diff --git a/experiments/mnist/increasing_inhibition.py b/experiments/mnist/increasing_inhibition.py
index 75a9b82..47ad03c 100644
--- a/experiments/mnist/increasing_inhibition.py
+++ b/experiments/mnist/increasing_inhibition.py
@@ -4,6 +4,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+import torchvision.datasets.mnist as vision_mnist
 from time import time as t
 from sklearn.metrics import confusion_matrix
 from scipy.spatial.distance import euclidean
@@ -11,7 +12,7 @@
 from bindsnet.datasets import MNIST
 from bindsnet.network import Network
 from bindsnet.encoding import poisson
-from bindsnet.network import load_network
+from bindsnet.network import load
 from bindsnet.learning import PostPre, NoOp
 from bindsnet.network.monitors import Monitor
 from bindsnet.network.topology import Connection
@@ -96,7 +97,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
         network.add_connection(input_exc_conn, source='X', target='Y')
         network.add_connection(recurrent_conn, source='Y', target='Y')
     else:
-        network = load_network(os.path.join(params_path, model_name + '.pt'))
+        network = load(os.path.join(params_path, model_name + '.pt'))
         network.connections['X', 'Y'].update_rule = NoOp(
             connection=network.connections['X', 'Y'], nu=network.connections['X', 'Y'].nu
         )
@@ -104,15 +105,17 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
         network.layers['Y'].theta_plus = 0
 
     # Load MNIST data.
-    dataset = MNIST(data_path, download=True)
+    dataset = vision_mnist.MNIST(root=data_path, download=True)
 
     if train:
-        images, labels = dataset.get_train()
+        images = dataset.train_data
+        labels = dataset.train_labels
     else:
-        images, labels = dataset.get_test()
+        images = dataset.test_data
+        labels = dataset.test_labels
 
     images = images.view(-1, 784)
-    images *= intensity
+    images = images * intensity
 
     # Record spikes during the simulation.
     spike_record = torch.zeros(update_interval, int(time / dt), n_neurons)
@@ -232,7 +235,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
         inpts = {'X' : sample}
 
         # Run the network on the input.
-        network.run(inpts=inpts, time=time)
+        network.run(inputs=inpts, time=time)
 
         retries = 0
         while spikes['Y'].get('s').sum() < 5 and retries < 3:
@@ -243,7 +246,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
             network.run(inpts=inpts, time=time)
 
         # Add to spikes recording.
-        spike_record[i % update_interval] = spikes['Y'].get('s').t()
+        spike_record[i % update_interval] = spikes['Y'].get('s').squeeze(1)
 
         # Optionally plot various simulation information.
         if plot:
@@ -261,7 +264,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
 
             plt.pause(1e-8)
 
-        network.reset_()  # Reset state variables.
+        network.reset_state_variables()  # Reset state variables.
 
     print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
 
@@ -354,7 +357,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
     # Parameters.
     parser = argparse.ArgumentParser()
     parser.add_argument('--seed', type=int, default=0)
-    parser.add_argument('--n_neurons', type=int, default=100)
+    parser.add_argument('--n_neurons', type=int, default=625)
     parser.add_argument('--n_train', type=int, default=60000)
     parser.add_argument('--n_test', type=int, default=10000)
     parser.add_argument('--c_low', type=float, default=1)
@@ -389,6 +392,7 @@ def main(seed=0, n_neurons=100, n_train=60000, n_test=10000, c_low=1, c_high=25,
     progress_interval = args.progress_interval
     update_interval = args.update_interval
     plot = args.plot
+    plot = True
     train = args.train
     gpu = args.gpu