diff --git a/DOCUMENTATION.md b/DOCUMENTATION.md
index e8d6c0bd..89e50006 100644
--- a/DOCUMENTATION.md
+++ b/DOCUMENTATION.md
@@ -90,7 +90,7 @@ Allows composing multiple encodings. The following example replicates the Neural
 
 From NeRF [[Mildenhall et al. 2020]](https://www.matthewtancik.com/nerf). Works better than OneBlob encoding if the dynamic range of the encoded dimension is high. However, suffers from stripe artifacts.
 
-The number of encoded dimensions is twice the specified number of frequencies for each input dimension.
+The number of encoded dimensions is twice the specified number of frequencies for each input dimension. E.g. with `n_frequencies == 4`, an input dimension `x` becomes `sin(πx), cos(πx), sin(2πx), cos(2πx), sin(4πx), cos(4πx), sin(8πx), cos(8πx)`.
 
 ```json5
 {
diff --git a/include/tiny-cuda-nn/encodings/frequency.h b/include/tiny-cuda-nn/encodings/frequency.h
index 938b89e9..c041b652 100644
--- a/include/tiny-cuda-nn/encodings/frequency.h
+++ b/include/tiny-cuda-nn/encodings/frequency.h
@@ -65,17 +65,15 @@ __global__ void frequency_encoding(
 	 *     frequency-encoded input dimension 0
 	 *     frequency-encoded input dimension 1
 	 *     frequency-encoded input dimension ...
-	 *     passthrough inputs
 	 *     padding (value 1.f)
 	 */
 	if (j >= fan_out_encoded) {
 		data_out(j, i) = 1;
 	} else {
 		/* Layout of encoded features (e.g. when inputs abcd.. are XYZ positions):
-		 *     sin(a.x), cos(a.x) sin(2pi a.x), cos(2pi a.x) sin(4pi a.x) ...
-		 *     sin(a.y), cos(a.y) sin(2pi a.y), cos(2pi a.y) sin(4pi a.y) ...
-		 *     sin(a.z), cos(a.z) sin(2pi a.z), cos(2pi a.z) sin(4pi a.z) ...
-		 *     (passthrough features)
+		 *     sin(pi a.x), cos(pi a.x) sin(2pi a.x), cos(2pi a.x) sin(4pi a.x) ...
+		 *     sin(pi a.y), cos(pi a.y) sin(2pi a.y), cos(2pi a.y) sin(4pi a.y) ...
+		 *     sin(pi a.z), cos(pi a.z) sin(2pi a.z), cos(2pi a.z) sin(4pi a.z) ...
 		 *     (padding)
 		 */
 		const uint32_t encoded_input_feature_i = j / (n_frequencies * 2);