Update course book

neuromatch · Jul 13, 2024 · 77cc124 · 77cc124
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diff --git a/_images/fd7702a2e831d720d3905a58c1f4760f2e067597ac07c43e7f261a3f1153ba78.png b/_images/fd7702a2e831d720d3905a58c1f4760f2e067597ac07c43e7f261a3f1153ba78.png
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diff --git a/_sources/projects/docs/project_guidance.md b/_sources/projects/docs/project_guidance.md
@@ -52,7 +52,7 @@ Split into groups. We recommend intentionally creating groups with diverse skill
 
 * Introductions (30 min = 2 min/student): say a few things about yourself, then about your research area or research interests. What are you really curious about that you might explore in your NMA project?
 Listen carefully as others talk about their interests.
-* Individual reading time (50 min): browse the project templates with slides and code 
+* Individual reading time (50 min): browse the project templates, videos, slides and code.
 * Now brainstorm within your group (60 min). Definitely choose a topic and start thinking about concrete questions if you can. Make sure the topic you choose is well suited for answering the broad range of questions you are interested in. Try to come up with one or a few topics of interest, either by yourselves or directly from the booklet (i.e., project templates).
 
 **Tips:**
@@ -62,13 +62,12 @@ The exploratory work you do in Week 1 will culminate in a project proposal on W1
 
 If you feel you lack research structure and scientific approach to make your experience smooth and organized, we welcome you to review the Modeling Practice guideline prepared for CN & DL courses by this [link](https://compneuro.neuromatch.io/tutorials/W2D1_ModelingPractice/student/W2D1_Intro.html) (you might want to watch an intro lecture and go together through the tutorial).
 
-You might want to just surf the Internet to find any relevant papers that match the project direction and your ideas to prepare for the next day's discussion.
+You might want to just surf the Internet to find any relevant papers that match the project direction and your ideas to prepare for the next day's discussion. You can also start going through green questions from the template.
 
 ### W1D3
 
 **Literature review - read 1-2 papers for your project topic, share findings with team members to create a short literature review with key questions and potential areas of investigation**
 
-* Split into groups.
 * Short discussion (30 min): tell your team members what paper(s) you will read with a short description.
 * Individual reading time (60 min): Pick 1-2 papers 
 * Now present within your group (60 min). Tell your group about the paper you read, including: 1) introduction to a topic, 2) approaches used in the paper, 3) main results of the paper, 4) initial ideas on how you will extend the paper in a new direction (can be rough and unpolished!), 5) conclusions.

diff --git a/...rces/tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/student/W1D3_Tutorial2.ipynb b/...rces/tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/student/W1D3_Tutorial2.ipynb
@@ -292,7 +292,7 @@
     "\n",
     "        fig.subplots_adjust(right=0.9)\n",
     "        cbar_ax = fig.add_axes([1.01, 0.18, 0.01, 0.53])\n",
-    "        cbar_ax.text(-1.6, 0.05, 'Normalized euclidean distance', size=12, rotation=90)\n",
+    "        cbar_ax.text(-2.3, 0.05, 'Normalized euclidean distance', size=10, rotation=90)\n",
     "        fig.colorbar(ax_, cax=cbar_ax)\n",
     "\n",
     "        plt.show()\n",
@@ -370,7 +370,7 @@
     "        cbar_ax = fig.add_axes([-0.01, 0.2, 0.01, 0.5])\n",
     "        #cbar_ax.text(-7, 0.05, 'dissimilarity between rdms', size=10, rotation=90)\n",
     "        fig.colorbar(ax_, cax=cbar_ax,location='left')\n",
-    "        ax.set_title('Dissimilarity between layer rdms')\n",
+    "        ax.set_title('Dissimilarity between layer rdms', fontdict = {'fontsize': 14})\n",
     "        ax.set_xticks(np.arange(len(ax_ticks)), labels=ax_ticks, fontsize=7, rotation=83)\n",
     "        ax.set_yticks(np.arange(len(ax_ticks)), labels=ax_ticks, fontsize=7)\n",
     "        [t.set_color(i) for (i,t) in zip(tick_colors, ax.xaxis.get_ticklabels())]\n",
@@ -384,7 +384,7 @@
     "\n",
     "            path_indices = np.arange(path_len[i], path_len[i+1])\n",
     "            ax.plot(dims[path_indices, 0], dims[path_indices, 1], color=path_colors[i], marker='.')\n",
-    "            ax.set_title('Representational geometry path')\n",
+    "            ax.set_title('Representational geometry path', fontdict = {'fontsize': 14})\n",
     "            ax.set_xlim([amin, amax])\n",
     "            ax.set_ylim([amin, amax])\n",
     "            ax.set_xlabel(f\"dim 1\")\n",
@@ -602,6 +602,7 @@
     "        data, target = data.to(device), target.to(device)\n",
     "        optimizer.zero_grad()\n",
     "        output = model(data)\n",
+    "        output = torch.log(output)  # to make it a log_softmax\n",
     "        loss = F.nll_loss(output, target)\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
@@ -629,6 +630,7 @@
     "        for data, target in test_loader:\n",
     "            data, target = data.to(device), target.to(device)\n",
     "            output = model(data)\n",
+    "            output = torch.log(output)\n",
     "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
     "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
     "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
@@ -826,6 +828,7 @@
     "        img.requires_grad = True\n",
     "\n",
     "        output = model(img)\n",
+    "        output = torch.log(output)\n",
     "        loss = F.nll_loss(output, target)\n",
     "\n",
     "        model.zero_grad()\n",
@@ -851,6 +854,7 @@
     "    \"\"\"\n",
     "    correct = 0\n",
     "    output = model(imgs)\n",
+    "    output = torch.log(output)\n",
     "    pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
     "    correct += pred.eq(targets.view_as(pred)).sum().item()\n",
     "\n",
@@ -1405,7 +1409,7 @@
     "\n",
     "$$\\hat M(\\text{layer i}, \\text{image j x image k})$$\n",
     "\n",
-    "We can then calculate the distances between the different rows of this new matrix. It's like an RDM matrix of the RDM matrices! This new measure of distance captures the representational geometries of different layers,  not the representations themselves.\n",
+    "We can then calculate the distances between the different rows of this new matrix. This new measure captures the distances between representational geometries of different layers. So we are now creating an RDM matrix of the RDM matrices!\n",
     "\n",
     "We compute the similarity between geometries using the cosine dissimilarity. The resulting matrix has the dimensions of `#layers` by `#layers`.\n",
     "\n",

diff --git a/projects/docs/project_guidance.html b/projects/docs/project_guidance.html
@@ -1161,20 +1161,19 @@ <h3>W1D1 - W1D2<a class="headerlink" href="#w1d1-w1d2" title="Permalink to this
 <ul class="simple">
 <li><p>Introductions (30 min = 2 min/student): say a few things about yourself, then about your research area or research interests. What are you really curious about that you might explore in your NMA project?
 Listen carefully as others talk about their interests.</p></li>
-<li><p>Individual reading time (50 min): browse the project templates with slides and code</p></li>
+<li><p>Individual reading time (50 min): browse the project templates, videos, slides and code.</p></li>
 <li><p>Now brainstorm within your group (60 min). Definitely choose a topic and start thinking about concrete questions if you can. Make sure the topic you choose is well suited for answering the broad range of questions you are interested in. Try to come up with one or a few topics of interest, either by yourselves or directly from the booklet (i.e., project templates).</p></li>
 </ul>
 <p><strong>Tips:</strong></p>
 <p>No need to have a very concrete project after the first meeting. You will determine the feasibility of your questions as you start to dig deeper, and you will likely change your question completely. That’s how research works!
 The exploratory work you do in Week 1 will culminate in a project proposal on W1D4.</p>
 <p>If you feel you lack research structure and scientific approach to make your experience smooth and organized, we welcome you to review the Modeling Practice guideline prepared for CN &amp; DL courses by this <a class="reference external" href="https://compneuro.neuromatch.io/tutorials/W2D1_ModelingPractice/student/W2D1_Intro.html">link</a> (you might want to watch an intro lecture and go together through the tutorial).</p>
-<p>You might want to just surf the Internet to find any relevant papers that match the project direction and your ideas to prepare for the next day’s discussion.</p>
+<p>You might want to just surf the Internet to find any relevant papers that match the project direction and your ideas to prepare for the next day’s discussion. You can also start going through green questions from the template.</p>
 </section>
 <section id="w1d3">
 <h3>W1D3<a class="headerlink" href="#w1d3" title="Permalink to this heading">#</a></h3>
 <p><strong>Literature review - read 1-2 papers for your project topic, share findings with team members to create a short literature review with key questions and potential areas of investigation</strong></p>
 <ul class="simple">
-<li><p>Split into groups.</p></li>
 <li><p>Short discussion (30 min): tell your team members what paper(s) you will read with a short description.</p></li>
 <li><p>Individual reading time (60 min): Pick 1-2 papers</p></li>
 <li><p>Now present within your group (60 min). Tell your group about the paper you read, including: 1) introduction to a topic, 2) approaches used in the paper, 3) main results of the paper, 4) initial ideas on how you will extend the paper in a new direction (can be rough and unpolished!), 5) conclusions.</p></li>

diff --git a/projects/project-notebooks/ComparingNetworks.html b/projects/project-notebooks/ComparingNetworks.html
@@ -1225,7 +1225,7 @@ <h3>Install and import feedback gadget<a class="headerlink" href="#install-and-i
 <h3>Project Background<a class="headerlink" href="#project-background" title="Permalink to this heading">#</a></h3>
 <div class="cell tag_remove-input docutils container">
 <div class="cell_output docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "0b78648cda9246fb991dd2f9fca34745"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "fc6d864974c044508ad178b646ebc335"}</script></div>
 </div>
 </section>
 <section id="submit-your-feedback">
@@ -1244,7 +1244,7 @@ <h3>Submit your feedback<a class="headerlink" href="#submit-your-feedback" title
 </div>
 </details>
 <div class="cell_output docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "40b6833b0c484cf4bb73c8c31ac6db06"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "f8c5a4ad86ca4673ad1661e37ca86913"}</script></div>
 </div>
 </section>
 <section id="project-slides">
@@ -1307,7 +1307,7 @@ <h3>Submit your feedback<a class="headerlink" href="#id1" title="Permalink to th
 </div>
 </details>
 <div class="cell_output docutils container">
-<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "233fee5f78fc4a989dd1089f08c7efd1"}</script></div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "482a9e580879438abe8653f39fbebe0e"}</script></div>
 </div>
 <p>In this notebook, we are going to provide code to get you started on Q1-Q3 of this project!</p>
 <p>The basic outline looks like this:</p>
@@ -1497,7 +1497,7 @@ <h3>Submit your feedback<a class="headerlink" href="#id2" title="Permalink to th
 </div>
 </details>
 <div class="cell_output docutils container">
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 <p>Above, we are plotting the inputs and outputs of the 3BFF task. One trial is 500 time steps, each with a 1% probability of getting an “up” or “down” pulse on each of its 3 input channels. When the task receives an “up” pulse, the state corresponding to that input channel moves from zero to one (if possible), and if a state at one receives a “down” pulse, it goes to zero. In this way, this system acts as 3 bits of memory, encoding 8 potential system states (2^3 states). We add noise to the inputs of the system so that it better reflects realistic computations that a neural circuit might perform.</p>
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 <p>Now that we can see the basic logic of the task, let’s do a basic overview of what task training is!</p>
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