add custom surrogate gradient section to documentation

docs/snntorch.surrogate.rst

@@ -7,7 +7,7 @@ The discrete nature of spikes makes it difficult for ``torch.autograd`` to calcu
 
 Alternative gradients are also available in the :mod:`snntorch.surrogate` module. 
 These represent either approximations of the backward pass or probabilistic models of firing as a function of the membrane potential.
-
+Custom, user-defined surrogate gradients can also be implemented.
 
 At present, the surrogate gradient functions available include:
 
@@ -67,7 +67,53 @@ Example::
 
     net = Net().to(device)
 
+Custom Surrogate Gradients
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+For flexibility, custom surrogate gradients can also be defined by the user
+using `custom_surrogate`.
+
+
+Example::
+
+   import snntorch as snn
+   from snntorch import surrogate
+   import torch
+   import torch.nn as nn
+
+   beta = 0.9
+
+   # Define custom surrogate gradient
+    def custom_fast_sigmoid(input_, grad_input, spikes):
+        ## The hyperparameter slope is defined inside the function.
+        slope = 25
+        grad = grad_input / (slope * torch.abs(input_) + 1.0) ** 2
+        return grad
+
+    spike_grad = surrogate.custom_surrogate(custom_fast_sigmoid)
+
+   # Define Network
+   class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    # Initialize layers
+        self.fc1 = nn.Linear(num_inputs, num_hidden)
+        self.lif1 = snn.Leaky(beta=beta, spike_grad=spike_grad)
+        self.fc2 = nn.Linear(num_hidden, num_outputs)
+        self.lif2 = snn.Leaky(beta=beta, spike_grad=spike_grad)
+
+    def forward(self, x, mem1, spk1, mem2):
+        cur1 = self.fc1(x)
+        spk1, mem1 = self.lif1(cur1, mem1)
+        cur2 = self.fc2(spk1)
+        spk2, mem2 = self.lif2(cur2, mem2)
+
+        return mem1, spk1, mem2, spk2
+
+    net = Net().to(device)
 
+    
 .. automodule:: snntorch.surrogate
    :members:
    :undoc-members: