+ spike field coherence

Mark-Kramer · Nov 13, 2024 · 732e154 · 732e154
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diff --git a/.DS_Store b/.DS_Store
diff --git a/Coherence_Lab_Part_1.ipynb b/Coherence_Lab_Part_1.ipynb
@@ -93,8 +93,8 @@
     "Yf = np.fft.fft(y - y.mean())           # Compute Fourier transform of y\n",
     "\n",
     "# Compute the spectra\n",
-    "Sxx = \"SOMETHING\"  # Spectrum of E1 trials\n",
-    "Syy = \"SOMETHING\"  # ... and E2 trials\n",
+    "Sxx = \"SOMETHING\"  # Spectrum of x\n",
+    "Syy = \"SOMETHING\"  # ... and y\n",
     "Sxy = \"SOMETHING\"  # ... and the cross spectrum\n",
     "\n",
     "# Compute the coherence.\n",
@@ -124,7 +124,7 @@
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    "nbconvert_exporter": "python",
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diff --git a/Coherence_Lab_Part_3.ipynb b/Coherence_Lab_Part_3.ipynb
@@ -374,7 +374,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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+   "version": "3.12.4"
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diff --git a/Slides/Coherence_Lecture_2.pdf b/Slides/Coherence_Lecture_2.pdf
diff --git a/docs/Analyzing_Rhythms_Lab_1.html b/docs/Analyzing_Rhythms_Lab_1.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Analyzing_Rhythms_Lab_2a.html b/docs/Analyzing_Rhythms_Lab_2a.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Analyzing_Rhythms_Lab_2b.html b/docs/Analyzing_Rhythms_Lab_2b.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Analyzing_Rhythms_Lab_3.html b/docs/Analyzing_Rhythms_Lab_3.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Backpropagation.html b/docs/Backpropagation.html
@@ -2,7 +2,7 @@
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@@ -228,7 +228,7 @@
 }
 
 // Store cell data
-globalThis.qpyodideCellDetails = [{"code":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd","options":{"fig-cap":"","warning":"true","out-width":"700px","message":"true","results":"markup","classes":"","fig-width":7,"dpi":72,"out-height":"","comment":"","context":"interactive","output":"true","label":"","autorun":"","read-only":"false","fig-height":5},"id":1},{"code":"df = pd.read_csv(\"https://raw.githubusercontent.com/Mark-Kramer/BU-MA665-MA666/master/Data/backpropagation_example_data.csv\")\n\n# Extract the variables from the loaded data\nin_true = np.array(df.iloc[:,0])  #Get the values associated with the first column of the dataframe\nout_true = np.array(df.iloc[:,1])  #Get the values associated with the second column of the dataframe","options":{"fig-cap":"","warning":"true","out-width":"700px","message":"true","results":"markup","classes":"","fig-width":7,"dpi":72,"out-height":"","comment":"","context":"interactive","output":"true","label":"","autorun":"","read-only":"false","fig-height":5},"id":2},{"code":"print(np.transpose([in_true, out_true]))","options":{"fig-cap":"","warning":"true","out-width":"700px","message":"true","results":"markup","classes":"","fig-width":7,"dpi":72,"out-height":"","comment":"","context":"interactive","output":"true","label":"","autorun":"","read-only":"false","fig-height":5},"id":3},{"code":"def sigmoid(x):\n    return 1/(1+np.exp(-x))     # Define the sigmoid anonymous function.\n\ndef feedforward(w, s0):         # Define feedforward solution.\n    # ... x1 = activity of first neuron,\n    # ... s1 = output of first neuron,\n    # ... x2 = activity of second neuron,\n    # ... s2 = output of second neuron,\n    # ... out = output of neural network.\n    return out,s1,s2","options":{"fig-cap":"","warning":"true","out-width":"700px","message":"true","results":"markup","classes":"","fig-width":7,"dpi":72,"out-height":"","comment":"","context":"interactive","output":"true","label":"","autorun":"","read-only":"false","fig-height":5},"id":4},{"code":"w     = [0.5,0.5]                  # Choose initial values for the weights.\nalpha = 0.01                    # Set the learning constant.\n\nK = np.size(in_true);\nresults = np.zeros([K,3])        # Define a variable to hold the results of each iteration.    \n\nfor k in np.arange(K):\n    s0     = in_true[k]          # Define the input,\n    target = out_true[k]         # ... and the target output.\n    \n    #Calculate feedforward solution to get output.\n    \n    #Update the weights.\n    w0 = w[0]; w1 = w[1];\n    w[1] = \"SOMETHING\"\n    w[0] = \"SOMETHING\"\n    \n    # Save the results of this step. --------------------------------------\n    # Here we save the 3 weights, and the neural network output.\n    # results[k,:] = [w[0],w[1],  out]\n\n# Plot the NN weights and error during training \n# plt.clf()\n# plt.plot(results[:,1], label='w1')\n# plt.plot(results[:,0], label='w0')\n# plt.plot(results[:,2]-target, label='error')\n# plt.legend()                       #Include a legend,\n# plt.xlabel('Iteration number');    #... and axis label.\n\n# Print the NN weights\n# print(results[-1,0:2])","options":{"fig-cap":"","warning":"true","out-width":"700px","message":"true","results":"markup","classes":"","fig-width":7,"dpi":72,"out-height":"","comment":"","context":"interactive","output":"true","label":"","autorun":"","read-only":"false","fig-height":5},"id":5}];
+globalThis.qpyodideCellDetails = [{"id":1,"options":{"autorun":"","read-only":"false","fig-width":7,"warning":"true","comment":"","context":"interactive","dpi":72,"label":"","out-width":"700px","message":"true","output":"true","fig-height":5,"classes":"","results":"markup","fig-cap":"","out-height":""},"code":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd"},{"id":2,"options":{"autorun":"","read-only":"false","fig-width":7,"warning":"true","comment":"","context":"interactive","dpi":72,"label":"","out-width":"700px","message":"true","output":"true","fig-height":5,"classes":"","results":"markup","fig-cap":"","out-height":""},"code":"df = pd.read_csv(\"https://raw.githubusercontent.com/Mark-Kramer/BU-MA665-MA666/master/Data/backpropagation_example_data.csv\")\n\n# Extract the variables from the loaded data\nin_true = np.array(df.iloc[:,0])  #Get the values associated with the first column of the dataframe\nout_true = np.array(df.iloc[:,1])  #Get the values associated with the second column of the dataframe"},{"id":3,"options":{"autorun":"","read-only":"false","fig-width":7,"warning":"true","comment":"","context":"interactive","dpi":72,"label":"","out-width":"700px","message":"true","output":"true","fig-height":5,"classes":"","results":"markup","fig-cap":"","out-height":""},"code":"print(np.transpose([in_true, out_true]))"},{"id":4,"options":{"autorun":"","read-only":"false","fig-width":7,"warning":"true","comment":"","context":"interactive","dpi":72,"label":"","out-width":"700px","message":"true","output":"true","fig-height":5,"classes":"","results":"markup","fig-cap":"","out-height":""},"code":"def sigmoid(x):\n    return 1/(1+np.exp(-x))     # Define the sigmoid anonymous function.\n\ndef feedforward(w, s0):         # Define feedforward solution.\n    # ... x1 = activity of first neuron,\n    # ... s1 = output of first neuron,\n    # ... x2 = activity of second neuron,\n    # ... s2 = output of second neuron,\n    # ... out = output of neural network.\n    return out,s1,s2"},{"id":5,"options":{"autorun":"","read-only":"false","fig-width":7,"warning":"true","comment":"","context":"interactive","dpi":72,"label":"","out-width":"700px","message":"true","output":"true","fig-height":5,"classes":"","results":"markup","fig-cap":"","out-height":""},"code":"w     = [0.5,0.5]                  # Choose initial values for the weights.\nalpha = 0.01                    # Set the learning constant.\n\nK = np.size(in_true);\nresults = np.zeros([K,3])        # Define a variable to hold the results of each iteration.    \n\nfor k in np.arange(K):\n    s0     = in_true[k]          # Define the input,\n    target = out_true[k]         # ... and the target output.\n    \n    #Calculate feedforward solution to get output.\n    \n    #Update the weights.\n    w0 = w[0]; w1 = w[1];\n    w[1] = \"SOMETHING\"\n    w[0] = \"SOMETHING\"\n    \n    # Save the results of this step. --------------------------------------\n    # Here we save the 3 weights, and the neural network output.\n    # results[k,:] = [w[0],w[1],  out]\n\n# Plot the NN weights and error during training \n# plt.clf()\n# plt.plot(results[:,1], label='w1')\n# plt.plot(results[:,0], label='w0')\n# plt.plot(results[:,2]-target, label='error')\n# plt.legend()                       #Include a legend,\n# plt.xlabel('Iteration number');    #... and axis label.\n\n# Print the NN weights\n# print(results[-1,0:2])"}];
 
 
 </script>

diff --git a/docs/Coherence_Lab_Part_1.html b/docs/Coherence_Lab_Part_1.html
@@ -2,7 +2,7 @@
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 <meta charset="utf-8">
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+<meta name="generator" content="quarto-1.5.57">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -238,8 +238,8 @@ <h2 class="anchored" data-anchor-id="compute-the-coherence">Compute the coherenc
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>Yf <span class="op">=</span> np.fft.fft(y <span class="op">-</span> y.mean())           <span class="co"># Compute Fourier transform of y</span></span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Compute the spectra</span></span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>Sxx <span class="op">=</span> <span class="st">"SOMETHING"</span>  <span class="co"># Spectrum of E1 trials</span></span>
-<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>Syy <span class="op">=</span> <span class="st">"SOMETHING"</span>  <span class="co"># ... and E2 trials</span></span>
+<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>Sxx <span class="op">=</span> <span class="st">"SOMETHING"</span>  <span class="co"># Spectrum of x</span></span>
+<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>Syy <span class="op">=</span> <span class="st">"SOMETHING"</span>  <span class="co"># ... and y</span></span>
 <span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>Sxy <span class="op">=</span> <span class="st">"SOMETHING"</span>  <span class="co"># ... and the cross spectrum</span></span>
 <span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Compute the coherence.</span></span>

diff --git a/docs/Coherence_Lab_Part_2.html b/docs/Coherence_Lab_Part_2.html
@@ -2,7 +2,7 @@
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diff --git a/docs/Coherence_Lab_Part_3.html b/docs/Coherence_Lab_Part_3.html
@@ -2,7 +2,7 @@
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