Reimagining Neural Networks: Using Predictive Coding Modules over Traditional Backpropagation to Model Neural Circuitry
Image by Trenton Systems
Written by Shree Bhattacharya
Imagine a world where artificial intelligence (AI) learns as intuitively as the human brain. This article delves into predictive coding, an innovative approach that offers a compelling, biologically inspired alternative to traditional backpropagation for neural network models. Unlike backpropagation, which trains networks by tweaking their settings whenever they make a mistake, predictive coding suggests that our brains work by constantly guessing what will happen next and then adjusting those guesses when the outcomes differ from what was expected. Hence, instead of merely fixing a mistake after it happens, predictive coding focuses on fine-tuning predictions to minimize future mismatches. This AI model was tested across multiple neural network topologies, focusing on learning speed, accuracy, adaptability, and alignment with biological processes. The findings indicate that predictive coding is more efficient than backpropagation, requiring less computational power and training while offering better generalization to new contexts. Throughout this article, Shree begs the question of whether predictive coding could revolutionize how we develop intelligent systems and potentially work towards bridging the gap between human intelligence and AI.
To be continued…
