Update README.md

README.md

@@ -43,7 +43,7 @@ To adapt these biological learning rules to modern hardware without cheating by
 Despite lacking global error gradients and relying on raw un-smoothed SGD, the model successfully learns the geometric shapes of the MNIST dataset and can classify and reconstruct them purely through local feature extraction.
 
 * **Hardware:** Trained natively on NVIDIA GPUs utilizing Numba/CuPy.
-* **Performance:** **74.14% Accuracy** on the Validation Set (achieved over 50 total epochs).
+* **Performance:** **87.90% Accuracy** on the Validation Set (achieved over 150 total epochs).
 * **Generative Capability:** The Karl Decoder successfully hallucinates and reconstructs recognizable MNIST digits based solely on the latent representation passed from the Hinton Encoder.
 
 ## Notes for the future