{"cells":[{"cell_type":"markdown","metadata":{"colab_type":"text","id":"5Uu0L6pRg-JB"},"source":["# 2023 Oct 6th, Tutorial 1\n","\n","## Intro\n","\n","So today will be about the diversity of models and what different models can do for us. Typically everyone has an intuition about how different analysis tools can extract different kinds of information from experimental data; you choose a different analysis depending on your question. However, this is less appreciated for models. But it’s the same for them; you want to build a different kind of model to answer different kinds of questions. It all depends on your goals. So today, we will examine three kinds of models that we can classify as (according to Dayan and Abbott, 2001): **what**, **how**, and **why** models.\n","\n","Each tutorial will guide you through one of those models to describe the exact same data: the time interval between neuronal action potentials, aka inter-spike interval (ISI). In tutorial 1, we will ask what function best describes the shape of the ISI distribution (it’s an exponential distribution). Such a “what” model can compactly describe the ISI distribution and allows, for example, to quantify ISI properties across datasets, task conditions, brain areas etc. In tutorial 2, we ask which mechanism could generate the observed ISI distribution. Such a “how” model proposes a specific way that a system produces the observed behavior. Here, you will see that it’s a balance between excitation and inhibition that generates exponentially distributed ISIs. Finally we will ask “why” the exponential distribution is the most optimal way to code information in neurons. “Why” models thus ask about the underlying principles of a phenomenon.\n","\n","In any research, we typically start with descriptive (“what”) models; you will see examples of those during the model fitting, GLM, dimensionality reduction, and deep learning days. Next, we often ask about the mechanisms and build “how” models to generate or test hypotheses of underlying mechanisms; examples of those will be in linear systems, real neurons, dynamic networks, and decision making days. Ultimately, we are usually interested in the underlying reason of why the phenomenon exists in the first place; examples of those are in Bayes, optimal Control, and reinforcement learning days. “Why” models are often the hardest to achieve; “what” models are usually the easiest. But more importantly, they allow answering different questions, provide different insights and have different utilities. Thinking about the question I want to answer, why I want to answer this question (i.e. my goal) and the hypotheses I want to evaluate determines my own modeling choices every day. The resulting diversity in models is great because all models address different facets of a problem (like in today's 3 tutorials) and are thus complementary in our quest for knowledge. Today’s materials will hopefully allow you to better appreciate the opportunities and limitations offered by all the modeling tools you will learn during the course.\n","\n","\n","# Model Types: \"What\" models\n","\n","We would like to acknowledge [Steinmetz _et al._ (2019)](https://www.nature.com/articles/s41586-019-1787-x) for sharing their data, a subset of which is used here.\n"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"xizgwM0DPnFc"},"source":["___\n","# Tutorial Objectives\n","This is tutorial 1 of a 3-part series on different flavors of models used to understand neural data. In this tutorial we will explore 'What' models, used to describe the data. To understand what our data looks like, we will visualize it in different ways. Then we will compare it to simple mathematical models. Specifically, we will:\n","\n","- Load a dataset with spiking activity from hundreds of neurons and understand how it is organized\n","- Make plots to visualize characteristics of the spiking activity across the population\n","- Compute the distribution of \"inter-spike intervals\" (ISIs) for a single neuron\n","- Consider several formal models of this distribution's shape and fit them to the data \"by hand\""]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"YOCsVZYBhDMi"},"source":["# Setup\n","\n"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"D1Pt68_Lcsn8"},"source":["Python requires you to explictly \"import\" libraries before their functions are available to use. We will always specify our imports at the beginning of each notebook or script."]},{"cell_type":"code","execution_count":3,"metadata":{"cellView":"both","colab":{},"colab_type":"code","executionInfo":{"elapsed":676,"status":"ok","timestamp":1594628183320,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"83AqE2hlg9H-"},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"hkvzsPVCc63w"},"source":["Tutorial notebooks typically begin with several set-up steps that are hidden from view by default.\n","\n","**Important:** Even though the code is hidden, you still need to run it so that the rest of the notebook can work properly. Step through each cell, either by pressing the play button in the upper-left-hand corner or with a keyboard shortcut (`Cmd-Return` on a Mac, `Ctrl-Enter` otherwise). A number will appear inside the brackets (e.g. `[3]`) to tell you that the cell was executed and what order that happened in.\n","\n","If you are curious to see what is going on inside each cell, you can double click to expand. Once expanded, double-click the white space to the right of the editor to collapse again."]},{"cell_type":"code","execution_count":4,"metadata":{"cellView":"form","colab":{},"colab_type":"code","executionInfo":{"elapsed":481,"status":"ok","timestamp":1594628200906,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"FyJb8B5vFYND"},"outputs":[],"source":["#@title Figure Settings\n","import ipywidgets as widgets #interactive display\n","\n","%matplotlib inline\n","%config InlineBackend.figure_format = 'retina'\n"]},{"cell_type":"code","execution_count":5,"metadata":{"cellView":"form","colab":{},"colab_type":"code","executionInfo":{"elapsed":656,"status":"ok","timestamp":1594628209774,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"velBtnwIM0Dl"},"outputs":[],"source":["#@title Helper functions\n","\n","#@markdown Most of the tutorials make use of helper functions\n","#@markdown to simplify the code that you need to write. They are defined here.\n","\n","# Please don't edit these, or worry about understanding them now!\n","\n","def restrict_spike_times(spike_times, interval):\n","  \"\"\"Given a spike_time dataset, restrict to spikes within given interval.\n","\n","  Args:\n","    spike_times (sequence of np.ndarray): List or array of arrays,\n","      each inner array has spike times for a single neuron.\n","    interval (tuple): Min, max time values; keep min <= t < max.\n","\n","  Returns:\n","    np.ndarray: like `spike_times`, but only within `interval`\n","  \"\"\"\n","  interval_spike_times = []\n","  for spikes in spike_times:\n","    interval_mask = (spikes >= interval[0]) & (spikes < interval[1])\n","    interval_spike_times.append(spikes[interval_mask])\n","  return np.array(interval_spike_times, object)"]},{"cell_type":"code","execution_count":6,"metadata":{"cellView":"form","colab":{},"colab_type":"code","executionInfo":{"elapsed":2110,"status":"ok","timestamp":1594628213408,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"9O9uom_44lAZ"},"outputs":[],"source":["#@title Data retrieval\n","#@markdown This cell downloads the example dataset that we will use in this tutorial.\n","import io\n","import requests\n","r = requests.get('https://osf.io/sy5xt/download')\n","if r.status_code != 200:\n","  print('Failed to download data')\n","else:\n","  spike_times = np.load(io.BytesIO(r.content), allow_pickle=True)['spike_times']"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"E81PWKTGym7P"},"source":["---\n","\n","# Section 1: Exploring the Steinmetz dataset\n","\n","In this tutorial we will explore the structure of a neuroscience dataset. \n","\n","We consider a subset of data from a study of [Steinmetz _et al._ (2019)](https://www.nature.com/articles/s41586-019-1787-x). In this study, Neuropixels probes were implanted in the brains of mice. Electrical potentials were measured by hundreds of electrodes along the length of each probe. Each electrode's measurements captured local variations in the electric field due to nearby spiking neurons. A spike sorting algorithm was used to infer spike times and cluster spikes according to common origin: a single cluster of sorted spikes is causally attributed to a single neuron.\n","\n","In particular, a single recording session of spike times and neuron assignments was loaded and assigned to `spike_times` in the preceding setup. \n","\n","Typically a dataset comes with some information about its structure. However, this information may be incomplete. You might also apply some transformations or \"pre-processing\" to create a working representation of the data of interest, which might go partly undocumented depending on the circumstances. In any case it is important to be able to use the available tools to investigate unfamiliar aspects of a data structure. \n","\n","Let's see what our data looks like..."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"U3a_9c4sjQ7c"},"source":["## Section 1.1: Warming up with `spike_times`"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"lHvAgKuMJGt3"},"source":["What is the Python type of our variable?"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":689,"status":"ok","timestamp":1594628251186,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"WsZrKJUFZ38z","outputId":"d3533fbc-8df0-4aac-df1d-eb0db4d75c9e"},"outputs":[{"data":{"text/plain":["numpy.ndarray"]},"execution_count":7,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["type(spike_times)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"mdzX4_CUFp2U"},"source":["You should see `numpy.ndarray`, which means that it's a normal NumPy array.\n","\n","If you see an error message, it probably means that you did not execute the set-up cells at the top of the notebook. So go ahead and make sure to do that.\n","\n","Once everything is running properly, we can ask the next question about the dataset: what's its shape?"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":636,"status":"ok","timestamp":1594628267653,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"_dYl5pCrIlRa","outputId":"d0802e5e-adbf-4d6e-b5ea-34006a230107"},"outputs":[{"data":{"text/plain":["(734,)"]},"execution_count":8,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["spike_times.shape"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"B1LEO0d4Fz3f"},"source":["There are 734 entries in one dimension, and no other dimensions. What is the Python type of the first entry, and what is *its* shape?"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":51},"colab_type":"code","executionInfo":{"elapsed":625,"status":"ok","timestamp":1594628276434,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"VR63MAS91Dgn","outputId":"f07e4388-d238-4323-dd84-049dd8b457cc"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'numpy.ndarray'>\n","(826,)\n"]}],"source":["idx = 0\n","print(\n","  type(spike_times[idx]),\n","  spike_times[idx].shape,\n","  sep=\"\\n\",\n",")"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"SNNE2OqLGSDM"},"source":["It's also a NumPy array with a 1D shape! Why didn't this show up as a second dimension in the shape of `spike_times`? That is, why not `spike_times.shape == (734, 826)`?\n","\n","To investigate, let's check another entry."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":51},"colab_type":"code","executionInfo":{"elapsed":633,"status":"ok","timestamp":1594628328883,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"34VOEiufG-Ec","outputId":"8e3b8c8f-587a-4405-bf81-43b03ec68f14"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'numpy.ndarray'>\n","(9723,)\n"]}],"source":["idx = 321\n","print(\n","  type(spike_times[idx]),\n","  spike_times[idx].shape,\n","  sep=\"\\n\",\n",")"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"225jJ8LgaV5W"},"source":["It's also a 1D NumPy array, but it has a different shape. Checking the NumPy types of the values in these arrays, and their first few elements, we see they are composed of floating point numbers (not another level of `np.ndarray`):"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":187},"colab_type":"code","executionInfo":{"elapsed":605,"status":"ok","timestamp":1594628364623,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"C5tROGLzaqeI","outputId":"cacecfba-aeb9-4c4e-901f-b6f33b8c2a18"},"outputs":[{"name":"stdout","output_type":"stream","text":["Neuron 0:\n","float32\n","[ 0.8149   14.822467 24.9646   25.1436   38.8709  ]\n","\n","\n","Neuron 321:\n","float32\n","[1.0698667 1.1536334 1.2403667 1.7072    1.799    ]\n","\n","\n"]}],"source":["i_neurons = [0, 321]\n","i_print = slice(0, 5)\n","\n","for i in i_neurons:\n","  print(\n","    \"Neuron {}:\".format(i),\n","    spike_times[i].dtype,\n","    spike_times[i][i_print],\n","    \"\\n\",\n","    sep=\"\\n\"\n","  )\n"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"KOFA2ntcZBiy"},"source":["Note that this time we've checked the NumPy `dtype` rather than the Python variable type. These two arrays contain floating point numbers (\"floats\") with 32 bits of precision.\n","\n","The basic picture is coming together:\n","- `spike_times` is 1D, its entries are NumPy arrays, and its length is the number of neurons (734): by indexing it, we select a subset of neurons. \n","- An array in `spike_times` is also 1D and corresponds to a single neuron; its entries are floating point numbers, and its length is the number of spikes attributed to that neuron. By indexing it, we select a subset of spike times for that neuron. \n","\n","Visually, you can think of the data structure as looking something like this:\n","\n","```\n","| . . . . . |\n","| . . . . . . . . |\n","| . . . |\n","| . . . . . . . |\n","```\n","\n","Before moving on, we'll calculate and store the number of neurons in the dataset and the number of spikes per neuron:"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":51},"colab_type":"code","executionInfo":{"elapsed":648,"status":"ok","timestamp":1594628476074,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"98RflSLUuIdx","outputId":"9dc6bdef-7468-4895-cc86-63be1fcc5876"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of neurons: 734\n","Number of spikes for first five neurons: [826, 2818, 3953, 646, 1115]\n"]}],"source":["n_neurons = len(spike_times)\n","total_spikes_per_neuron = [len(spike_times_i) for spike_times_i in spike_times]\n","\n","print(f\"Number of neurons: {n_neurons}\")\n","print(f\"Number of spikes for first five neurons: {total_spikes_per_neuron[:5]}\")"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"239jckGLuHb5"},"source":["## Section 1.2: Getting warmer: counting and plotting total spike counts\n","\n","As we've seen, the number of spikes over the entire recording is variable between neurons. More generally, some neurons tend to spike more than others in a given period. Lets explore what the distribution of spiking looks like across all the neurons in the dataset."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"zblGrgIVQgLk"},"source":["Are most neurons \"loud\" or \"quiet\", compared to the average? To see, we'll define bins of constant width in terms of total spikes and count the neurons that fall in each bin. This is known as a \"histogram\".\n","\n","You can plot a histogram with the matplotlib function `plt.hist`. If you just need to compute it, you can use the numpy function `np.histogram` instead."]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":431},"colab_type":"code","executionInfo":{"elapsed":1204,"status":"ok","timestamp":1594628767669,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"jQtz2HtsEiwd","outputId":"170e5f5a-0265-49ad-d720-49d0db526c3f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"image/png":{"height":414,"width":558},"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["plt.hist(total_spikes_per_neuron, bins=50)\n","plt.xlabel(\"Total spikes per neuron\")\n","plt.ylabel(\"Number of neurons\");"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"vrp7PWtZeG-H"},"source":["Let's see what percentage of neurons have a below-average spike count:"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":794,"status":"ok","timestamp":1594628788818,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"yWcnId0_FHb2","outputId":"2b510319-6bf3-4b3f-fc55-4805db306835"},"outputs":[{"name":"stdout","output_type":"stream","text":["68.0% of neurons are below the mean\n"]}],"source":["mean_spike_count = np.mean(total_spikes_per_neuron)\n","frac_below_mean = (total_spikes_per_neuron < mean_spike_count).mean()\n","print(f\"{frac_below_mean:2.1%} of neurons are below the mean\")"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"_OH6T-ikImSJ"},"source":["We can also see this by adding the average spike count to the histogram plot:"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":431},"colab_type":"code","executionInfo":{"elapsed":980,"status":"ok","timestamp":1594628843796,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"r80VSqHuIx26","outputId":"0e496e00-6270-4b53-b47e-557e4a8e1ba3"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"image/png":{"height":414,"width":558},"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["plt.hist(total_spikes_per_neuron, bins=50)\n","plt.xlabel(\"Total spikes per neuron\")\n","plt.ylabel(\"Number of neurons\")\n","plt.axvline(mean_spike_count, color=\"orange\", label=\"Mean neuron\")\n","plt.legend();"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"qwcMvOddf8lm"},"source":["This shows that the majority of neurons are relatively \"quiet\" compared to the mean, while a small number of neurons are exceptionally \"loud\": they must have spiked more often to reach a large count.\n","\n","### Exercise 1: Comparing mean and median neurons\n","\n","If the mean neuron is more active than 68% of the population, what does that imply about the relationship between the mean neuron and the median neuron?\n","\n","*Exercise objective:* Reproduce the plot above, but add the median neuron.\n"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"colab_type":"code","executionInfo":{"elapsed":1359,"status":"ok","timestamp":1594628992922,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"xITqNSatw7pP","outputId":"7f407cbb-ffd4-4383-9ace-a0fd2f3183a3"},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x7f5c7697e5f8>"]},"execution_count":17,"metadata":{"tags":[]},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"image/png":{"height":414,"width":558},"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["# To complete the exercise, fill in the missing parts (...) and uncomment the code\n","\n","median_spike_count = np.median(total_spikes_per_neuron)  # Hint: Try the function np.median\n","\n","plt.hist(total_spikes_per_neuron, bins=50)\n","plt.axvline(median_spike_count, color=\"limegreen\", label=\"Median neuron\")\n","plt.axvline(mean_spike_count, color=\"orange\", label=\"Mean neuron\")\n","plt.xlabel(\"Total spikes per neuron\")\n","plt.ylabel(\"Number of neurons\")\n","plt.legend()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"dAilqW_Bxuk6"},"source":["\n","*Bonus:* The median is the 50th percentile. What about other percentiles? Can you show the interquartile range on the histogram?"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"qupPEXRjrsfi"},"source":["---\n","\n","# Section 2: Visualizing neuronal spiking activity"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"9ytWqlqIs95u"},"source":["## Section 2.1: Getting a subset of the data\n","\n","Now we'll visualize trains of spikes. Because the recordings are long, we will first define a short time interval and restrict the visualization to only the spikes in this interval. We defined a utility function, `restrict_spike_times`, to do this for you. If you call `help()` on the function, it will tell you a little bit about itself:"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"colab_type":"code","executionInfo":{"elapsed":624,"status":"ok","timestamp":1594629064223,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"F7EeTMtLguVy","outputId":"eaefd0be-70c2-4bda-e5c7-1dc732f0761b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Help on function restrict_spike_times in module __main__:\n","\n","restrict_spike_times(spike_times, interval)\n","    Given a spike_time dataset, restrict to spikes within given interval.\n","    \n","    Args:\n","      spike_times (sequence of np.ndarray): List or array of arrays,\n","        each inner array has spike times for a single neuron.\n","      interval (tuple): Min, max time values; keep min <= t < max.\n","    \n","    Returns:\n","      np.ndarray: like `spike_times`, but only within `interval`\n","\n"]}],"source":["help(restrict_spike_times)"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{},"colab_type":"code","executionInfo":{"elapsed":627,"status":"ok","timestamp":1594629068973,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"HHw20g3P4fCI"},"outputs":[],"source":["t_interval = (5, 15)  # units are seconds after start of recording\n","interval_spike_times = restrict_spike_times(spike_times, t_interval)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"gP_sc8L7zHqg"},"source":["Is this a representative interval? What fraction of the total spikes fall in this interval?"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":666,"status":"ok","timestamp":1594629077876,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"4bA_Twv5fV05","outputId":"8a9b2ee7-dfa6-4c5f-c433-65118d2f18d3"},"outputs":[{"name":"stdout","output_type":"stream","text":["0.33% of the total spikes are in the interval\n"]}],"source":["original_counts = sum([len(spikes) for spikes in spike_times])\n","interval_counts = sum([len(spikes) for spikes in interval_spike_times])\n","frac_interval_spikes = interval_counts / original_counts\n","print(f\"{frac_interval_spikes:.2%} of the total spikes are in the interval\")"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"HCMu5XGZf_Lk"},"source":["How does this compare to the ratio between the interval duration and the experiment duration? (What fraction of the total time is in this interval?)\n","\n","We can approximate the experiment duration by taking the minimum and maximum spike time in the whole dataset. To do that, we \"concatenate\" all of the neurons into one array and then use `np.ptp` (\"peak-to-peak\") to get the difference between the maximum and minimum value:"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":945,"status":"ok","timestamp":1594629171811,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"zgX397FNj9zG","outputId":"9341c125-6ce2-4b90-b2a8-0c786f1c1364"},"outputs":[{"name":"stdout","output_type":"stream","text":["0.37% of the total time is in the interval\n"]}],"source":["spike_times_flat = np.concatenate(spike_times)\n","experiment_duration = np.ptp(spike_times_flat)\n","interval_duration = t_interval[1] - t_interval[0]\n","\n","frac_interval_time = interval_duration / experiment_duration\n","print(f\"{frac_interval_time:.2%} of the total time is in the interval\")"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"iOiCg3lIY6JG"},"source":["These two values—the fraction of total spikes and the fraction of total time—are similar. This suggests the average spike rate of the neuronal population is not very different in this interval compared to the entire recording.\n","\n","## Section 2.2: Plotting spike trains and rasters\n","\n","Now that we have a representative subset, we're ready to plot the spikes, using the matplotlib `plt.eventplot` function. Let's look at a single neuron first:"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":431},"colab_type":"code","executionInfo":{"elapsed":599,"status":"ok","timestamp":1594629218543,"user":{"displayName":"Evgenii 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","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"image/png":{"height":414,"width":557},"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["neuron_idx = 1\n","plt.eventplot(interval_spike_times[neuron_idx], color=\".2\")\n","plt.xlabel(\"Time (s)\")\n","plt.yticks([]);"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{},"colab_type":"code","executionInfo":{"elapsed":668,"status":"ok","timestamp":1594630009878,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"BDdeZdi6Tt27"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"L282dtXQCO6w"},"source":["We can also plot multiple neurons. Here are three:"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"colab_type":"code","executionInfo":{"elapsed":871,"status":"ok","timestamp":1594630019736,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"m6c_Yd_7yEPp","outputId":"0b837732-0064-4571-b016-c25b4965689d"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"image/png":{"height":413,"width":557},"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["neuron_idx = [1, 11, 51]\n","plt.eventplot(interval_spike_times[neuron_idx], color=\".2\")\n","plt.xlabel(\"Time (s)\")\n","plt.yticks([]);"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"1NJdg_TjyB9_"},"source":["This makes a \"raster\" plot, where the spikes from each neuron appear in a different row.\n","\n","Plotting a large number of neurons can give you a sense for the characteristics in the population. Let's show every 5th neuron that was recorded:"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"colab_type":"code","executionInfo":{"elapsed":1277,"status":"ok","timestamp":1594630041645,"user":{"displayName":"Evgenii 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","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"image/png":{"height":413,"width":558},"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["neuron_idx = np.arange(0, len(spike_times), 5)\n","plt.eventplot(interval_spike_times[neuron_idx], color=\".2\")\n","plt.xlabel(\"Time (s)\")\n","plt.yticks([]);"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"YDWveM6NjaBG"},"source":["*Question*: How does the information in this plot relate to the histogram of total spike counts that you saw above?"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"KO4SaTXByZly"},"source":["---\n","\n","# Section 3: Inter-spike intervals and their distributions"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"Pks6reFdeatu"},"source":["Given the ordered arrays of spike times for each neuron in `spike_times`, which we've just visualized, what can we ask next? \n","\n","Scientific questions are informed by existing models. So, what knowledge do we already have that can inform questions about this data?\n","\n","We know that there are physical constraints on neuron spiking. Spiking costs energy, which the neuron's cellular machinery can only obtain at a finite rate. Therefore neurons should have a refractory period: they can only fire as quickly as their metabolic processes can support, and there is a minimum delay between consecutive spikes of the same neuron.\n","\n","More generally, we can ask \"how long does a neuron wait to spike again?\" or \"what is the longest a neuron will wait?\" Can we transform spike times into something else, to address questions like these more directly?\n","\n","We can consider the inter-spike times (or interspike intervals: ISIs). These are simply the time differences between consecutive spikes of the same neuron.\n","\n","### Exercise 2: Plot the distribution of ISIs for a single neuron\n","\n","*Exercise objective:* make a histogram, like we did for spike counts, to show the distribution of ISIs for one of the neurons in the dataset.\n","\n","Do this in three steps:\n","\n","1. Extract the spike times for one of the neurons\n","2. Compute the ISIs (the amount of time between spikes, or equivalently, the difference between adjacent spike times)\n","3. Plot a histogram with the array of individual ISIs"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"colab_type":"code","executionInfo":{"elapsed":1090,"status":"ok","timestamp":1594631317234,"user":{"displayName":"Evgenii 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kSZKkLTVoZ7iU2oFbgXOBI4El3S2MiL8CrqIIWy4B9szMUZk5oby2P/BvwPomtROA6yjCloeAvTNzHDAG+Nuy5l3A+S2ePQ2YRxG23AHsUT53AvDpstsxwGkt6velCIKGAtcCUzJzW2AH4OKy25yImN3dv4ckSZIkSeq+wRy4LMjM7TLznZl5emZeDaztTmFEvJwimNgGOCUzP5aZD9TaM3NFZi7IzJMzc1WTW5wK7AQ8CxySmXeXdesy80JgTtnvuIh4VZP6T1OEM0uAwzJzYVm/MjPnUARAAJ+MiLYm9V8AhgC/BmZn5qKy/unMPJ5i5g3AORExpDt/E0mSJEmS1H2DNnDJzA29KP97oA24l2IWy5b6cHm+OjMfbdL+FYpXjIYAR9U3RMQYoLZGy0WZ+YLXlYDPl+fxFK8I1dfvRvHKEsB5mfmCGTh19VMpZupIkiRJkjRotViNo08N2sCll2qBybdbrZHSSkTsQbG+C8CPmvXJzJXAgvLrwQ3N+wGjuqh/DHiwRf1BdZ9vbDHM24EVLeolSZIkDQK1fTo2btw4wCORBl7tf9p3Y/+aygzmRXN7pFw/ZVL59Z6IeC3wT8DbKRatfYpiXZUvZ+YdTW6xV93n+zt51P3ALOA1vaifDuzZon5pZi5tVpiZGyLiIWDvJvVNRcQ93ej26u7cS5IkSVLfGzZsGOvWrWPNmjWMHj266wJpEFuzZg1Q/LvoL85weaH6NVXeAtxNseDuBIpdiiYDs4EFEfEvTeon1X1+opPn1NrGR8TYJvXtmflsN+onNVyf1NC+pfWSJEmSBoFx48YB0N7ePiCvU0gvFplJe3s7sPnfRX9whssL1S9C+xngD8DfAD/JzI0R8RrgQuBtwKcj4jeZOb+upv6/3upOnlPfNo5iTZf6+s5q69sb/6+lt/VNZebMrvqUs2BmdOd+kiRJkvrW+PHjWbZsGR0dHQC0tbUxcuRIIqJfX6uQBkJmkpmsWbOG9vZ2Ojo6iAgmTJjQb2MwcHmh+lk/AbwvM39Zu5CZD0TEXwC/o9iJaA4wH0mSJEl6ERk5ciQ777wzixYtoqOjY1PwIr0URQQ777wzI0aM6Ldn+krRC62o+3xrfdhSUy56e2H59XURsWOL+s5elKxvW9Hkc1cvWdbaVzRc7229JEmSpEFi7NixTJs2jZe97GUMHz7cmS16SYkIhg8fzste9jKmTZvG2LFjuy6qkDNcXqh+7ZMHW/aCB+o+7wo8WX5eXHd9MtAqRp5cnjvKAKemVt8WEaM6WcdlckP/xvrJdK5VvSRJkqRBZMSIEUycOJGJEycO9FCklxRnuLzQA8CGbvSrj4brV6Cq31mofsehRrW2Bxqub2n9b1rUT4yIHZoVRsQQNu8o1FgvSZIkSZJ6ycClQWauAX5efp3eSdfads4JPFZ3fSHFQrsAf96sMCLGAG8tv97c0Hw7UJvV0qp+17qxNdb/uO5z03qK3Zdqi+U21kuSJEmSpF4ycGnu8vJ8YES8YNedchvnj5dff5GZT9Xasthv7Yry6wciYmqT+58IjKWYSXNlfUNmrgK+V349ISKaLaH8ifK8Ari2of4RitAG4JSIaLbJ+Bnl+XE2h0uSJEmSJKkigzpwiYi2iNi+drD5946uv14GKPWuBP6b4rWh70XEgRGxTXnP6cB/UuxQtBH4ZJNHnwcsoViY9vqImFnWDo+IEyi2mwa4JDMXNqk/E1gFvBz4YUS8sqwfExFnAseX/T6bme1N6j9BEea8Hrg6IiaX9dtFxFeBWWW/0zOzO69PSZIkSZKkLTDYF829l2JB20anlUfNt4Cja18yc2NEvAe4leLVoVuA1RGxHqjNOFkPnJiZP2m8eWYuj4jDgJvK+rsjYgUwEqjNOLkZOKnZoDPz0YiYDVxD8erRwohYTjErZkjZ7XLg3Bb1d0bE8cBFwF8CfxkRz5Rjr609c1ZmzmtWL0mSJEmSemdQz3DpjcxcAswATgX+hyJgGUWxXstlwIzM/Hon9fcAewL/BvyOImhZRfG6z98AszJzbSf1NwCvA75ePnMk0E6xRssRmfnR8vWlVvWXAm8CrqLYeWk0sJTiFaQDM3NuF38CSZIkSZLUQ4N6hktmTu1l/Vrgi+XRk/ongZPLoyf1DwPH9aS2rP8lcFRP6yVJkiRJUs84w0WSJEmSJKliBi6SJEmSJEkVM3CRJEmSJEmqmIGLJEmSJElSxQxcJEmSJEmSKmbgIkmSJEmSVDEDF0mSJEmSpIoZuEiSJEmSJFXMwEWSJEmSJKliBi6SJEmSJEkVM3CRJEmSJEmqmIGLJEmSJElSxQxcJEmSJEmSKmbgIkmSJEmSVDEDF0mSJEmSpIoZuEiSJEmSJFVs6EAPQFI1pp5xfSX3eezsQyu5jyRJkiS9lDnDRZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkig0d6AFIenGZesb1vb7HY2cfWsFIJEmSJGnr5QwXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqNmgDl4gYHRGzIuJTETE/Ih6PiCyPuT2859fq7vFYN/rvGBFfjIjfRsSzEbEsIhZExLEREd2o3z0iLo6IRyNiTUQ8FRE3RcT7ujneGRHx7YhYFBFrI+KPEfH9iHhHd+olSZIkSVLPDB3oAfShfYAbqrpZRLwdOG4L+s8EbgJeVl5aCYwD9iuPIyLi3Zm5rkX9IcA1wOjyUgewHXAwcHBEXA78dWZmi/pjgYvY/N94ObAjcDhweESclZlzu/t7JEmSJElS9w3aGS6lduBW4FzgSGBJT24SEaOBrwPPAXd3o/8E4DqKsOUhYO/MHAeMAf4WWA+8Czi/Rf00YB5F2HIHsEdmTgAmAJ8uux0DnNaifl/gaxRhy7XAlMzcFtgBuLjsNiciZnf1WyRJkiRJ0pYbzIHLgszcLjPfmZmnZ+bVwNoe3utzwO7AF4DfdKP/qcBOwLPAIZl5N0BmrsvMC4E5Zb/jIuJVTeo/TRHOLAEOy8yFZf3KzJwDXFL2+2REtDWp/wIwBPg1MDszF5X1T2fm8RQzbwDOiYgh3fg9kiRJkiRpCwzawCUzN1Rxn4j4M+DvgYXAZ7tZ9uHyfHVmPtqk/SsUrxgNAY5qeN4YoLZGy0WZ+UyT+s+X5/EUrwjV1+9G8coSwHmZub6T+qnA/q1/hiRJkiRJ6olBG7hUISJGAJcBARyXmWu6UbMHsEv59UfN+mTmSmBB+fXghub9gFFd1D8GPNii/qC6zze2GObtwIoW9ZIkSZIkqZcMXDp3JjAd+EZm/qybNXvVfb6/k361ttf0sn7PFvVLM3Nps8Jy9s9DLeolSZIkSVIvDeZdinolIv4fcDrwJC0Wp21hUt3nJzrpV2sbHxFjy1kv9fXtmflsN+onNVyf1NDeWf3eTeqbioh7utHt1d25lyRJkiRJg50zXJqIiKEUrxINBf6+xToqrYyr+7y6k371beOafO6str59XMP13tZLkiRJkqRecoZLc2cAbwCuy8x5Az2YF4PMnNlVn3IWzIx+GI4kSZIkSS9qznBpEBGvAf6FYhehj/fgFivqPo/upF9924omnzurrW9f0XC9t/WSJEmSJKmXnOHyQhcCw4E5QHtEjG1or/3Noq5tbd32y4vr+k4GOlo8Z3J57qhbv6W+vi0iRnWyjsvkhv6N9ZPpXKt6SZIkSZLUS85weaFp5fnzFLM/Go+jyvZd6q6dWFdfv7NQ/Y5DjWptDzRc39L637SonxgROzQrjIghbF7gtrFekiRJkiT1koFL9RYCfyg//3mzDhExBnhr+fXmhubbgdqsllb1u1JsV92s/sd1n5vWA29h82K5jfWSJEmSJKmXDFwaZObUzIxWB/CtsuvjddfPr6tP4Iry6wciYmqTx5wIjAU2AFc2PH8V8L3y6wkRMaFJ/SfK8wrg2ob6RyhCG4BTImJYk/ozar8B+HmTdkmSJEmS1AuDOnCJiLaI2L52sPn3jq6/3mSdlt46D1hCsTDt9RExsxzP8Ig4AfhM2e+SzFzYpP5MYBXwcuCHEfHKsn5MRJwJHF/2+2xmtjep/wRFmPN64OqImFzWbxcRXwVmlf1Oz8wNvfytkiRJkiSpwaAOXIB7gafqjinl9dMarl9Q5UMzczlwGPA08Brg7ojooNj56KsUi/LeDJzUov5RYDawmuLVo4UR8QywHDgLCOBy4NwW9XdShDLPAX8JLIqIduBPwAllt7Pc8lqSJEmSpL4x2AOXAZOZ9wB7Av8G/A4YRjFr5Xbgb4BZmbm2k/obgNcBXwceA0YC7RRrtByRmR8tX19qVX8p8CbgKuAJitk2SyleQTowM+f27hdKkiRJkqRWBvW20Jk5tQ/ueTRwdDf7PgmcXB49edbDwHE9qS3rf8nmXZUkSZIkSVI/cYaLJEmSJElSxQxcJEmSJEmSKmbgIkmSJEmSVDEDF0mSJEmSpIoZuEiSJEmSJFXMwEWSJEmSJKliBi6SJEmSJEkVM3CRJEmSJEmqmIGLJEmSJElSxQxcJEmSJEmSKmbgIkmSJEmSVDEDF0mSJEmSpIoZuEiSJEmSJFXMwEWSJEmSJKliBi6SJEmSJEkVM3CRJEmSJEmqmIGLJEmSJElSxQxcJEmSJEmSKjZ0oAcgbe2mnnH9QA9BkiRJkvQi4wwXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqNnSgByBp8Jl6xvVd9nnsdZ33f+zsQ6sckiRJkiT1K2e4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqZuAiSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVczARZIkSZIkqWIGLpIkSZIkSRUzcJEkSZIkSaqYgYskSZIkSVLFDFwkSZIkSZIqNmgDl4gYHRGzIuJTETE/Ih6PiCyPuV3UTo6Ij0fENRHx+4h4tjwejYjvRMQ7ujmGHSPiixHx27J+WUQsiIhjIyK6Ub97RFxcPndNRDwVETdFxPu6+fwZEfHtiFgUEWsj4o8R8f3ujl+SJEmSJPXM0IEeQB/aB7hhS4siYgrwOFAfiKwuv08tjw9ExGXAcZm5ocV9ZgI3AS8rL60ExgH7lccREfHuzFzXov4Q4BpgdHmpA9gOOBg4OCIuB/46M7NF/bHARWz+b7wc2BE4HDg8Is7KzLkt/xCSJEmSJKnHBu0Ml1I7cCtwLnAksKQbNUMowpVbgY8AkzNzDDAW2BP4Qdnvo8DcZjeIiAnAdRRhy0PA3pk5DhgD/C2wHngXcH6L+mnAPIqw5Q5gj8ycAEwAPl12OwY4rUX9vsDXKMKWa4EpmbktsANwcdltTkTM7vxPIUmSJEmSemIwBy4LMnO7zHxnZp6emVcDa7tR1w7MLOuuyMzFAJm5MTMfAN4L3Fj2/ceIGNnkHqcCOwHPAodk5t3lPdZl5oXAnLLfcRHxqib1n6YIZ5YAh2XmwrJ+ZWbOAS4p+30yItqa1H+BIjj6NTA7MxeV9U9n5vEUM28AzomIId34m7TBY3kAACAASURBVEiSJEmSpC0waAOXVq/6dKNueWb+spP2BC4rv44Fpjfp9uHyfHVmPtqk/SsUrxgNAY6qb4iIMUBtjZaLMvOZJvWfL8/jKV4Rqq/fjeKVJYDzMnN9J/VTgf2btEuSJEmSpF4YtIFLH1tT9/l5M0QiYg9gl/Lrj5oVZ+ZKYEH59eCG5v2AUV3UPwY82KL+oLrPN9Lc7cCKFvWSJEmSJKmXBvOiuX3pbeV5HbCwoW2vus/3d3KP+4FZwGt6UT+dYl2ZZvVLM3Nps8LM3BARDwF7N6lvKiLu6Ua3V3fnXpIkSZIkDXbOcNlC5YK2x5df/yMzOxq6TKr7/EQnt6q1jY+IsU3q2zPz2W7UT2q4PqmhfUvrJUmSJElSLznDZQtExCg2b9X8J+CMJt3G1X1e3cnt6tvGUazpUl/fWW19+7iG672tbyozZ3bVp5wFM6M795MkSZIkaTBzhks3RcRQ4CpgJsW2zkfVdjCSJEmSJEmqZ+DSDeXWyVdS7Aj0HPDBzLy5RfcVdZ9Hd3Lb+rYVTT53VlvfvqLhem/rJUmSJElSLxm4dKEMW74NzAY2AB/KzO92UlI/62VyJ/1qbR3lrkWN9W3lK0xd1TfOslnc0L6l9ZIkSZIkqZcMXDpRN7PlA2wOW/6ji7L6nYX2atlrc9sDvaz/TYv6iRGxQ7PC8nfVdhRqrJckSZIkSb1k4NJCGUpcBbyfzWHL1d0oXQj8ofz85y3uPQZ4a/m18dWk24Ha7kSt6nel2BK6Wf2P6z43rQfewubFclu9GiVJkiRJknrIwKWJupktsynWbDmqm2ELmZnAFeXXD0TE1CbdTgTGUgQ5VzbUrwK+V349ISImNKn/RHleAVzbUP8IRWgDcEpEDGtSX9td6XHg561+iyRJkiRJ6plBHbhERFtEbF872Px7R9dfj4ixdTW1NVvez+YFcrt6jajRecASioVpr4+ImeW9h0fECcBnyn6XZObCJvVnAquAlwM/jIhXlvVjIuJM4Piy32czs71J/ScowpzXA1dHxOSyfruI+Cowq+x3emZu2MLfJkmSJEmSujCoAxfgXuCpumNKef20husX1NW8hWLNFoAEvhIRSzo53t/40MxcDhwGPA28Brg7IjqAlcBXgeEUr/Kc1GzQmfkoxeya1RSvHi2MiGeA5cBZQACXA+e2qL+TIpR5DvhLYFFEtAN/Ak4ou52VmfOa/9kkSZIkSVJvDB3oAbwI1YdQw4Adu+jfdCehzLwnIvakmG1yGEXYs4piUdtvAZdl5sZWN83MGyLidWX9QRSzXdopQqSLM/N7rWrL+ksj4pfAKcABwA7AUuC/gK9k5k+6+F2SJEmSJKmHBiRwiYgAXlk+/+HMXNsXz8nMqT2ouY1iBkkVz38SOLk8elL/MHBcL57/S+ContZLkiRJkqSeqfSVoogYFRHvLo8pLfocCSwGHgR+DSyNiLlVjkOSJEmSJGkgVT3D5a+Ab1Is2LpbY2NEvItiQVrYPItkHPAvETEuM0+peDySJEmSJEn9rupFcw8qz7/IzP9r0n4uRdASwN3AdykWgg3gHyLi9RWPR5IkSZIkqd9VHbi8hmJnn583NkTEDGCvsv1fM3OfzJwN7E2xmGwAH614PJIkSZIkSf2u6sBlh/L82yZt7yrP64HP1S5m5u+BeRSBy34Vj0eSJEmSJKnfVR24bF+eO5q01cKUOzOzvaHtf8rztIrHI0mSJEmS1O+qDlxqC+GOet7FYhvofWnxuhHwp/I8tuLxSJIkSZIk9buqA5enyvOrGq7vA2xbfr6zSd3o8rym4vFIkiRJkiT1u6oDl/soZrkcGRH1s1z+pjyvB+5oUlfbQvqPFY9HkiRJkiSp3w2t+H7XAIcBrwBui4irgD0pdh9K4PrMXNWk7k3l+cGKxyNJkiRJktTvqg5crgT+Dnhj3VGzDpjbWBAR44G3UwQyd1U8HkmSJEmSpH5X6StFmbkRmAX8oLwU5bEYOCIzf92k7GhgePn5lirHI0mSJEmSNBCqnuFCZj4NvDcidqBYm2U18JsyjGnmIeCYojTvrno8kiRJkiRJ/a3ywKUmM59i865FnfW7ua/GIEmSJEmSNBCq3qVIkiRJkiTpJa/PZrjURMQuwHSgDRiemVf09TMlSZIkSZIGUp8FLhFxHHAy8MqGpisa+n0SOABYlJkf7avxSJIkSZIk9ZfKXymKiLER8WPgIoqwJeqOZu4C3gl8JCL2rHo8kiRJkiRJ/a0v1nD5DnAgRcDyKPB54Gud9P8JsKT8fFgfjEeSJEmSJKlfVRq4RMQhwKFAAt8CXp2ZnwRualWTmQn8mCKg2a/K8UiSJEmSJA2Eqme4fLg8LwSOzcznuln3v+V5esXjkSRJkiRJ6ndVBy77UsxuuSIzN2xBXe2Voh0rHo8kSZIkSVK/qzpwmVief7+FdevK8/AKxyJJkiRJkjQgqg5c1pTnYVtYt0N5bq9wLJIkSZIkSQOi6sBlcXne0rVY9i3Pj1Q4FkmSJEmSpAFRdeDyM4rdht4fEd26d0TsCLyPYu2Xn1Y8HkmSJEmSpH5XdeByRXneHfhcV50jYhRwFTAK2AB8o+LxSJIkSZIk9btKA5fMvAuYRzHL5fSImBcRb6JhTZeImBwRxwD3Am+jmN1yUWb6SpEkSZIkSdrqDe2De34U2BV4E8WrQu8rrydARDxHEcjUBHALcEofjEWSJEmSJKnfVf1KEZm5GjgA+BKwniJQqR21Z9a+rwe+CBySmc9VPRZJkiRJkqSB0BczXMjMdcBJEXEOMBt4KzAVmACsBJ6gWGD36sxc1BdjkCRJkiRJGih9ErjUZOYS4Mvl0aWIeHlm/rEvxyRJkiRJktTXKn2lKCLO60XtJOC26kYjSZIkSZI0MKpew+XkiDhtS4si4uUUYcsrKh6PJEmSJElSv6t80Vzg7Ij4SHc7lzNbfophiyRJkiRJGiSqDlzuodh96OsRcVhXncuZLT8BXkWxbfQXKx6PJEmSJElSv6s6cJkF/I5iMd7/iIi3tOpYhi0/ZXPY8q+ZeXrF45EkSZIkSep3lQYumfkn4F3AYmAU8MOI2KuxX5Ow5d8yc4vXfpEkSZIkSXoxqnwNl8x8jGKmyzPAtsCNEbFrrb3Ja0TnZ+apVY9DkiRJkiRpoPTForlk5q+BdwPPAi8Hbo6I7evClj3YHLac0hdjkCRJkiRJGih9ErgAZObtwJHARoodiG7m+WHLlwxbJEmSJEnSYNRngQtAZv4n8DGKnYteTxG2AHwlM0/uy2dLkiRJkiQNlD4NXAAy8zLgDIrQJYEvZ+Y/9vVzJUmSJEmSBsrQnhRFxGU9KFsODAHGd1KfmfnXPRmTJEmSJEnSi0WPAhfgaIrZKj3xkS7aDVwkSZIkSdJWraeBCxSvCFWtpyGOJEmSJEnSi0ZPA5dplY5CkiRJkiRpEOlR4JKZj1c9EEmSJEmSpMGiz3cpkiRJkiRJeqkxcJEkSZIkSaqYgYskSZIkSVLFerSGS0TsX/ucmT9vdr2n6u8nSZIkSZK0NerpLkW3UWzhnA33qF3vqcb7SZIkSZIkbXV6E27EFl6XJEmSJEl6Sehp4HLWFl6XpC0y9YzrK7nPY2cfWsl9JEmSJGlL9ChwycymwUqr65IkSZIkSS8l7lIkSZIkSZJUMQMXSZIkSZKkihm4SJIkSZIkVaxPt2COiIOA9wJ7A5OAscBKYDHwP8D8zLylL8cgSZIkSZLU3/okcImIGcBlwGvrL5fnccBOwAzgYxHxv8BfZ+a9fTEWSZIkSZKk/lb5K0URMQu4nSJsibrjGeCJ8lx//Q3AHRHx51WPRZIkSZIkaSBUGrhExM7A1cBIijDlLmA2sH1mbpeZUzJzO2D78vqdZelI4OqImFzleCRJkiRJkgZC1TNcTqV4ZSiBL2TmmzPzu5m5rL5TZi4rr+8HnF1eHlfWS5IkSZIkbdWqDlxmUYQtv8jMM7pTkJn/DPwXxYyYQ6saSESMjohZEfGpiJgfEY9HRJbH3G7eY8eI+GJE/DYino2IZRGxICKOjYjoRv3uEXFxRDwaEWsi4qmIuCki3tfN58+IiG9HxKKIWBsRf4yI70fEO7pZ//ay/x/L+kXl/WZ0p16SJEmSJPVM1YvmTinP39nCuu8A+wI7VziWfYAbelocETOBm4CXlZdWUszC2a88joiId2fmuhb1hwDXAKPLSx3AdsDBwMERcTnFYsHZov5Y4CI2/zdaDuwIHA4cHhFnZebcTsY/F5hTfs3y+ZOBo4D3R8QJmXlpZ38DSZIkSZLUM1XPcFlZnpdsYd2TDfVVaQduBc4FjqSb44qICcB1FGHLQ8DemTkOGAP8LbAeeBdwfov6acA8irDlDmCPzJwATAA+XXY7BjitRf2+wNcowpZrgSmZuS2wA3Bx2W1ORMxuUT+bzWHLxcAOZf2U8n5Dga+Vz5EkSZIkSRWrOnD5bXnedQvrdmmor8KCcqHed2bm6Zl5NbC2m7WnUmxd/SxwSGbeDZCZ6zLzQjaHGcdFxKua1H+aIpxZAhyWmQvL+pWZOQe4pOz3yYhoa1L/BWAI8GtgdmYuKuufzszjKWbeAJwTEUPqC8vvXyi/3piZx2fm02X9IuD9wP3l/b+AJEmSJEmqXNWBy1UUa7F8uDEIaKXs9xGK116urGogmbmhF+UfLs9XZ+ajTdq/QjEbZwjFKzqbRMQYoLZGy0WZ+UyT+s+X5/EUrwjV1+9G8coSwHmZub6T+qnA/g1tB7A58Pp8QxvlK1DnlV/3K2fjSJIkSZKkClUduFxCsdXzXsDlETG8s84RMQy4rOx/J/D1isezxSJiDzbPuPlRsz6ZuRJYUH49uKF5P2BUF/WPAQ+2qD+o7vONLYZ5O7Cii/oVFK8zNVM/rsZ6SZIkSZLUS5UGLuWsksMo1gk5CnggIk6KiNdHxJgojCm/nwQ8AHwI+D5waC9npVRlr7rP93fSr9b2ml7W79mifmlmLm1WWP6dHuqi/sFWf8/yvk+1qJckSZIkSb1U6S5FEVH/P/AD2I3Nr680LSnPtZ13WvXLzKx6R6VWJtV9fqKTfrW28RExtpz1Ul/fnpnPdqN+UsP1SQ3tndXv3cv6HZrUNxUR93Sj26u7cy9JkiRJkga7qkOMZolJyxRlC/v0l3F1n1d30q++bRybd1ga16S9s/pxDdcHul6SJEmSJPVS1YHLzykWv9Ugk5kzu+pTzoKZ0Q/DkSRJkiTpRa3SwCUz31bl/QbIirrPo4GOFv1Gt6hZ0aS9s/oVDdcHul6SJEmSJPVS1bsUDQaL6z5P7qRfra2jbv2W+vq2iBhFa7X6xQ3XFze093e9JEmSJEnqJQOXF6rfWWivlr02tz3Qy/rftKifGBE7NCuMiCFsXqC2Vf30sl+z+okUC+Y2q5ckSZIkSb00YIFLRIyIiDED9fxOLAT+UH7+82YdynG/tfx6c0Pz7UBtd6JW9bsC01vU/7juc9N64C1sXuy2Vf044M0t6uvv21gvSZIkSZJ6qdLAJSKGRsTryqOtRZ/9I+K/KXbJ6YiI30XE0VWOozcyM4Eryq8fiIipTbqdCIwFNgBXNtSvAr5Xfj0hIiY0qf9EeV4BXNtQ/whFaANwSkQMa1J/Rnl+nGKh4no/K6/X99ukvN8p5dfbM/PRJveXJEmSJEm9UPUMl8OB+4B7gR0bGyNib4oZGDMptoIOYHfgGxFxasVjISLaImL72sHm3zu6/npEjG0oPQ9YQrGw7PURMbO83/CIOAH4TNnvksxc2OTRZwKrgJcDP4yIV5b1YyLiTOD4st9nM7O9Sf0nKMKc1wNXR8Tksn67iPgqMKvsd3pmbqgvLL+fXn49JCK+GhHblfWTgauB15X3Px1JkiRJklS5qgOX2qsq92XmQ03a/w0YRhG0LAHupvgf/gF8NiJ2q3g89wJP1R1TyuunNVy/oL4oM5cDhwFPA68B7o6IDmAl8FVgOMWrOCc1e2g5a2Q2xSyetwILI+IZYDlwFsXvvRw4t0X9nRShzHPAXwKLIqId+BNwQtntrMyc16J+Xvkcyv5/KusXlfd7Djg+M/+rWb0kSZIkSeqdqgOX1wEJ/LSxISJeTbGmSFK8hjMlM/ehCCTWUQQxx1Y8nh7LzHuAPSlCot9RjG8Vxes+fwPMysy1ndTfQPH3+DrwGDASaKeY4XNEZn60fH2pVf2lwJuAq4AnKGbbLKV4BenAzJzbxfjnAgeW/ZeW9U+U9/uz8v6SJEmSJKkPDK34frWdbx5s0lZ7DWYjda/CZOZdEXEtxYyQt1c5mMyc2sv6J4GTy6Mn9Q8Dx/Xi+b8EjupF/U+An/S0XpIkSZIk9UzVM1y2L8/N1iWp7erzP5n5x4a2BeX5lRWPR5IkSZIkqd9VHbgMazjXq71O9LMmbUvL87gmbZIkSZIkSVuVqgOXZeX5eYvfRsRrgYnl12YLtY4oz+srHo8kSZIkSVK/qzpwuZ9iB54jIiLqrn+kPG9k8+tD9Wq7Bz1Z8XgkSZIkSZL6XdWBy7Xl+Q3A9yPiPRHxz8DfU+5elJnN1nfZuzz/tuLxSJIkSZIk9buqdyn6BnASsDvwF+UBxayXjcBnGgsiYiRwEEUg898Vj0eSJEmSJKnfVTrDJTPXAu8C7qMIWWrHs8DHM7PZ60QfAMaUn2+tcjySJEmSJEkDoeoZLmTmI8CMiJgJvAJYDdze4lUigHXAWRQzXO6oejySJEmSJEn9rfLApSYz7wHu6Ua/q/pqDJIkSZIkSQOh6kVzJUmSJEmSXvIMXCRJkiRJkipm4CJJkiRJklQxAxdJkiRJkqSKGbhIkiRJkiRVzMBFkiRJkiSpYgYukiRJkiRJFTNwkSRJkiRJqpiBiyRJkiRJUsV6FLhERHtEPB0Rsxqu718e21UzPEmSJEmSpK1PT2e4TAC2BYY1XL8N+CmwXy/GJEmSJEmStFXraeCS5TmqGogkSZIkSdJg0dPAZVV53rGqgUiSJEmSJA0WPQ1cfl+ePxQRI6sajCRJkiRJ0mAwtId1PwLeALwFWBQRvwXW1rV/NiL+sQf3zcw8sIdjkiRJkiRJelHoaeByHvBBYFdgO+DP6toC2LMH9ww2rw0jSZIkSZK01erRK0WZ2Q68CbgAeBhYz/MDk+jBIUmSJEmSNCj0dIYLmbkU+Pv6axGxkSJ0eW9m/mcvxyZJkiRJkrRV6umiuZIkSZIkSWqhxzNcWjirPD9U8X0lSZIkSZK2GpUGLpl5Vte9JEmS/j979x6uS1nfB//7A0Q5bAhqA0Iq20PEc2rQaqsmKhUVCDFBcpmQSIyHhMQm9VTN4VWKMTFRK62xRmk8ReOBmGoREc+vSn0TRZtqRDHKjm9FrAaEvdmIKL/+8czKflw+69nrMGuvtTefz3XNNfPMff9m7rUuhmfx5Z4ZAIB92x67paiqDqiqI6pq7Fk1AAAAAJvKugUuVXVgVf1KVb2rqr6R5MYk30xyY1X9n2H/L1fVges1BgAAAICNsC6BS1X9RJLLk5yX5DFJbpfvfwX07Yf9f5bkC1X10PUYBwAAAMBGGD1wqapHJnlvkn+eXQHLjiR/m+SSYb1jqu3YJO+rqn8z9lgAAAAANsKogUtVHZbkL5IcmEmYcmGShyY5vLvv190PHdaHDfvfNZQemOTNQz0AAADAXm3sGS6/nsntQ53k/+nun+ruS7q7F3cc9p+a5PeGXbdNctbI4wEAAADY48YOXE4e1pd09wuXU9Ddf5DkY5nMiPmpkccDAAAAsMeNHbjcLZPZLW9bYd1C/7uNOxwAAACAPW/swOXwYX3VCuu+Pqw9wwUAAADY640duFwzrO+4wrofGdbfGnEsAAAAABti7MDlskyexfILyy2oqkpyRia3In1u5PEAAAAA7HFjBy4XDOv7VdVLl1nzR0l+fNh+58jjAQAAANjjxg5c/jTJ14btf1dVH6uqn66qQ6Y7VdXBVXVqVX0kyTOH3V9L8uqRxwMAAACwxx0w5sG6+4aqOi3JB5LcJsm/SvJXSbqqrkpyfZJDkhyVya1HGdY3JDmtu28YczwAAAAAG2HsGS7p7v8vyUOy63kuNZzn6CR3Hdb7TbX9XZJ/3d1/PfZYAAAAADbCqDNcFnT3p6vqPklOSfKzSf5lkjsk2ZJkeya3D/1NkrcnubC7ez3GAQAAALAR1iVwSZIhRLkgux6kCwAAAHCLMPotRQAAAAC3dAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAY2QEbPQCA9bT1uReu+RjbXnTyCCMBAABuScxwAQAAABjZqDNcquqDw+ZHuvvsMY8NAAAAsLcY+5ainxzW5498XAAAAIC9xti3FH1j0RoAAADgFmfswOWLw/rokY8LAAAAsNcYO3A5P0kl+dmRjwsAAACw1xg7cHlVks8leWhV/ebIxwYAAADYK4wauHT3jUlOSfK3SV5WVedX1U9W1YFjngcAAABgMxs1cKmqLyf5YJIjs+vWog8m2VFVX6uqL+9m+dKY4xlDVT2yqt5WVf9QVd+uqhuGsb6pqn5yN7VbqursqvpMVe2oqmur6hNV9czlhFBVdWRVvbSqvjCc9+qq+mhVPbmqahn1d6mqV1XVFcPYv1FVF1fVaSv5HQAAAAArM/Zrobcm6WF7YV3DeY5cRn3vvsueMQQar0zyq1O7bxjWdxqWX6iql3X3M2bUH5vkw5n8TpJkZ5JbJ7n/sJxRVSd09zVLnP/4JBcnud2wa0eSLUkeMiyPq6pTu/s7S9SflMkzdQ4edl2X5LZJTkxyYlW9NsmTunvT/M4BAABgXzH2M1y+MmP5hxUsXxl5PGvxy9kVtvxlkrt198HdfXCSuyd559D29Kr6menCqjogyQWZhC1fS/LI7j4kk/Dj8Um2J7lfkjfOOnFVHZ7kXZmELZ9P8oDu3pLkkCRPS3JTkkclOXeJ+jsledtwvkuSHNfdhyc5PMk5Q7cnJnn28n4VAAAAwEqMOsOlu7eOebwN9oRh/fdJfr67v7vQ0N1fqKrTMwlD7pzk55L8t6naM5PcZ9g+rbs/PtTdnOStVbVfkr9IctIwy+UDi879rCRHZTKj5qTuvmKo/06SV1TVYUn+IMlTq+rc7r58Uf05mYQzVyU5pbu/NdTvSPL8qjoqyVOT/G5VnbfULBsAAABgdcae4bIvucOw/tvpsGVBd9+U5H8OHw9d1HzmsP7QQtiyyFuSXDFsP2FG+8K+tyyELYu8PJNbjPZPcsZ0Q1UdkmThGS2vXAhbFvnDYX1YksfOaAcAAADWQOCytC8P6x8bbhH6PlV1qyT/Yvj4yan9Byd58PDxolkHHp6b8p7h44mLjntckjvupn5Hko/Oqs/k+S4H7aZ+W5LLlqgHAAAA1kjgsrRXDuu7JnlzVd11oWEIRd6Wye1EX0rysqm6e2TX7/Wzc46/0HZUVd12av+9Z/SZV3/PRftXWn+vOX0AAACAVRj7LUXfp6oelMkMinskOSLJrbr7hEV9bp/kwCTf7u6r13M8K9HdF1TV05P8UZLHZfJWoIW3FB2U5FuZhDK/193XTZUePbX91TmnmG47OsnVU9srqT+sqg4dZr1M11/T3TfMqFtcf/ScPv+kqi5dRre7L+dYAAAAsK9bl8Clqu6e5DVJHji9O7Nf+/ycJM9I8s2qOmbW81I2SnefW1VfzORn+eHsulUnmYREh2by5p/poGjL1PbOOYefbtuyxPZK6ndMbe+udrp9y9xeAAAAwIqNfkvRMKvlE5mELTW1LOVPhvbbZxM9T6SqDq6qt2byeuavZDK2fzYsJyb5XJJfSvI3VXXfDRvoHtLdx+9uyeStTQAAAHCLN2rgMrwh579l8kri7yZ5QZLjMnlt8kzd/Q/Z9dDZTRO4JHlxJuP+QpKHdvf7uvubw/K+JD+R5PJMgqJXTNVtn9o+eM7xp9u2L7G9lvp5tdPt2+f2AgAAAFZs7Bkuv57kyCQ3J/nZ7n5+d38xyU27qftYJrNc7j/yeFalqrYkeerw8RXd/e3FfYbno/zJ8PEhVfXDw/aVU92OmXOa6bYrl9heTv11U89vma4/oqoOytIW6q+c0wcAAABYhbEDl5/K5Dkt/727L1xB3cKtKHed22vPuVt2Pd/mS3P6fXFq+07D+rJMAqfk+98YtNhC21WLHhb82Rl95tV/btH+ldb/3Zw+AAAAwCqMHbgsvKXmPSusWwgcDh9xLGtx89T2sXP6HTm1vT1JuntnkkuGfY+eVVRVleRRw8f3Lmq+PJNnxsyrPyTJQ5eo/1iShbcTLVV/bCZvjppVDwAAAKzR2IHLQmDyzRXW3WpYf2/EsazF57MrtHhyVf3A25yqav/suu3omkye9bLg9cP64VX1wPyg05Pcedh+w3RDd/fUvsdX1dYZ9b+RyRuSvpfkTYvqr0/y9uHjWVU1K8R6zrDenuQdM9oBAACANRg7cFmYqXK7FdYt3I6z0qBmXQzPZ/mvw8cfT3JBVd2nqvYblvsmeXeSfz30Obe7p8Oi1yf5TCbPpXl7VZ2QJEPt6UnOG/pd1N0fmDGElyS5KpMH215YVccP9QdW1VmZPIw4SV7d3ZfPqH9ekuuT3GEY+48O9YdU1fOS/NrQ7/e7+5rl/l4AAACA5fmBmRtr9MUkP5xJEHHebvpOe0wmz3759MjjWYvnJPnRTG7LWVhuHNpuPdXvzUleOF3Y3d+tqlOTfCjJ1iTvr6qdmQRctxm6fTrJGbNO3N3XVtUpSS5Ocs8kn6yq7UPtwmyg9yZ5+hL1V1TVzyU5P5Nbjy6vqmszmRWz/9DttZm8iQkAAAAY2dgzXN6TyayOx1XVvDfs/JOqelSSBw8f3z3yeFZtmOVyUia3/7wzyf/O5GdLkv8/k9t2TunuX1g0u2WhfluS+yY5J5MH2XYmb2u6NMmzkjxo3uyS7r40H93a+gAAIABJREFUyb2SvCyTIOtWmcxa+ViSpyR5THffOKf+3cP5z0uyLZOw5pok70vyuO7+leH2JQAAAGBkY89weVUmM0MOTfLOqjqlu69aqnNV/ZvsegbJ17PoeSYbbQgk/nJYVlO/Pcnzh2U19V9P8oxhWU39l7LrOTMAAADAHjJq4NLd/1hVv5XkNUnul+TzVfXmTD0Mt6rOTPIjSU5M8pBMZo3cnOQp82ZsAAAAAOwtxp7hku5+3fBmnBcnOSy7Zlgs3L7ymqnulcltNmd194VjjwUAAABgI4z9DJckSXf/p0wenPuuTIKWmrEkyUWZPMvkNbOOAwAAALA3Gn2Gy4Lu/mSSU6vqhzJ5KO7WJIcn2ZHkq0k+0t3fWK/zAwAAAGyUdQtcFnT3t5K4XQgAAAC4xViXW4oAAAAAbsnWfYZLklTVliRHZfK66B1JrhpemQwAAACwz1m3wKWqfiTJWUl+JsndsutBuUnSVXV5krcn+dPu/up6jQMAAABgT1uXW4qq6teTXJbkuUmOG84z/Yai/Yb9v5Pk81V11nqMAwAAAGAjjD7Dpaqel+T5Cx+TfC+T8OXvk1yf5JAkd01yjyT7D5//pKpu390vGHs8AAAAAHvaqIFLVT0oyfMyCVq+m+SlSc7t7q/P6Htkkt9K8swkt0ry/Kp6b3f/9ZhjAgAAANjTxr6l6DeHY96c5PTu/u1ZYUuSdPfXu/t3kjwuSWcS0vzmyOMBAAAA2OPGDlwemkl48vbufudyCrr7giTnZxK4/MTI4wEAAADY48YOXP7ZsH7PCusuHta3H3EsAAAAABti7MDlG8N65wrrFvp/c8SxAAAAAGyIsQOXTw3r+6ywbqH/pSOOBQAAAGBDjB24vCqTZ7E8paput5yCqrp9kqdk8uyXPx15PAAAAAB73KiBS3e/O8krM3mWywer6u7z+lfVcUneP/R/RXev9NkvAAAAAJvOAaspqqp5bxN6a5I7JHlskr+tqouTfCDJ32fyrJaDk9w1ySOSPHoYw39L8pdV9RPd/ZHVjAkAAABgs1hV4JLkw5ncAjRPJ7lVkpOHZZYa+j12WHoNYwIAAADYFNYSbtRI/ZZ7HAAAAIC9wmoDl/8w6igAAAAA9iGrCly6W+ACAAAAsISxXwsNAAAAcIsncAEAAAAYmcAFAAAAYGTr+grmqtovyV2SHJHkNsup6e6PrOeYAAAAANbbugQuVfWIJM9I8ogkt15Baa/XmAAAAAD2lNHDjar64yTPXPg49vEBAAAANrtRA5eqOj3Js6Z2fTHJx5J8PcmNY54LAAAAYLMae4bL04b1TUme1N1vHPn4AAAAAJve2G8p+rFMnsNynrAFAAAAuKUaO3BZeGbLR0c+LgAAAMBeY+zAZduwPnDk4wIAAADsNcYOXP57JrNcHjzycQEAAAD2GmMHLn+S5JtJnlBV9xr52AAAAAB7hVEDl+7+epKfTvLdJB+oqtPGPD4AAADA3mDs10Knuz9eVfdJ8o4kb6uqrye5NMk/Jrl59+X9pLHHBAAAALAnjR64VNXhSc5Ocq9MnudyVJKTVnAIgQsAAACwVxs1cKmqQ5N8MMm/WNy0zEP0mOMBAAAA2Ahjz3D5rST3G7avzOQhupck+XqSG0c+FwAAAMCmNHbg8vhhvS3Jv+zub458fAAAAIBNb+zXQt8pk9uCXiFsAQAAAG6pxg5crh/W/zDycQEAAAD2GmMHLp8b1keNfFwAAACAvcbYgcufZ/JGotNHPi4AAADAXmPswOW1mbwW+iFV9ZyRjw0AAACwVxg1cOnuTvLTSd6e5A+q6sKqOqmqbjfmeQAAAAA2s1FfC11V35v+mOTRw5KqWs4hurvHflU1AAAAwB41drixOFVZVsoCAAAAsC8ZO3D5SJIe+ZgAAAAAe5VRA5fuftiYxwMAAADYG3leCsBubH3uhWs+xrYXnTzCSAAAgL3F2K+FBgAAALjFE7gAAAAAjEzgAgAAADCyUZ/hUlUfXOMhurtPGGUwAAAAABtk7IfmPiyrfy10raEWAAAAYNNYj7cU1Qr79ypqAAAAADatUZ/h0t377W5Jsn+SI5OcmuQDmYQtb05yUHfvP+Z4AAAAADbCHn9obk98o7vf1d2PTPL7SR6f5K17eiwAAAAA62HD31LU3c9L8qkkP1VVP7/R4wEAAABYqw0PXAZvzuTWoidt9EAAAAAA1mqzBC5fGdb33tBRAAAAAIxgswQutx/Wh2/oKAAAAABGsFkClzOG9VUbOoolVNVhVfWcqvofVfWNqrqxqv53VX2oqs6uqh9aom7L0P6ZqtpRVddW1Seq6plVdeAyzntkVb20qr5QVTdU1dVV9dGqenJV7fZV2lV1l6p6VVVdUVXfHsZ+cVWdtprfAwAAALA8B2zkyatqa5I/SvLgJJ3Ja6I3lap6eCbPmDly2PWdJDuTHDMsD0vyjiT/c1HdsUk+nGTrsGtnklsnuf+wnFFVJ3T3NUuc9/gkFye53bBrR5ItSR4yLI+rqlO7+ztL1J+U5PwkBw+7rkty2yQnJjmxql6b5End3cv4NQAAAAArMOoMl6r64DKXj1XVl5N8KcnjhvIbMwlfNo2qenCSCzMJW/4qyQOS3Ka7j0hySJJ/meSFSa5dVHdAkgsyCVu+luSR3X1IJuHH45NsT3K/JG9c4ryHJ3lXJmHL55M8oLu3DOd8WpKbkjwqyblL1N8pyduG812S5LjuPjyTW7bOGbo9McmzV/L7AAAAAJZn7BkuD8tkpspyTN8Sc3WSX+ruL448nlWrqoOTvCHJQUle3t2/Od3e3TuTfGJYFjszyX2G7dO6++NDzc1J3lpV+yX5iyQnDbNcFs/seVaSo5LckOSk7r5iqP9OkldU1WFJ/iDJU6vq3O6+fFH9OZmEM1clOaW7vzXU70jy/Ko6KslTk/xuVZ231CwbAAAAYHXW4xkutYzlpiT/J5Nbbp6b5O7dfdE6jGUtfinJnTMJLf79CmvPHNYfWghbFnlLkiuG7SfMaF/Y95aFsGWRl2dyi9H+2fX8myRJVR2SZOEZLa9cCFsW+cNhfViSx878CQAAAIBVGzVw6e79lrncpruP6u5HdPcfd/c3xxzHSBZCj/O7+9vLLRpmxjx4+DgzRBqem/Ke4eOJi+qPS3LH3dTvSPLRWfWZPN/loN3Ub0ty2RL1AAAAwBpt6ENzN6uqWni4bZJcWlV3TPJ7SR6TyfNcrknyN0n+tLsvXFR+j+wKsj475zQLbUdV1W27++rh871n9Fmq/jFJ7rlo/0rq75HkXnP6/JOqunQZ3e6+nGMBAADAvm6zvBZ6s9maZOG1zXfOJJx4SpIfTnL9sD4lybuq6rxFr2g+emr7q3POMd129BLby6k/rKoOnVF/TXffsIz6o+f0AQAAAFbBDJfZjpja/r0k30pyepJ3dvdNw4yXlwz7npzJ7Tn/cei/Zap255xzTLdtWWJ7JfU7FtXPq51u3zK316C7j99dn2EWzI8v53gAAACwLzPDZbb9Fm0/qbv/srtvSpLu/komr3f+26HP7wyvggYAAABY/QyXqnremANZ0N3nrMdxV2j71PYXu/sdizt0981V9ZIkf57kdkmOT/LXi2oPnnOO6bbtS2wfnOS6VdbPO/d0+/a5vQAAAIAVW8usjLOT9EjjmLYZApfpZ6d8fk6/z01tH5tJ4HLl1L5jkvyvJWqPmdq+contY7J04LJQf93w1qLF9UdU1UFznuNyzKL+AAAAwEjWektRjbxsCsMbg+Y9sHbB9JgXwqfLktw8bN87S1tou2rqDUXJ979ZaDn1n1u0f6X1fzenDwAAALAKa5nh8vMjnP/HkvzbTG5v2TSBy+C9SZ6YyauTlzL9SuYrkqS7d1bVJUkemuTRSV68uGh4q9Gjps4z7fIkX0lyx6H+/Bn1hwzHn1X/sSQ3JDloqP/EjPpjp36uxfUAAADAGq06cOnut662tqrunMmtQ4/P989uec9qj7kOXptJ4HLXqnrs4ue4VNV+SZ41fPxqkk9NNb8+k0Dk4VX1wO7+60XHPj2T100nyRumG7q7q+oNmbwd6fFV9YLu3rao/jeSHJrke0netKj++qp6e5JfTHJWVf3n7r52Uf1zhvX2JD/wfBoAAABgbfboW4qq6qiq+i+Z3Hbz88P5K8n/SPKT3X3ynhzPPN390SR/OXz8r1V12sKbiIbXQr85yX2H9t/t7punyl+f5DOZ/Gxvr6oThrr9qur0JOcN/S7q7g/MOP1LklyVycyfC6vq+KH+wKo6K8kLhn6v7u7LZ9Q/L8n1Se6Q5IKq+tGh/pDhYce/NvT7/e6+Zpm/EgAAAGCZ9sirjKvqh5I8N8nTMrnVZWFGy//KJKy4cE+MYxV+OckPJ/mJTMKXG6tqZ5Ijpvr8h+5+/XRRd3+3qk5N8qEkW5O8f6jbL8lthm6fTnLGrJN297VVdUqSizO5bemTVbV9qL3V0O29SZ6+RP0VVfVzmdyO9NAkl1fVtZnMitl/6PbazLjdCQAAAFi7dZ3hUlUHV9XvJPlykmdn17NavpTkjO7+F5s4bEl3X5/k4UmekuQjmcwaOTSTW4jekuTB3X32ErXbMpkBc04mD7LtJDcluTSTW5EeNG92SXdfmuReSV6W5IuZBC3XZ/KMlqckeUx33zin/t3D+c9Lsi2TsOaaJO9L8rju/pXuXo+3TAEAAMAt3rrMcBluvfm1JL+byQyRhRktV2ZyO8yfdfd31+PcYxtuFfqvw7LS2u1Jnj8sqzn315M8Y1hWU/+lJE9dTS0AAACweqMGLsPbd34pydlJjl3YneTqJH+U5OXd/e0xzwkAAACw2YwWuFTVY5P8fna9brgyuQXm3CQv7u7rxjoXAAAAwGa25sClqh6R5A+SPGBhV5LvJHlVkhd29/9Z6zkAAAAA9iarDlyq6gGZBC2PWNiV5OYkb0hydnf/w9qHBwAAALD3WcsMl7/O5M07Naz/KsnvdffnxxgYAAAAwN5qjGe4dJKdSe6Z5K8mz81d/bG6+14jjAkAAABgw4z10NyDkxy3xmMszJQBAAAA2KutNXBZ03QWAAAAgH3RqgOX7t5vzIEAAAAA7CuEJgAAAAAjE7gAAAAAjEzgAgAAADAygQsAAADAyAQuAAAAACMTuAAAAACMTOACAAAAMDKBCwAAAMDIBC4AAAAAIxO4AAAAAIxM4AIAAAAwMoELAAAAwMgELgAAAAAjE7gAAAAAjEzgAgAAADAygQsAAADAyAQuAAAAACMTuAAAAACM7ICNHgDALcHW5144ynG2vejkUY4DAACsLzNcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcVqiqnltVvbDspu+Wqjq7qj5TVTuq6tqq+kRVPbOqDlzGuY6sqpdW1Req6oaqurqqPlpVT66qWkb9XarqVVV1RVV9u6q+UVUXV9VpK/mZAQAAgJU5YKMHsDepquOSPH+ZfY9N8uEkW4ddO5PcOsn9h+WMqjqhu69Zov74JBcnud2wa0eSLUkeMiyPq6pTu/s7S9SflOT8JAcPu65LctskJyY5sapem+RJ3T03NAIAAABWzgyXZaqq/ZK8Jsltknx8N30PSHJBJmHL15I8srsPyST8eHyS7Unul+SNS9QfnuRdmYQtn0/ygO7ekuSQJE9LclOSRyU5d4n6OyV523C+S5Ic192HJzk8yTlDtycmefbuf3IAAABgpQQuy/dvk/zrJG9K8t7d9D0zyX2G7dO6+/1J0t03d/dbk/zq0HZSVZ0wo/5ZSY5KckOSk7r7k0P9d7r7Fdk1y+apVXW3GfXnZBLOXJXklO6+fKjf0d3PT/Lqod/vVtURu/lZAAAAgBUSuCzDMGPkhUn+McnTl1Fy5rD+UHfPmg3zliRXDNtPmNG+sO8t3X3FjPaXZ3KL0f5Jzlg01kOSLDyj5ZXd/a0Z9X84rA9L8tiZPwEAAACwagKX5Tkvkxkjz+jub8zrWFUHJ3nw8PGiWX2G56a8Z/h44qL645LccTf1O5J8dFZ9Js93OWg39duSXLZEPQAAALBGApfdqKqnJDkhyfu7+w3LKLlHdv1ePzun30LbUVV126n9957RZ179PRftX2n9veb0AQAAAFbBW4rmqKpjkrw4k2ep/Opuui84emr7q3P6TbcdneTqVdYfVlWHDrNepuuv6e4bllF/9Jw+/6SqLl1Gt7sv51gAAACwrzPDZb5XZfJmn7O7+8vLrNkytb1zTr/pti1LbK+lfl7tdPuWub0AAACAFTPDZQlV9YtJTk7yP5P8xw0ezobr7uN312eYBfPje2A4AAAAsKmZ4TJDVR2Z5Nwk30vylO7+7grKt09tHzyn33Tb9iW211I/r3a6ffvcXgAAAMCKCVxme1GS2yV5dZLPV9Wh00uSAxc6Tu1f2Hfl1HGOmXOO6bYrl9heTv11U89vma4/oqoOytIW6q+c0wcAAABYBYHLbHca1mdlMgNk8fLbU30X9v3x8PmyJDcP29NvDFpsoe2q7r56av9nZ/SZV/+5RftXWv93c/oAAAAAqyBwGVl370xyyfDx0bP6VFUledTw8b2Lmi9P8pXd1B+S5KFL1H8sk7cqzas/NpPXV8+qBwAAANZI4DJDdz+su2upJcl/mOq7sP/fTR3i9cP64VX1wBmnOD3JnYftNyw6d0/te3xVbZ1R/xtJDs3kGTNvWlR/fZK3Dx/PqqrDZ9Q/Z1hvT/KOGe0AAADAGghc1sfrk3wmSSV5e1WdkCRVtV9VnZ7kvKHfRd39gRn1L0lyVSYPtr2wqo4f6g+sqrOSvGDo9+ruvnxG/fOSXJ/kDkkuqKofHeoPqarnJfm1od/vd/c1a/xZAQAAgEW8FnoddPd3q+rUJB9KsjXJ+6tqZyYB122Gbp9OcsYS9ddW1SlJLk5yzySfrKrtQ+2thm7vTfL0JeqvqKqfS3J+JrceXV5V12YyK2b/odtrk7x4LT8nAAAAMJsZLuuku7cluW+SczJ5kG0nuSnJpUmeleRB82aXdPelSe6V5GVJvphJ0HJ9Js9oeUqSx3T3jXPq3z2c/7wk2zIJa65J8r4kj+vuXxluXwIAAABGZobLKnT32UnOXka/7UmePyyrOc/XkzxjWFZT/6UkT11NLQAAALB6ZrgAAAAAjEzgAgAAADAygQsAAADAyAQuAAAAACMTuAAAAACMTOACAAAAMDKBCwAAAMDIDtjoAQCwfFufe+Gaj7HtRSePMBIAAGAeM1wAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEdsNEDAGDP2vrcC9d8jG0vOnmEkQAAwL7LDBcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQClyVU1e2q6olV9caq+lxVXV9VN1bV/66qd1TVzyzjGFuq6uyq+kxV7aiqa6vqE1X1zKo6cBn1R1bVS6vqC1V1Q1VdXVUfraonV1Uto/4uVfWqqrqiqr5dVd+oqour6rTl/h4AAACAlTtgowewiV2V7//9fDvJTUmOGZafrqqLkjyuu3cuLq6qY5N8OMnWYdfOJLdOcv9hOaOqTujua2advKqOT3JxktsNu3Yk2ZLkIcPyuKo6tbu/s0T9SUnOT3LwsOu6JLdNcmKSE6vqtUme1N09/9cAAAAArJQZLks7IMnfJPn1JHfp7oO6+9Akd0ryZ0OfxyR51eLCqjogyQWZhC1fS/LI7j4kk/Dj8Um2J7lfkjfOOnFVHZ7kXZmELZ9P8oDu3pLkkCRPyyT4eVSSc5eov1OStw3nuyTJcd19eJLDk5wzdHtikmcv71cBAAAArITAZWmP6O4Hdvcru/vLCzu7e1t3Pzm7gpZfrKp/vqj2zCT3GbZP6+73D7U3d/dbk/zq0HZSVZ0w49zPSnJUkhuSnNTdnxzqv9Pdr0jy/KHfU6vqbjPqz8kknLkqySndfflQv6O7n5/k1UO/362qI5bxuwAAAABWQOCyhO7+0G66/NnU9v0XtZ05rD/U3R+fUfuWJFcM20+Y0b6w7y3dfcWM9pdncovR/knOmG6oqkOSLDyj5ZXd/a0Z9X84rA9L8tgZ7QAAAMAaCFxW79tT2/svbFTVwUkePHy8aFbh8NyU9wwfT5xuq6rjktxxN/U7knx0Vn0mz3c5aDf125JctkQ9AAAAsEYemrt6D5va/szU9j2yK8j67Jz6hbajquq23X318PneM/osVf+YJPdctH8l9fdIcq85ff5JVV26jG53X86xAAAAYF9nhssqVNUPJfnt4eNHu/sLU81HT21/dc5hptuOXmJ7OfWHVdWhM+qv6e4bllF/9Jw+AAAAwCqY4bJCVbVfkj9PcodMbit62qIuW6a2f+B10Uu0bVlieyX1OxbVz6udbt8yt9egu4/fXZ9hFsyPL+d4AAAAsC8zw2Xl/lOSU4bt3+ju/7WRgwEAAAA2H4HLClTVS7JrRsvTu/s1M7ptn9o+eM7hptu2L7G9lvp5tdPt2+f2AgAAAFZM4LJMVfXHSZ45fHxWd5+7RNcrp7aPmXPI6bYrl9heTv11w1uLFtcfUVUHZWkL9VfO6QMAAACsgsBlGarqxUmePXz899390jndL0ty87B97zn9FtqumnpDUfL9bxZaTv3nFu1faf3fzekDAAAArILAZTeG24ieNXz899394nn9u3tnkkuGj49e4piV5FHDx/cuar48yVd2U39IkocuUf+xJAtvJ1qq/thMXgk9qx4AAABYI4HLHEPYMn0b0dywZcrrh/XDq+qBM9pPT3LnYfsN0w3d3VP7Hl9VW2fU/0aSQ5N8L8mbFtVfn+Ttw8ezqurwGfXPGdbbk7xjyZ8CAAAAWBWvhV7Come2PKO7X7aC8tcn+a0k90ny9qo6s7s/MLxS+rQk5w39LuruD8yof0mSJyc5KsmFVfWE7r60qg5M8qQkLxj6vbq7L59R/7wkP5PJq6svqKondfcXh5kxz0zya0O/3+/ua1bwcwEkSbY+98JRjrPtRSePchwAANhsBC4zVNUds+uZLTcneU5VPWdOyUu6+yULH7r7u1V1apIPJdma5P1VtTOTGUW3Gbp9OskZsw7W3ddW1SlJLk5yzySfrKrtQ+2thm7vTfL0JeqvqKqfS3J+JrceXV5V12YyK2b/odtrkyx3xg4AAACwAm4pmm2/RdtH7mY5dPEBuntbkvsmOSeTB9l2kpuSXJrJM2EeNG92SXdfmuReSV6W5IuZBC3XZ/KMlqckeUx33zin/t3D+c9Lsi2TsOaaJO9L8rju/pXh9iUAAABgZGa4zDCEJTXCcbYnef6wrKb+60meMSyrqf9SkqeuphYAAABYPTNcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAds9AAAuOXa+twL13yMbS86eYSRAADAuMxwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVwAAAAARiZwAQAAABiZwAUAAABgZAIXAAAAgJEJXAAAAABGdsBGDwAA1mLrcy9c8zG2vejkEUYCAAC7mOECAAAAMDKBCwAAAMDIBC4AAAAAIxO4AAAAAIxM4AIAAAAwMoELAAAAwMgELgAAAAAjO2CjBwAAG23rcy8c5TjbXnTyKMcBAGDvZ4YLAAAAwMgELgAAAAAjE7gAAAAAjEzgAgAAADAyD83lFmush2QCAADAYma4AAAAAIxM4AIAAAAwMoELAAAAwMgELgAAAAAjE7gAAAAAjMxbigBgJGO8/Wzbi04eYSQAAGw0M1wAAAAARiZwAQAAABiZwAUAAABgZJ7hAgCbiOfAAADsG8xwAQAAABiZwAUAAABgZG4pAoB9zBi3JSVuTQIAWAszXAAAAABGJnDZx1XVlqo6u6o+U1U7quraqvpEVT2zqg7c6PEBAADAvsgtRfuwqjo2yYeTbB127Uxy6yT3H5YzquqE7r5mQwYIAAAA+yiByz6qqg5IckEmYcvXkjyhu99fVfslOT3JeUlcijL0AAAY3ElEQVTul+SNSdykD8APGOtZMGvlWTIAwN7ILUX7rjOT3GfYPq27358k3X1zd781ya8ObSdV1QkbMUAAAADYV5nhsu86c1h/qLs/PqP9LUlemOROSZ6Q5AN7amAAsBLeugQA7I0ELvugqjo4yYOHjxfN6tPdXVXvSXJWkhP31NgAYKO4RQoA2JMELvume2TX7WKfndNvoe2oqrptd1+9vsMCADZL8JMIfwBgPQlc9k1HT21/dU6/6bajkywZuFTVpcs4749ddtllOf7445fRdeN97avXbvQQbtGOP2jX9tdu+K2NGwjALditX+ffv5vZvY85fM3H+Owm+ntnjJ8HYCN86lOfelN3n7HSOoHLvmnL1PbOOf2m27Ys2Wv5vnfDDTdc+6lPfWrbCMdab3cf1p/f0FHcgn3q+z59aYNGwQq5bmB1XDusyqe+vtEjGNcKfx7XDayc62aTEbiwLN29d0xbWaaFGTv72s8F68l1A6vj2oGVc93AyrluNh+vhd43bZ/aPnhOv+m27Uv2AgAAAFZE4LJvunJq+5g5/abbrlyyFwAAALAiApd902VJbh627z2n30LbVd5QBAAAAOMRuOyDuntnkkuGj4+e1aeqKsmjho/v3RPjAgAAgFsKgcu+6/XD+uFV9cAZ7acnufOw/YY9MyQAAAC4ZRC47Lten+QzSSrJ26vqhCSpqv2q6vQk5w39LuruD2zQGAEAAGCf5LXQ+6ju/m5VnZrkQ0m2Jnl/Ve3MJGS7zdDt00nO2JgRAgAAwL7LDJd9WHdvS3LfJOck+WySTnJTkkuTPCvJg7r7mg0bIAAAAOyjqrs3egwAAAAA+xQzXAAAAABGJnABAAAAGJnABQAAAGBkAhcAAACAkQlcAAAAAEYmcAEAAAAYmcAFAAAAYGQCFwAAAICRCVzY1KpqS1WdXVWfqaodVXVtVX2iqp5ZVQeu8dhHVtVLq+oLVXVDVV1dVR+tqidXVS2j/i5V9aqquqKqvl1V36iqi6vqtLWMC9ZqM143VfW6quplLAesZXywFutx7VTVD1XVT1fVOVX1rqr62tQ/77+8guP4zmFT2ozXje8cNrt1um6Oqapfr6rzq+rvh7/Tbhi+N95cVY9Y5nHW9N9IfL/q7o0eA8xUVccm+XCSrcOunUn2T3Lr4fOnk5zQ3des4tjHJ7k4ye2GXTuS3CbJwhfvxUlO7e7vLFF/UpLzkxw87LouyaHZFWK+NsmT2gXGHrZZr5uqel2SM5N8O8m1c05zTHd/b6Vjg7Var2tn+I/D1y7R/MTuft0yjuE7h01ps143vnPYzNbjuqmqf57kH5JMByI7h88HTe17TZKnLvXP/Vr/G4kfZIYLm9LwfxwuyORfRF9L8sjuPiSTPzYfn2R7kvsleeMqjn14kndl8i+Szyd5QHdvSXJIkqcluSnJo5Kcu0T9nZK8bRjLJUmO6+7Dkxye5Jyh2xOTPHulY4O12MzXzZS3dvdRcxZ/+LLHree1M7gqyUVJXpjkZ1c4Nt85bEqb+bqZ4juHTWUdr5v9MwlXPpBJ2HjMcNxDk9wryTuHfr+S5OwlxjbW33pM626LZdMtSZ6UpIflX81o//mp9hNWeOwXDHU7k9xpRvtvD+3fTXK3Ge1/PrR/LckPzWh/1dB+bZIjNvp3abnlLJv8unnd0P66jf49WSyLl3W+dvafsW/hWL+8jHrfOZZNuWzy68Z3jmVTLut13WQSwv/4nPbKJMDsTEKd28zos6a/9SyzFzNc2KzOHNYf6u6Pz2h/S5Irhu0nrPDYC/3f0t1XzGh/eSbT5/ZPcsZ0Q1UdkmThfvlXdve3ZtT/4bA+LMljVzg2WItNed3AXmDdrp1ew/9B953DJrcprxvY5Nbluunua7v7U3PaO5PbiZLJrJd7zOjmb711IHBh06mqg5M8ePh40aw+w7803jN8PHEFxz4uyR13c+wdST66xLEfkl33QS5Vvy3JZSsdG6zFJr9uYNNaz2tnBL5z2JQ2+XUDm9ImuG6+PbW9/6Kx+VtvnQhc2IzukV3/bH52Tr+FtqOq6rbLPPa9Z9TPO/Y911h/r2WOC9ZqM183006oqsuHt6xcNzyd/9yq+tFljgXGtp7Xzlr5zmGz2szXzTTfOWwmG33dPGxYfyfJ5YvaxvxbjykCFzajo6e2vzqn33Tb0Uv2WtuxD6uqQ2fUX9PdNyyjfrnjgrXazNfNtB9JcudM7g8+OJMv+N9K8tmqOmuZ44Exree1s1a+c9isNvN1M813DpvJhl03wwPYf234+Nbuvm6NY5v3tx5TBC5sRlumtnfO6TfdtmXJXuMee8uM9nn1yx0XrNVmvm6S5FOZPOF+a5Jbd/dtM3nmxGlJvpTkwCT/papOC+xZ63ntrJXvHDarzXzdJL5z2Jw25LqpqoOSnJ9J6PjNJM/dLGO7JThg910AYG26+z/P2LczyV9V1f+b5BNJ7pTkpVX1V8M9zACwYr5zYGJ4DfVfJDk+k9c6n9HdV27sqG5ZzHBhM9o+tX3wnH7TbduX7DXusbfPaJ9Xv9xxwVpt5utmru7+xyR/MHw8Nsn9llsLI1jXf77XyHcOm9Vmvm7m8p3DBtqj101V7Z/kTZm8we67SX6hu9+7GcZ2SyJwYTOaTl2PmdNvum25Se1Kj33d8ETuxfVHDNPzdlcvQWZP2czXzXJMvxrxziushbVYz2tnrXznsFlt5utmOXznsBH22HUzhC1vTPJzSb6X5Be7+y9HHNtq/ta7RRK4sBldluTmYfvec/ottF3V3Vcv89jTT91ezrE/93/bu/coyYr6gOPfX5angEREQAXZKB5FCSAgIKIeQUWiBFGRRcCQKISH4fggRnyQjcSoOXrQoMSoIGKQ+IiuDxREgyiiiDwEFwVRViDxgQqIvBaXX/6oauYyO919d+b2TO/O93NOna57b92q6jt7p6d/W7dqhucvbdkvaabG+b6Rxtko752Z8jNH42qc7xtpXM3KfdMY2bKIiWDLJ4ec5t96I2LARWOnPmP77br5/KnKREQA+9TNfkPjpnIdcOOQujcAntGn7ouA3koR/c7fmrLs26r2TZq2Mb9v2ti9kb9hGudL0zLie2em/MzRWBrz+6YNP3M062bjvqnBlk8ABzERbPmvFqfOxt9685IBF42rj9XXZ0fEblMcP5CJIaBntq20TorWK78oIhZOUexYYEPKL6mzJp1/J/DfdfPoiNh4ivP/ob7eASxp2zepA2N539Q/HvqKiE2AN9XNm4Ar2vZN6shI7p2Z8jNHY24s7xs/czTmRnbfNEa2vIwyZ8shLYMtM/5bTwNkpsk0domygtZVQAI3A3vX/X9C+UV0ez325SnOXVyPJbBwiuMbA7+ox5cCO9f96wBHA/fWY6f26dufAX+oZb4JPL7u3wA4kTJUMIE3zPV1NM2vNK73DXAY8FnKcpybNfavT5nI7dpG2wfN9XU0zb80ynunltl0UuqVf/Wk/Q+Z4lw/c0xjmcb1vvEzxzTOaVT3DbAAOLseuw84cBp9m9F3JFOf6zrXHTCZ+iVgIWWYZ+8Xy52UodW97cuBh01xXpsP8Z0p69D3yv0eWN7YPg9Yd0Df/qL2p1f+Nkokubd9OhBzfQ1N8y+N430DHN4ok5Qvj7+ZdM/cAxwz19fPNH/TiO+dbJkW9znfzxzTWKZxvG/8zDGNexrFfQM8s3FsOfDLIWnKYCMz/I5kWjn5SJHGVmYuA7YH3kaZyKkXsb0MOB7YPTNvnWbdlwFPBk4GfgKsTflldxFwBLBvZt474Pwv1759GFgGrAfcCpwPvDQz/ybrby1pNo3pfXMB8GbgS8BPa382pnyIXwq8C9g2M0+dTr+kLozy3umgb37maCyN6X3jZ47G2ojum+b3+rWBzYekKVe+m+l3JK0s/HyWJEmSJEnqliNcJEmSJEmSOmbARZIkSZIkqWMGXCRJkiRJkjpmwEWSJEmSJKljBlwkSZIkSZI6ZsBFkiRJkiSpYwZcJEmSJEmSOmbARZIkSZIkqWMGXCRJkiRJkjpmwEWSJEmSJKljBlwkSZIkSZI6ZsBFkiRJkiSpYwZcJEnSvBERyyIiI2LZXPdlnMyn6xIRS+p7/eII6j601n1bRGzWdf2SpNWLARdJkgRA/aKYEZEtyu4eEe+PiMsi4rcRcV9E3BURN0XENyLifRFxcEQ8oov2tGoi4vCIWBwRi+e6L+MkIvYB9q+bi0fQxNnAdcDGwDtGUL8kaTUSmf6NI0mSSgCkl8/M6FNmY+A04CUtq01go8y8czrtda2O4Nga+HlmLpyNNudCRHwDeBa0u7bz4bpERABXADsAX8jM/YecMt12DgPOBO4HtsvMH42iHUnS+FtrrjsgSZJWDxGxNnAesFvddR/wBeAi4BdAAFsATwGeAzyq7puVYIqmb00NskzyEkqwBeBfRtjOJ4CTKAGsfwQWjbAtSdIYM+AiSZLaOpaJYMsyYN/M/PFUBetogj2Aoyj/0y/NtdfX12sz85JRNZKZKyLiLOBNwEsj4jGZeeOo2pMkjS/ncJEkSW0d0sgf3S/YApDFtzPzsMy8axb6JvUVETsCu9fNs2ahyV4bC4AjZ6E9SdIYMuAiSZLaemIjf+Gc9aKPiNg0It4REddExJ0R8buIuDQijo+Ih7Sso/VqPcPKRsQZjYmBF9Z9L46IL0TEjRGxfPKEwRGxfkQcEBEfiIhLGhMS3x4RSyPi3yNih6naq+d/o9b5rMa+nCItnsH7fl5EfDwiflYnSr4jIn4cER+MiJ2HnLuw0Ycz6r5N6wS/V9e67oiIyyPihLY/txYOa+SHBlwiYqf6fq6OiN/Xn8Gv67+tcyPirRHx+H7nZ+Y1wJV189A64kuSNM/4SJEkSWprQSP/CGBsHpOIiKdR5pPZtLH7IcAuNR0eES+Yi75V60bEZ4EDhpS7Blg4xf6HAk+q6aiIeEdmvqnbLg4WERtS5ifZb4rDT6jpyIg4BXhtZg59lCwidgGWAI+edOgpNb0sIvbOzN/NqPMTKxPdmJk/G9KnxcCJrDz30CNq2hbYB9gZeNGAqi4AdqTM5bIDEwEYSdI8YcBFkiS19VNgu5r/O+Dv57AvD4iIbYBzKUEJgKspq8TcBDwSOBjYFfgUsPZc9BE4GdiXcg0/DlxLCQg9a1K59YHfAedTVtT5X8rkxI8GdgJeRnkPJ0TErzPzvZPOfwsl6PTPwJPrvqmCPH0fB5tKRCwAvgLsWXfdBpwOXE75e3JP4BXAOsBx9X0Me5RmK+AcYBPKqJMLgD9QgkrHAg+nBCzeW+ueloh4LPC4uvm9IWX3p0x0C3A3ZZnn71J+JusBW1ICeM9t0fR3G/l9MOAiSfOOARdJktTW2cDba/74+kjFR4ALM/OOuesWH2Qi2PJR4MjM/GPvYES8D3g38Lo56FvPvsCngUMzc3lj/+mTyh0OfK3Z/6aIeDMluPRE4G0RcVrz2mfmRbXcaxr7lnTQ/+OZCLZcC+yVmf/XOP6xiDgV+BolgHJERHw+M88ZUOdelMDNnpMnsa2PG10O/Cnw8oh446T2VsVujfywyXKPqK8rgOdk5sVTFYqI9YDth9TVbGu3vqUkSWss53CRJEltncyDv0TuD3wRuK3ObXFmRBwTEU+arQ7VyVD3rpvXAUdNDlZkZlICBgNHN4zYzcBfTwq2rCQzz+0XbKnHfw4cUzc3YuJRmZGJiHWA19bNPwIHThX8yMwrgL9t7DqhRfXHTbViUGbeAHygbi5g4mc8Hds18tcOKbtNfV3aL9gCkJn3ZObAf0/1Z3V33RwWnJEkrYEMuEiSpFYy827KqISTgXsah/6EMq/FYZQvyUsj4gcRceAsdKv5uMwp/QIaNejynlnoTz+nZ+adHdXVDATMxsiJPYDNa/4rmXl1v4KZ+Rng+rr59IjYbEC9t1DmhOnnfxr5mQTxtm7kh80F01tRa8uI2HgGbfbcWl+3cuJcSZp/DLhIkqTWMvOuzHwdZS6Lo4DPUOYZmWx74FMR8bGIGOXfG09t5L8+pOyw46P0rbYFI2KzurLSVyPi5rri0gMrDPHgYNeW3Xd1Jbs28l9tUf78Rn5QQOj7mbliwPHmv6uHtWi3n00a+WEBl17fNwEujIiDI+Khg04Y4rf1dR1ggxnUI0laDTmHiyRJWmWZ+VvgP2oiIrYAdgeeBxzCxJwqr6CMeDhpRF15VCN/fd9SlD5HxG2UeUFm21RBqZVExEGUa9p2dMVMggFtPbKRv65F+WaZR/YtBb8ZUs+9jfx6LdrtZ91GfthcQ+8EXkgZUbMDZQTOioi4Evg2ZWLf8+porzZ+38ivT5kUWJI0TzjCRZIkzVhm/jIzl2TmMcBjgYsah98QEeuPqOkN6+sfM/O+FuW7eqxnVQ39gh4Rz6R8we8FWy4H/pUyL8oiyuNTvdTTXKp7VDZq5Ntcv2ZQYaO+pWDostEdaQZuBgaoMvNWSuDw7cCv6u4FlCWgjwM+B/wqIt5W57YZphk4axukkSStIRzhIkmSOlVHkhwM3ED5W2NDymMpF46gud6X+7UiYu0WQZcuH+vo+j+uFjfqPDIzPzxVoYiY7UdTmqNC2rS9YSM/l6tX9TQfI9qkb6mqrvr0log4kTLK5emUFZr2piy5vRHwVmDXiNi3zg/UT6+95cxdsE+SNEcc4SJJkjqXmTfz4EdLHtWv7Aw1V8vZpm8pICIezvDHiXqjIQaOXqgToA798t5WHS3xjLr5/X7BlmrrAcdG4ReN/ONblG+Wme5Szl1a1si3/pll5v2ZeUVmvj8zF1EmDj6AiQDOPsALhlTTa+/GIYEZSdIayICLJEkaleaKQaOau6K5NO9eQ8q2WVr4tvq6aUSsPaDcdnQ7WubhTIw8/umQsvu0qO+Bx3U6WB2neY2f26J8s8xcLsXd88NG/gnTraQGYJYAJzZ279mvfEQsZGLumaum264kafVlwEWSJLUSEZsPL/VA2YXAnzd2XdN1f6rPNfKv7hckqUGH17aor9fPtZkYcTKV49p1r7W7GvnH9SsUERvR7n00A1wzDQxdDPyy5l8QEX2XaI6IFzMxwuWizPz1DNvuwiWNfBfLaC9r5Ac9nt9s65K+pSRJaywDLpIkqa1LI+IjEbHLoEIRsSVluejehK7fycxhozamJTN/AHytbj4RODUiHjSRbA22vIsyGeow5zbyJ0XEupMLRMSrgFdNr8dTy8zbgZ/UzV0i4oDJZSJiQ+DTwFYtqryhkd9phn1bDpxcN9cCPh0RK60+FBHbU1etqt45k3a7kpk3MDFqaNdBZSPiQxGx3YDjawFHNHb9YEB1zYDLecP6KUla8zhpriRJamsd4JXAKyPieuCbwJXALZRHWDYHnga8iLIELpSRFseMuF9HA5dRVqB5FWUy0zOBm4AtgJdTvvx+D9iSwfPJLKEsL70NsAclyHQaZS6SLSjvbS/gW5SRKF3OTXMK8G81/5mIOIuy2tMdlEeYDq/tnUlZbnuQrzMxCue0iDgZ+Dmwou67PjMHLqM9yXuA/SiP0DwJWBoRp1NWUlqLMrHsXzGxBPOHM/OcVah/1JYArwe2iojHZubP+pQ7AjgiIpZSloD+IWXOlg0oq28tYmIEz3WUwGI/z66vy2pgUJI0zxhwkSRJbV0FPAcISkBi4CS1wFLg8My8cpSdyszrI2Jf4POUVWS2B949RV8OpASJBtV1b0QsAs4HHkZ5LOq9k4pdArwEuHTmvX+Q91MCQ4dQRiEfVlPT54GjGB5wOYcSrNmT8nP6wKTj/0RZFamVzFxRr/HZwAsp1+b1UxWt7+M1beueJR9nor+HACcNKf/kmvq5Ctg/M6dc6jkitgV2rJv/uQr9lCStQXykSJIktZKZzwMeQxnl8lFKwOEWyuS491FGAlwBnAb8JbBjZn5/lvp2MbAt5TGWHwN3UybAvQx4A7BrZt7Ysq7LKIGWUyijXe4BbgW+AxwLPCMzbxnBe8jMPJQyIueC2v/lwM3Al4CDMvNF/b7kT6prBWXy2jfWft/KxOiW6fbvD5m5H/B84BOUETP3UJY7vg74EPDUzDwuM+/vX9PsqyNMLq6bhw4o+mjKv+8zKKN3etftbsrcLZ+jBGx2ysxlA+rptbECGLTilCRpDRauUCdJkqQ1XZ0X57N1c4/M/M6I2llACdQtBD5Zl5SWJM1DjnCRJEnSfLCEMucQwAkjbOdgSrDlfsqjW5KkecqAiyRJktZ4WYZ1v7Fu7hcRO3fdRh3d8pa6eUZm/qjrNiRJqw8DLpIkSZoXMvM8ysTDsAqTBq+Cg4EnALcz2lE0kqTVgHO4SJIkSZIkdcwRLpIkSZIkSR0z4CJJkiRJktQxAy6SJEmSJEkdM+AiSZIkSZLUMQMukiRJkiRJHTPgIkmSJEmS1DEDLpIkSZIkSR0z4CJJkiRJktQxAy6SJEmSJEkdM+AiSZIkSZLUMQMukiRJkiRJHTPgIkmSJEmS1DEDLpIkSZIkSR0z4CJJkiRJktSx/weXICo3ZJ4oVAAAAABJRU5ErkJggg==","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"image/png":{"height":413,"width":558},"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["def compute_single_neuron_isis(spike_times, neuron_idx):\n","  \"\"\"Compute a vector of ISIs for a single neuron given spike times.\n","\n","  Args:\n","    spike_times (list of 1D arrays): Spike time dataset, with the first\n","      dimension corresponding to different neurons.\n","    neuron_idx (int): Index of the unit to compute ISIs for.\n","\n","  Returns:\n","    isis (1D array): Duration of time between each spike from one neuron.\n","  \"\"\"\n","  #############################################################################\n","  # Students: Fill in missing code (...) and comment or remove the next line\n","  # raise NotImplementedError(\"Exercise: compute single neuron ISIs\")\n","  #############################################################################\n","\n","  # Extract the spike times for the specified neuron\n","  single_neuron_spikes = spike_times[neuron_idx]\n","\n","  # Compute the ISIs for this set of spikes\n","  # Hint: the function np.diff computes discrete differences along an array\n","  isis = np.diff(single_neuron_spikes)\n","\n","  return isis\n","\n","# Uncomment the following lines when you are ready to test your function\n","single_neuron_isis = compute_single_neuron_isis(spike_times, neuron_idx=283)\n","plt.hist(single_neuron_isis, bins=50, histtype=\"stepfilled\")\n","plt.axvline(single_neuron_isis.mean(), color=\"orange\", label=\"Mean ISI\")\n","plt.xlabel(\"ISI duration (s)\")\n","plt.ylabel(\"Number of spikes\")\n","plt.legend()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"W8GmZDFV6JjP"},"source":["---\n","\n","In general, the shorter ISIs are predominant, with counts decreasing rapidly (and smoothly, more or less) with increasing ISI. However, counts also rapidly decrease to zero with _decreasing_ ISI, below the maximum of the distribution (8-11 ms). The absence of these very low ISIs agrees with the refractory period hypothesis: the neuron cannot fire quickly enough to populate this region of the ISI distribution.\n","\n","Check the distributions of some other neurons. To resolve various features of the distributions, you might need to play with the value of `n_bins`. Using too few bins might smooth over interesting details, but if you use too many bins, the random variability will start to dominate.\n","\n","You might also want to restrict the range to see the shape of the distribution when focusing on relatively short or long ISIs. *Hint:* the third argument to `plt.hist` sets the interval to define bins over."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"bns9w278zVx6"},"source":["---\n","\n","# Section 4: What is the functional form of an ISI distribution?"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"AxGOkl-AfvFs"},"source":["The ISI histograms seem to follow continuous, monotonically decreasing functions above their maxima. The function is clearly non-linear. Could it belong to a single family of functions?\n","\n","To motivate the idea of using a mathematical function to explain physiological phenomena, let's define a few different function forms that we might expect the relationship to follow: exponential, inverse, and linear."]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{},"colab_type":"code","executionInfo":{"elapsed":622,"status":"ok","timestamp":1594632176398,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"uaJcbGPv-1AX"},"outputs":[],"source":["def exponential(xs, scale, rate, x0):\n","  \"\"\"A simple parametrized exponential function, applied element-wise.\n","\n","  Args:\n","    xs (np.ndarray or float): Input(s) to the function.\n","    scale (float): Linear scaling factor.\n","    rate (float): Exponential growth (positive) or decay (negative) rate.\n","    x0 (float): Horizontal offset.\n","\n","  \"\"\"\n","  ys = scale * np.exp(rate * (xs - x0))\n","  return ys\n","\n","def inverse(xs, scale, x0):\n","  \"\"\"A simple parametrized inverse function (`1/x`), applied element-wise.\n","\n","  Args:\n","    xs (np.ndarray or float): Input(s) to the function.\n","    scale (float): Linear scaling factor.\n","    x0 (float): Horizontal offset.\n","\n","  \"\"\"\n","  ys = scale / (xs - x0)\n","  return ys\n","\n","def linear(xs, slope, y0):\n","  \"\"\"A simple linear function, applied element-wise.\n","\n","  Args:\n","    xs (np.ndarray or float): Input(s) to the function.\n","    slope (float): Slope of the line.\n","    y0 (float): y-intercept of the line.\n","\n","  \"\"\"\n","  ys = slope * xs + y0\n","  return ys"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"AQbn-rpageDC"},"source":["### Interactive Demo: ISI functions explorer\n","\n","Here is an interactive demo where you can vary the parameters of these functions and see how well the resulting outputs correspond to the data. Adjust the parameters by moving the sliders and see how close you can get the lines to follow the falling curve of the histogram. This will give you a taste of what you're trying to do when you *fit a model* to data.\n","\n","\"Interactive demo\" cells have hidden code that defines an interface where you can play with the parameters of some function using sliders. You don't need to worry about how the code works – but you do need to **run the cell** to enable the sliders.\n"]},{"cell_type":"code","execution_count":29,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/","height":654,"referenced_widgets":["4f2a3d9cab764453ac605b41f1b7e54e","96665b69c8e5411a9d1210a66e3f88ca","f6d7ae314c574863bbc22107e685c025","284c8c5ecbac4360b469276d893234fc","50325d0d3b20454b8efa3c26b2d29b90","09ea6e063c234f458b4254e22572533e","7cdbd91c2e5d4cce976a0f177c036adb","5586f87ba5a446e8a88ca82fa1564501","a84730c9e0c24e82bee4322a5e9ae0de","7e47ab895d4f4837a22be4b4f730aa32","92ba8f075e194605b36da11dd3eaf333","4b5128f28ea74899beb0b4fd1edb4ee0","b7edddfe469c44a0bd55d02a77835466","5037e2db83bd48209d2a921c4396f321","009f27f91ec645fca128a49db99a82b5","e8c07b2075d1443fb9544a3af53435cc","e6d1f7cda68b4a33ada857021f7cee15","d12c6f6d4b544784afdbb383c401e913","f8b812486cae4bc5966b61f974bef3d9","7b801fb883b14f0eafc71c8548fc28a5","e1f45c871923480383653c39918ac53a","71ecb2b1d8f645229166ffe3331fa963","2a2e9b2859c943d7802cd7d5c5b385fb","5eee97e92c174a4fba77f8403caf4e62","5547d58405b342f38b3a432091cb1407"]},"colab_type":"code","executionInfo":{"elapsed":1214,"status":"ok","timestamp":1594632186260,"user":{"displayName":"Evgenii Tretiakov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggu3c2KuDxkCviEiF6RedytMTB3sHW4G95B3lqboQ=s64","userId":"14040943026517255518"},"user_tz":-120},"id":"NGIGUXtV9Y9v","outputId":"4444e687-48e4-47e6-cdec-ae0d1360ff75"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4f2a3d9cab764453ac605b41f1b7e54e","version_major":2,"version_minor":0},"text/plain":["interactive(children=(FloatSlider(value=1000.0, description='exp_scale', max=20000.0, step=250.0), FloatSlider…"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["#@title\n","\n","#@markdown Be sure to run this cell to enable the demo\n","# Don't worry about understanding this code! It's to setup an interactive plot.\n","single_neuron_idx = 283\n","single_neuron_spikes = spike_times[single_neuron_idx]\n","single_neuron_isis = np.diff(single_neuron_spikes)\n","\n","counts, edges = np.histogram(\n","  single_neuron_isis,\n","  bins=50,\n","  range=(0, single_neuron_isis.max())\n",")\n","\n","functions = dict(\n","  exponential=exponential,\n","  inverse=inverse,\n","  linear=linear,\n",")\n","\n","colors = dict(\n","  exponential=\"C1\",\n","  inverse=\"C2\",\n","  linear=\"C4\",\n",")\n","\n","@widgets.interact(\n","  exp_scale=widgets.FloatSlider(1000, min=0, max=20000, step=250),\n","  exp_rate=widgets.FloatSlider(-10, min=-200, max=50, step=1),\n","  exp_x0=widgets.FloatSlider(0.1, min=-0.5, max=0.5, step=0.005),\n","  inv_scale=widgets.FloatSlider(1000, min=0, max=3e2, step=10),\n","  inv_x0=widgets.FloatSlider(0, min=-0.2, max=0.2, step=0.01),\n","  lin_slope=widgets.FloatSlider(-1e5, min=-6e5, max=1e5, step=10000),\n","  lin_y0=widgets.FloatSlider(10000, min=0, max=4e4, step=1000),\n",")\n","def fit_plot(\n","  exp_scale=1000, exp_rate=-10, exp_x0=0.1,\n","  inv_scale=1000, inv_x0=0,\n","  lin_slope=-1e5, lin_y0=2000,\n","):\n","  \"\"\"Helper function for plotting function fits with interactive sliders.\"\"\"\n","  func_params = dict(\n","    exponential=(exp_scale, exp_rate, exp_x0),\n","    inverse=(inv_scale, inv_x0),\n","    linear=(lin_slope, lin_y0),\n","  )\n","  f, ax = plt.subplots()\n","  ax.fill_between(edges[:-1], counts, step=\"post\", alpha=.5)\n","  xs = np.linspace(1e-10, edges.max())\n","  for name, function in functions.items():\n","    ys = function(xs, *func_params[name])\n","    ax.plot(xs, ys, lw=3, color=colors[name], label=name);\n","  ax.set(\n","      xlim=(edges.min(), edges.max()),\n","      ylim=(0, counts.max() * 1.1),\n","      xlabel=\"ISI (s)\",\n","      ylabel=\"Number of spikes\",\n","  )\n","  ax.legend()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"rI0h02Scdt6g"},"source":["# Summary\n","\n","In this tutorial, we loaded some neural data and poked at it to understand how the dataset is organized. Then we made some basic plots to visualize (1) the average level of activity across the population and (2) the distribution of ISIs for an individual neuron. In the very last bit, we started to think about using mathematical formalisms to understand or explain some physiological phenomenon. All of this only allowed us to understand \"What\" the data looks like.\n","\n","This is the first step towards developing models that can tell us something about the brain. That's what we'll focus on in the next two tutorials."]}],"metadata":{"colab":{"collapsed_sections":[],"name":"Copy of NeuromatchAcademy_W1D1_Tutorial1","provenance":[{"file_id":"https://github.com/NeuromatchAcademy/course-content/blob/master/tutorials/W1D1_ModelTypes/student/W1D1_Tutorial1.ipynb","timestamp":1594627592210}],"toc_visible":true},"kernelspec":{"display_name":"Python 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