diff --git a/examples/sparse_cnn.ipynb b/examples/sparse_cnn.ipynb
index 408ffa4..c1f68c5 100644
--- a/examples/sparse_cnn.ipynb
+++ b/examples/sparse_cnn.ipynb
@@ -30,12 +30,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Uncomment the following line to install nupic.torch\n",
-    "#!pip install -e git+https://github.com/numenta/nupic.torch.git#egg=nupic.torch"
+    "!pip install -e git+https://github.com/numenta/nupic.torch.git#egg=nupic.torch"
    ]
   },
   {
@@ -196,7 +196,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nupic.torch.modules import KWinners2d, KWinners, SparseWeights, Flatten\n",
+    "from nupic.torch.modules import (\n",
+    "    KWinners2d, KWinners, SparseWeights, Flatten, rezeroWeights, updateBoostStrength)\n",
     "\n",
     "sparseCNN = nn.Sequential(\n",
     "    # Sparse CNN layer\n",
@@ -255,20 +256,55 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "sgd = optim.SGD(sparseCNN.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)\n",
+    "train(model=sparseCNN, loader=first_loader, optimizer=sgd, criterion=F.nll_loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After each epoch we rezero the weights to keep the initial sparsity constant during training. We also apply the boost strength factor after each epoch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "sparseCNN.apply(rezeroWeights)\n",
+    "sparseCNN.apply(updateBoostStrength)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test and print results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'accuracy': 0.978, 'loss': 0.06757185096740723, 'total_correct': 9780}\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'accuracy': 0.9782, 'loss': 0.06787856979370117, 'total_correct': 9782}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "sgd = optim.SGD(sparseCNN.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)\n",
-    "train(model=sparseCNN, loader=first_loader, optimizer=sgd, criterion=F.nll_loss)\n",
-    "results = test(model=sparseCNN, loader=test_loader, criterion=F.nll_loss)\n",
-    "print(results)"
+    "test(model=sparseCNN, loader=test_loader, criterion=F.nll_loss)"
    ]
   },
   {
@@ -280,28 +316,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'accuracy': 0.9856, 'loss': 0.04167602500915527, 'total_correct': 9856}\n",
-      "{'accuracy': 0.9868, 'loss': 0.040336785697937014, 'total_correct': 9868}\n",
-      "{'accuracy': 0.9872, 'loss': 0.03909029731750488, 'total_correct': 9872}\n",
-      "{'accuracy': 0.9874, 'loss': 0.037309212684631346, 'total_correct': 9874}\n",
-      "{'accuracy': 0.9876, 'loss': 0.037822017288208006, 'total_correct': 9876}\n",
-      "{'accuracy': 0.9877, 'loss': 0.03768303909301758, 'total_correct': 9877}\n",
-      "{'accuracy': 0.988, 'loss': 0.03783873291015625, 'total_correct': 9880}\n",
-      "{'accuracy': 0.9881, 'loss': 0.038252718925476076, 'total_correct': 9881}\n",
-      "{'accuracy': 0.9873, 'loss': 0.03846522216796875, 'total_correct': 9873}\n"
+      "{'accuracy': 0.9862, 'loss': 0.0412615779876709, 'total_correct': 9862}\n",
+      "{'accuracy': 0.9868, 'loss': 0.04029187545776367, 'total_correct': 9868}\n",
+      "{'accuracy': 0.9867, 'loss': 0.03934368209838867, 'total_correct': 9867}\n",
+      "{'accuracy': 0.9876, 'loss': 0.03759277114868164, 'total_correct': 9876}\n",
+      "{'accuracy': 0.9877, 'loss': 0.03777754402160644, 'total_correct': 9877}\n",
+      "{'accuracy': 0.9872, 'loss': 0.03784116630554199, 'total_correct': 9872}\n",
+      "{'accuracy': 0.9872, 'loss': 0.03829168930053711, 'total_correct': 9872}\n",
+      "{'accuracy': 0.9876, 'loss': 0.03837165260314941, 'total_correct': 9876}\n",
+      "{'accuracy': 0.9871, 'loss': 0.03919747161865234, 'total_correct': 9871}\n"
      ]
     }
    ],
    "source": [
     "for epoch in range(1, EPOCHS):\n",
     "    train(model=sparseCNN, loader=train_loader, optimizer=sgd, criterion=F.nll_loss)\n",
+    "    sparseCNN.apply(rezeroWeights)\n",
+    "    sparseCNN.apply(updateBoostStrength)\n",
     "    results = test(model=sparseCNN, loader=test_loader, criterion=F.nll_loss)\n",
     "    print(results)"
    ]
@@ -316,18 +354,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.05 : {'accuracy': 0.9842, 'loss': 0.04920544128417969, 'total_correct': 9842}\n",
-      "0.1 : {'accuracy': 0.9782, 'loss': 0.067373148727417, 'total_correct': 9782}\n",
-      "0.15 : {'accuracy': 0.9697, 'loss': 0.0976463264465332, 'total_correct': 9697}\n",
-      "0.2 : {'accuracy': 0.9524, 'loss': 0.15406905670166016, 'total_correct': 9524}\n",
-      "0.25 : {'accuracy': 0.9238, 'loss': 0.23606297912597657, 'total_correct': 9238}\n"
+      "0.05 : {'accuracy': 0.9841, 'loss': 0.05029125137329102, 'total_correct': 9841}\n",
+      "0.1 : {'accuracy': 0.9777, 'loss': 0.06853677139282227, 'total_correct': 9777}\n",
+      "0.15 : {'accuracy': 0.9697, 'loss': 0.09935387115478515, 'total_correct': 9697}\n",
+      "0.2 : {'accuracy': 0.9509, 'loss': 0.1598887924194336, 'total_correct': 9509}\n",
+      "0.25 : {'accuracy': 0.9225, 'loss': 0.2412475631713867, 'total_correct': 9225}\n"
      ]
     }
    ],
@@ -359,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/examples/sparse_linear.ipynb b/examples/sparse_linear.ipynb
index d14a4bf..0a70d03 100644
--- a/examples/sparse_linear.ipynb
+++ b/examples/sparse_linear.ipynb
@@ -30,8 +30,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "# Uncomment the following line to install nupic.torch\n",
@@ -188,7 +190,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nupic.torch.modules import KWinners, SparseWeights, Flatten\n",
+    "from nupic.torch.modules import (\n",
+    "    KWinners, SparseWeights, Flatten, rezeroWeights, updateBoostStrength)\n",
     "\n",
     "sparseNN = nn.Sequential(\n",
     "    Flatten(),\n",
@@ -244,53 +247,90 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "sgd = optim.SGD(sparseNN.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)\n",
+    "train(model=sparseNN, loader=first_loader, optimizer=sgd, criterion=F.nll_loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After each epoch we rezero the weights to keep the initial sparsity constant during training. We also apply the boost strength factor after each epoch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "sparseNN.apply(rezeroWeights)\n",
+    "sparseNN.apply(updateBoostStrength)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test and print results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'accuracy': 0.9506, 'loss': 0.16315889587402344, 'total_correct': 9506}\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'accuracy': 0.9486, 'loss': 0.16556934661865233, 'total_correct': 9486}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "sgd = optim.SGD(sparseNN.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)\n",
-    "train(model=sparseNN, loader=first_loader, optimizer=sgd, criterion=F.nll_loss)\n",
-    "results = test(model=sparseNN, loader=test_loader, criterion=F.nll_loss)\n",
-    "print(results)"
+    "test(model=sparseNN, loader=test_loader, criterion=F.nll_loss)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At this point the duty cycles should be stable and we can train on larger batch sizes"
+    "At this point the duty cycles should be stable and we can train the rest of the epochs on larger batch sizes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'accuracy': 0.9627, 'loss': 0.12201591415405273, 'total_correct': 9627}\n",
-      "{'accuracy': 0.9634, 'loss': 0.12190820617675781, 'total_correct': 9634}\n",
-      "{'accuracy': 0.9623, 'loss': 0.12345575714111329, 'total_correct': 9623}\n",
-      "{'accuracy': 0.9639, 'loss': 0.1185587173461914, 'total_correct': 9639}\n",
-      "{'accuracy': 0.9611, 'loss': 0.11994301071166992, 'total_correct': 9611}\n",
-      "{'accuracy': 0.9633, 'loss': 0.11600606689453125, 'total_correct': 9633}\n",
-      "{'accuracy': 0.9634, 'loss': 0.11699238586425781, 'total_correct': 9634}\n",
-      "{'accuracy': 0.9639, 'loss': 0.11530724716186523, 'total_correct': 9639}\n",
-      "{'accuracy': 0.9633, 'loss': 0.11797227020263672, 'total_correct': 9633}\n"
+      "{'accuracy': 0.9621, 'loss': 0.12309523391723633, 'total_correct': 9621}\n",
+      "{'accuracy': 0.9625, 'loss': 0.12451380462646484, 'total_correct': 9625}\n",
+      "{'accuracy': 0.9621, 'loss': 0.12468773880004883, 'total_correct': 9621}\n",
+      "{'accuracy': 0.9638, 'loss': 0.11706881561279296, 'total_correct': 9638}\n",
+      "{'accuracy': 0.9623, 'loss': 0.120688623046875, 'total_correct': 9623}\n",
+      "{'accuracy': 0.9645, 'loss': 0.11490174255371094, 'total_correct': 9645}\n",
+      "{'accuracy': 0.9648, 'loss': 0.1163398452758789, 'total_correct': 9648}\n",
+      "{'accuracy': 0.9643, 'loss': 0.1144802864074707, 'total_correct': 9643}\n",
+      "{'accuracy': 0.9657, 'loss': 0.11591439743041992, 'total_correct': 9657}\n"
      ]
     }
    ],
    "source": [
     "for epoch in range(1, EPOCHS):\n",
     "    train(model=sparseNN, loader=train_loader, optimizer=sgd, criterion=F.nll_loss)\n",
+    "    sparseNN.apply(updateBoostStrength)\n",
+    "    sparseNN.apply(rezeroWeights)\n",
     "    results = test(model=sparseNN, loader=test_loader, criterion=F.nll_loss)\n",
     "    print(results)"
    ]
@@ -305,18 +345,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.05 : {'accuracy': 0.9541, 'loss': 0.14802040100097658, 'total_correct': 9541}\n",
-      "0.1 : {'accuracy': 0.939, 'loss': 0.1916733642578125, 'total_correct': 9390}\n",
-      "0.15 : {'accuracy': 0.9094, 'loss': 0.2852293640136719, 'total_correct': 9094}\n",
-      "0.2 : {'accuracy': 0.8639, 'loss': 0.4125948455810547, 'total_correct': 8639}\n",
-      "0.25 : {'accuracy': 0.8043, 'loss': 0.5801907653808593, 'total_correct': 8043}\n"
+      "0.05 : {'accuracy': 0.9563, 'loss': 0.14633502197265624, 'total_correct': 9563}\n",
+      "0.1 : {'accuracy': 0.9406, 'loss': 0.19336406707763673, 'total_correct': 9406}\n",
+      "0.15 : {'accuracy': 0.9114, 'loss': 0.28374275512695313, 'total_correct': 9114}\n",
+      "0.2 : {'accuracy': 0.8656, 'loss': 0.4086779327392578, 'total_correct': 8656}\n",
+      "0.25 : {'accuracy': 0.8082, 'loss': 0.5770498291015626, 'total_correct': 8082}\n"
      ]
     }
    ],
@@ -348,7 +390,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,