diff --git a/Backprop/source.txt b/Backprop/source.txt
index 17dd772..04e9719 100644
--- a/Backprop/source.txt
+++ b/Backprop/source.txt
@@ -100,7 +100,7 @@ $$ a_k^l = \sigma(z_k^l) $$
 
 Now finally calculate the input sum of a neuron $m$ in layer $l+1$.
 
-$$ z_m^{l+1} = \sum_{k} w_{mk}^{l+1} a^l_k + b_m^l $$
+$$ z_m^{l+1} = \sum_{k} w_{mk}^{l+1} a^l_k + b_m^{l+1} $$
 
 Here we have gone forward one step in the layers, from the activations in layer $l-1$ to the input sums of neurons in layer $l+1$. An error function C is defined using one example from our training data, and its derivative is calculated with respect to a single weight in layer $l$.