
    "    r12[i_trial] = my_CC(sp1_count[::20], sp2_count[::20])\n",
    "    sp_rate[i_trial] = len(sp1) / pars['T'] * 1000.\n",
    "\n",
    "  return r12.mean(), sp_rate.mean(), sp1, sp2\n",
    "\n",
    "\n",
    "def plot_c_r_LIF(c, r, mycolor, mylabel):\n",
    "  z = np.polyfit(c, r, deg=1)\n",
    "  c_range = np.array([c.min() - 0.05, c.max() + 0.05])\n",
    "  plt.plot(c, r, 'o', color=mycolor, alpha=0.7, label=mylabel, zorder=2)\n",
    "  plt.plot(c_range, z[0] * c_range + z[1], color=mycolor, zorder=1)\n",
    "\n",
    "\n",
    "print(help(LIF_output_cc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VpDGQSuQmUR0"
   },
   "source": [
    "## Drive a neuron with correlated inputs and visualize its output\n",
    "In the following, you will inject correlated GWN in two neurons. You need to define the mean (`gwn_mean`), standard deviation (`gwn_std`), and input correlations (`c_in`).\n",
    "\n",
    "We will simulate $10$ trials to get a better estimate of the output correlation. Change the values for the above variables to explore how they impact the output correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "TzWrUz1tmUmc",
    "outputId": "57e7f681-1308-4c05-9c87-cc410fb7cc32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation time = 0.05 min\n",
      "Input correlation = 0.3\n",
      "Output correlation = 0.12221385653086354\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 414,
       "width": 845
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# @title\n",
    "\n",
    "# @markdown Do not forget to execute this cell to simulate the LIF\n",
    "\n",
    "pars = default_pars(T=80000, dt=1.)  # get the parameters\n",
    "c_in = 0.3  # set input correlation value\n",
    "gwn_mean = 10.\n",
    "gwn_std = 10.\n",
    "\n",
    "bin_size = 10.  # ms\n",
    "\n",
    "starttime = time.perf_counter()  # time clock\n",
    "\n",
    "r12_ss, sp_ss, sp1, sp2 = LIF_output_cc(pars, mu=gwn_mean, sig=gwn_std, c=c_in,\n",
    "                                        bin_size=bin_size, n_trials=10)\n",
    "\n",
    "# just the time counter\n",
    "endtime = time.perf_counter()\n",
    "timecost = (endtime - starttime) / 60.\n",
    "print(f\"Simulation time = {timecost:.2f} min\")\n",
    "\n",
    "print(f\"Input correlation = {c_in}\")\n",
    "print(f\"Output correlation = {r12_ss}\")\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(sp1, np.ones(len(sp1)) * 1, '|', ms=20, label='neuron 1')\n",
    "plt.plot(sp2, np.ones(len(sp2)) * 1.1, '|', ms=20, label='neuron 2')\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('neuron id.')\n",
    "plt.xlim(1000, 8000)\n",
    "plt.ylim(0.9, 1.2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nKQVWR60qqdi"
   },
   "source": [
    "## Think!\n",
    "- Is the output correlation always smaller than the input correlation? If yes, why?\n",
    "- Should there be a systematic relationship between input and output correlations? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3zpGLfwgqXgq"
   },
   "source": [
    "Lets vary `c_in` and plot the relationship between the `c_in` and output correlation. This might take some time depending on the number of trials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "colab_type": "code",
    "id": "-27REsp8rGy5",
    "outputId": "26913956-1bdc-4175-d4b3-57d505aff9e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation time = 0.47 min\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 486
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title\n",
    "\n",
    "#@markdown Don't forget to execute this cell!\n",
    "\n",
    "pars = default_pars(T=80000, dt=1.)  # get the parameters\n",
    "bin_size = 10.\n",
    "c_in = np.arange(0, 1.0, 0.1)  # set the range for input CC\n",
    "r12_ss = np.zeros(len(c_in))  # small mu, small sigma\n",
    "\n",
    "starttime = time.perf_counter() # time clock\n",
    "for ic in range(len(c_in)):\n",
    "  r12_ss[ic], sp_ss, sp1, sp2 = LIF_output_cc(pars, mu=10.0, sig=10.,\n",
    "                                              c=c_in[ic], bin_size=bin_size,\n",
    "                                              n_trials=10)\n",
    "\n",
    "endtime = time.perf_counter()\n",
    "timecost = (endtime - starttime) / 60.\n",
    "print(f\"Simulation time = {timecost:.2f} min\")\n",
    "\n",
    "plt.figure(figsize=(7, 6))\n",
    "plot_c_r_LIF(c_in, r12_ss, mycolor='b', mylabel='Output CC')\n",
    "plt.plot([c_in.min() - 0.05, c_in.max() + 0.05], \n",
    "         [c_in.min() - 0.05, c_in.max() + 0.05], \n",
    "         'k--', dashes=(2, 2), label='y=x')\n",
    "\n",
    "plt.xlabel('Input CC')\n",
    "plt.ylabel('Output CC')\n",
    "plt.legend(loc='best', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i4CqOWkIMAJ-"
   },
   "source": [
    "---\n",
    "# Section 3: Correlation transfer function\n",
    "The above plot of input correlation vs. output correlation is called the __correlation transfer function__ of the neurons. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YEB9LWD0smps"
   },
   "source": [
    "## Section 3.1: How do the mean and standard deviation of the GWN affect the correlation transfer function?\n",
    "\n",
    "The correlations transfer function appears to be linear. The above can be taken as the input/output transfer function of LIF neurons for correlations, instead of the transfer function for input/output firing rates as we had discussed in the previous tutorial (i.e., F-I curve).\n",
    "\n",
    "What would you expect to happen to the slope of the correlation transfer function if you vary the mean and/or the standard deviation of the GWN?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "colab_type": "code",
    "id": "kH9WSUy9dvUf",
    "outputId": "c53205b6-b83f-48c5-8210-bfedc6f3e995"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation time = 0.73 min\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 485
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pars = default_pars(T=80000, dt=1.) # get the parameters\n",
    "no_trial = 10\n",
    "bin_size = 10.\n",
    "c_in = np.arange(0., 1., 0.2)  # set the range for input CC\n",
    "r12_ss = np.zeros(len(c_in))   # small mu, small sigma\n",
    "r12_ls = np.zeros(len(c_in))   # large mu, small sigma\n",
    "r12_sl = np.zeros(len(c_in))   # small mu, large sigma\n",
    "\n",
    "starttime = time.perf_counter()  # time clock\n",
    "for ic in range(len(c_in)):\n",
    "  r12_ss[ic], sp_ss, sp1, sp2 = LIF_output_cc(pars, mu=10.0, sig=10.,\n",
    "                                              c=c_in[ic], bin_size=bin_size,\n",
    "                                              n_trials=no_trial)\n",
    "  r12_ls[ic], sp_ls, sp1, sp2 = LIF_output_cc(pars, mu=18.0, sig=10.,\n",
    "                                              c=c_in[ic], bin_size=bin_size,\n",
    "                                              n_trials=no_trial)\n",
    "  r12_sl[ic], sp_sl, sp1, sp2 = LIF_output_cc(pars, mu=10.0, sig=20.,\n",
    "                                              c=c_in[ic], bin_size=bin_size,\n",
    "                                              n_trials=no_trial)\n",
    "endtime = time.perf_counter()\n",
    "timecost = (endtime - starttime) / 60.\n",
    "print(f\"Simulation time = {timecost:.2f} min\")\n",
    "\n",
    "plt.figure(figsize=(7, 6))\n",
    "plot_c_r_LIF(c_in, r12_ss, mycolor='b', mylabel=r'Small $\\mu$, small $\\sigma$')\n",
    "plot_c_r_LIF(c_in, r12_ls, mycolor='y', mylabel=r'Large $\\mu$, small $\\sigma$')\n",
    "plot_c_r_LIF(c_in, r12_sl, mycolor='r', mylabel=r'Small $\\mu$, large $\\sigma$')\n",
    "plt.plot([c_in.min() - 0.05, c_in.max() + 0.05],\n",
    "         [c_in.min() - 0.05, c_in.max() + 0.05],\n",
    "         'k--', dashes=(2, 2), label='y=x')\n",
    "plt.xlabel('Input CC')\n",
    "plt.ylabel('Output CC')\n",
    "plt.legend(loc='best', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jrzxU3RULDDI"
   },
   "source": [
    "### Think!\n",
    "Why do both the mean and the standard deviation of the GWN affect the slope of the correlation transfer function? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "67gsO37w-8Qj"
   },
   "source": [
    "## Section 3.2: What is the rationale behind varying $\\mu$ and $\\sigma$?\n",
    "The mean and the variance of the synaptic current depends on the spike rate of a Poisson process. We can use [Campbell's theorem](https://en.wikipedia.org/wiki/Campbell%27s_theorem_(probability)) to estimate the mean and the variance of the synaptic current:\n",
    "\n",
    "\\begin{align}\n",
    "\\mu_{\\rm syn} = \\lambda J \\int P(t) \\\\\n",
    "\\sigma_{\\rm syn} = \\lambda J \\int P(t)^2 dt\\\\\n",
    "\\end{align}\n",
    "\n",
    "where $\\lambda$ is the firing rate of the Poisson input, $J$ the amplitude of the postsynaptic current and $P(t)$ is the shape of the postsynaptic current as a function of time. \n",
    "\n",
    "Therefore, when we varied $\\mu$ and/or $\\sigma$ of the GWN, we mimicked a change in the input firing rate. Note that, if we change the firing rate, both $\\mu$ and $\\sigma$ will change simultaneously, not independently. \n",
    "\n",
    "Here, since we observe an effect of $\\mu$ and $\\sigma$ on correlation transfer, this implies that the input rate has an impact on the correlation transfer function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3_dyceUAEzh8"
   },
   "source": [
    "### Think!\n",
    "- What are the factors that would make output correlations smaller than input correlations? (Notice that the colored lines are below the black dashed line)\n",
    "- What does it mean for the correlation in the network?\n",
    "- What do you think will happen if you use a wider range of input correlations?\n",
    "- Here we have studied the transfer of correlations by injecting GWN. But in the previous tutorial, we mentioned that GWN is unphysiological. Indeed, neurons receive colored noise (i.e., Shot noise or OU process). How do these results obtained from injection of GWN apply to the case where correlated spiking inputs are injected in the two LIFs? Will the results be the same or different? \n",
    "\n",
    "_Here is a reference [De La Rocha, Jaime, et al. \"Correlation between neural spike trains increases with firing rate.\" Nature  (2007)](https://www.nature.com/articles/nature06028/)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LJfdR6Xu7Vyi"
   },
   "source": [
    "---\n",
    "# Summary\n",
    "\n",
    "In this tutorial, we studied how the input correlation of two LIF neurons is mapped to their output correlation. Specifically, we:\n",
    "\n",
    "- injected correlated GWN in a pair of neurons,\n",
    "\n",
    "- measured correlations between the spiking activity of the two neurons, and\n",
    "\n",
    "- studied how the transfer of correlation depends on the statistics of the input, i.e., mean and standard deviation.\n",
    "\n",
    "Here, we were concerned with zero time lag correlation. For this reason, we restricted estimation of correlation to instantaneous correlations. If you are interested in time-lagged correlation, then we should estimate the cross-correlogram of the spike trains and find out the dominant peak and area under the peak to get an estimate of output correlations. \n",
    "\n",
    "We leave this as a future to-do for you if you are interested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6IdP4bt2NpB2"
   },
   "source": [
    "---\n",
    "# Bonus 1: Example of a conductance-based LIF model\n",
    "Above, we have written code to generate correlated Poisson spike trains. You can write code to stimulate the LIF neuron with such correlated spike trains and study the correlation transfer function for spiking input and compare it to the correlation transfer function obtained by injecting correlated GWNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "colab_type": "code",
    "id": "z7-8LaIWnAKY",
    "outputId": "883aad29-31c6-4caf-b329-6a62c906d3af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function run_LIF_cond in module __main__:\n",
      "\n",
      "run_LIF_cond(pars, I_inj, pre_spike_train_ex, pre_spike_train_in)\n",
      "    conductance-based LIF dynamics\n",
      "    \n",
      "    Args:\n",
      "      pars               : parameter dictionary\n",
      "      I_inj              : injected current [pA]. The injected current here can\n",
      "                           be a value or an array\n",
      "      pre_spike_train_ex : spike train input from presynaptic excitatory neuron\n",
      "      pre_spike_train_in : spike train input from presynaptic inhibitory neuron\n",
      "    \n",
      "    Returns:\n",
      "      rec_spikes         : spike times\n",
      "      rec_v              : mebrane potential\n",
      "      gE                 : postsynaptic excitatory conductance\n",
      "      gI                 : postsynaptic inhibitory conductance\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# @title Function to simulate conductance-based LIF\n",
    "\n",
    "def run_LIF_cond(pars, I_inj, pre_spike_train_ex, pre_spike_train_in):\n",
    "  \"\"\"\n",
    "  conductance-based LIF dynamics\n",
    "\n",
    "  Args:\n",
    "    pars               : parameter dictionary\n",
    "    I_inj              : injected current [pA]. The injected current here can\n",
    "                         be a value or an array\n",
    "    pre_spike_train_ex : spike train input from presynaptic excitatory neuron\n",
    "    pre_spike_train_in : spike train input from presynaptic inhibitory neuron\n",
    "\n",
    "  Returns:\n",
    "    rec_spikes         : spike times\n",
    "    rec_v              : mebrane potential\n",
    "    gE                 : postsynaptic excitatory conductance\n",
    "    gI                 : postsynaptic inhibitory conductance\n",
    "  \"\"\"\n",
    "\n",
    "  # Retrieve parameters\n",
    "  V_th, V_reset = pars['V_th'], pars['V_reset']\n",
    "  tau_m, g_L = pars['tau_m'], pars['g_L']\n",
    "  V_init, E_L = pars['V_init'], pars['E_L']\n",
    "  gE_bar, gI_bar = pars['gE_bar'], pars['gI_bar']\n",
    "  VE, VI = pars['VE'], pars['VI']\n",
    "  tau_syn_E, tau_syn_I = pars['tau_syn_E'], pars['tau_syn_I']\n",
    "  tref = pars['tref']\n",
    "  dt, range_t = pars['dt'], pars['range_t']\n",
    "  Lt = range_t.size\n",
    "\n",
    "  # Initialize\n",
    "  tr = 0.\n",
    "  v = np.zeros(Lt)\n",
    "  v[0] = V_init\n",
    "  gE = np.zeros(Lt)\n",
    "  gI = np.zeros(Lt)\n",
    "  Iinj = I_inj * np.ones(Lt)  # ensure I has length Lt\n",
    "\n",
    "  if pre_spike_train_ex.max() == 0:\n",
    "    pre_spike_train_ex_total = np.zeros(Lt)\n",
    "  else:\n",
    "    pre_spike_train_ex_total = pre_spike_train_ex * np.ones(Lt)\n",
    "\n",
    "  if pre_spike_train_in.max() == 0:\n",
    "    pre_spike_train_in_total = np.zeros(Lt)\n",
    "  else:\n",
    "    pre_spike_train_in_total = pre_spike_train_in * np.ones(Lt)\n",
    "\n",
    "  # simulation\n",
    "  rec_spikes = []  # recording spike times\n",
    "  for it in range(Lt - 1):\n",
    "    if tr > 0:\n",
    "      v[it] = V_reset\n",
    "      tr = tr - 1\n",
    "    elif v[it] >= V_th:   # reset voltage and record spike event\n",
    "      rec_spikes.append(it)\n",
    "      v[it] = V_reset\n",
    "      tr = tref / dt\n",
    "    # update the synaptic conductance\n",
    "    gE[it+1] = gE[it] - (dt / tau_syn_E) * gE[it] + gE_bar * pre_spike_train_ex_total[it + 1]\n",
    "    gI[it+1] = gI[it] - (dt / tau_syn_I) * gI[it] + gI_bar * pre_spike_train_in_total[it + 1]\n",
    "\n",
    "    # calculate the increment of the membrane potential\n",
    "    dv = (-(v[it] - E_L) - (gE[it + 1] / g_L) * (v[it] - VE) - \\\n",
    "            (gI[it + 1] / g_L) * (v[it] - VI) + Iinj[it] / g_L) * (dt / tau_m)\n",
    "\n",
    "    # update membrane potential\n",
    "    v[it + 1] = v[it] + dv\n",
    "\n",
    "  rec_spikes = np.array(rec_spikes) * dt\n",
    "\n",
    "  return v, rec_spikes, gE, gI\n",
    "\n",
    "\n",
    "print(help(run_LIF_cond))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DHs35UCoRpG4"
   },
   "source": [
    "## Interactive Demo: Correlated spike input to an LIF neuron \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 633,
     "referenced_widgets": [
      "5d5d4fc437de4a999df6388ff6bacc4e",
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      "d3d7b7abb4ae48a19ba16debb84c9f37",
      "036701913895456fb6aaf2314a794662",
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      "0af571745b1c44088d81b093ea3952b7",
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      "8085aaa9559c4618a48b24e616721b0a",
      "428a2c6674714cd0bdcce9e9df6f40f9",
      "02641ee59d9143e5b4875cfa0c7729cb"
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    },
    "colab_type": "code",
    "id": "bksg7PSWnGWV",
    "outputId": "8d74fea0-ae20-460e-fa20-ca36fc55d1aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input correlation = 0.3\n",
      "Output correlation = 0.022883216558263017\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 485,
       "width": 989
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# @title\n",
    "\n",
    "# @markdown Make sure you execute this cell to enable the widget!\n",
    "\n",
    "my_layout.width = '450px'\n",
    "@widgets.interact(\n",
    "  pwc_ee=widgets.FloatSlider(0.3, min=0.05, max=0.99, step=0.01,\n",
    "                             layout=my_layout),\n",
    "  exc_rate=widgets.FloatSlider(1e3, min=500., max=5e3, step=50.,\n",
    "                               layout=my_layout),\n",
    "  inh_rate=widgets.FloatSlider(500., min=300., max=5e3, step=5.,\n",
    "                               layout=my_layout),\n",
    ")\n",
    "\n",
    "\n",
    "def EI_isi_regularity(pwc_ee, exc_rate, inh_rate):\n",
    "  pars = default_pars(T=1000.)\n",
    "  # Add parameters\n",
    "  pars['V_th'] = -55.     # spike threshold [mV]\n",
    "  pars['V_reset'] = -75.  # reset potential [mV]\n",
    "  pars['tau_m'] = 10.     # membrane time constant [ms]\n",
    "  pars['g_L'] = 10.       # leak conductance [nS]\n",
    "  pars['V_init'] = -65.   # initial potential [mV]\n",
    "  pars['E_L'] = -75.      # leak reversal potential [mV]\n",
    "  pars['tref'] = 2.       # refractory time (ms)\n",
    "\n",
    "  pars['gE_bar'] = 4.0    # [nS]\n",
    "  pars['VE'] = 0.         # [mV] excitatory reversal potential\n",
    "  pars['tau_syn_E'] = 2.  # [ms]\n",
    "  pars['gI_bar'] = 2.4    # [nS]\n",
    "  pars['VI'] = -80.       # [mV] inhibitory reversal potential\n",
    "  pars['tau_syn_I'] = 5.  # [ms]\n",
    "\n",
    "  my_bin = np.arange(0, pars['T']+pars['dt'], .1)  # 20 [ms] bin-size\n",
    "\n",
    "  exc_rate = 1e3\n",
    "  inh_rate = 0.4e3\n",
    "  pwc_ee = 0.3\n",
    "  pwc_ii = 0.01\n",
    "  # generate two correlated spike trains for excitatory input\n",
    "  sp1e, sp2e = generate_corr_Poisson(pars, exc_rate, pwc_ee)\n",
    "\n",
    "  sp1_spike_train_ex, _ = np.histogram(sp1e, bins=my_bin)\n",
    "  sp2_spike_train_ex, _ = np.histogram(sp2e, bins=my_bin)\n",
    "\n",
    "  # generate two uncorrelated spike trains for inhibitory input\n",
    "  sp1i, sp2i = generate_corr_Poisson(pars, inh_rate, pwc_ii)\n",
    "  sp1_spike_train_in, _ = np.histogram(sp1e, bins=my_bin)\n",
    "  sp2_spike_train_in, _ = np.histogram(sp2e, bins=my_bin)\n",
    "\n",
    "  v1, rec_spikes1, gE, gI = run_LIF_cond(pars, 0, sp1_spike_train_ex, sp1_spike_train_in)\n",
    "  v2, rec_spikes2, gE, gI = run_LIF_cond(pars, 0, sp2_spike_train_ex, sp2_spike_train_in)\n",
    "\n",
    "  # bin the spike time\n",
    "  bin_size = 20  # [ms]\n",
    "  my_bin = np.arange(0, pars['T'], bin_size)\n",
    "  spk_1, _ = np.histogram(rec_spikes1, bins=my_bin)\n",
    "  spk_2, _ = np.histogram(rec_spikes2, bins=my_bin)\n",
    "\n",
    "  r12 = my_CC(spk_1, spk_2)\n",
    "  print(f\"Input correlation = {pwc_ee}\")\n",
    "  print(f\"Output correlation = {r12}\")\n",
    "\n",
    "  plt.figure(figsize=(14, 7))\n",
    "  plt.subplot(211)\n",
    "  plt.plot(sp1e, np.ones(len(sp1e)) * 1, '|', ms=20,\n",
    "           label='Exc. input 1')\n",
    "  plt.plot(sp2e, np.ones(len(sp2e)) * 1.1, '|', ms=20,\n",
    "           label='Exc. input 2')\n",
    "  plt.plot(sp1e, np.ones(len(sp1e)) * 1.3, '|k', ms=20,\n",
    "           label='Inh. input 1')\n",
    "  plt.plot(sp2e, np.ones(len(sp2e)) * 1.4, '|k', ms=20,\n",
    "           label='Inh. input 2')\n",
    "\n",
    "  plt.ylim(0.9, 1.5)\n",
    "  plt.legend()\n",
    "  plt.ylabel('neuron id.')\n",
    "\n",
    "  plt.subplot(212)\n",
    "  plt.plot(pars['range_t'], v1, label='neuron 1')\n",
    "  plt.plot(pars['range_t'], v2, label='neuron 2')\n",
    "  plt.xlabel('time (ms)')\n",
    "  plt.ylabel('membrane voltage $V_{m}$')\n",
    "  plt.tight_layout()\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z7TXjCQa7CR7"
   },
   "source": [
    "\n",
    "Above, we are estimating the output correlation for one trial. You can modify the code to get a trial average of output correlations.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tbhWzUDm5hiY"
   },
   "source": [
    "---\n",
    "# Bonus 2: Ensemble Response\n",
    "\n",
    "Finally, there is a short BONUS lecture video on the firing response of an ensemble of neurons to time-varying input. There are no associated coding exercises - just enjoy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 519
    },
    "colab_type": "code",
    "id": "xkrEq6Jzn-lc",
    "outputId": "f6267f0b-0cf7-44d6-85b9-a1fc46917ff4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Video available at https://youtube.com/watch?v=78_dWa4VOIo\n"
     ]
    },
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"854\"\n",
       "            height=\"480\"\n",
       "            src=\"https://www.youtube.com/embed/78_dWa4VOIo?fs=1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fbac1b3ce90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Video 2 (Bonus): Response of ensemble of neurons to time-varying input\n",
    "from IPython.display import YouTubeVideo\n",
    "video = YouTubeVideo(id=\"78_dWa4VOIo\", width=854, height=480, fs=1)\n",
    "print(\"Video available at https://youtube.com/watch?v=\" + video.id)\n",
    "video"
   ]
  }
 ],
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