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Artificial intelligence has made remarkable progress in recent decades, but many of its foundations are still loosely inspired by biology. Artificial neural networks (ANNs), the backbone of machine learning and deep learning, were originally modeled after the human brain. Early examples, such as the perceptron in the 1950s, attempted to capture the way neurons fire and interact. Although modern ANNs now contain billions of parameters and power everything from voice assistants to generative models, their connection to biological processes has largely weakened over time.
This raises an important question: can revisiting biology help AI progress even further? Natural systems have evolved efficient, adaptive mechanisms to learn and solve problems—offering potential insights for making AI models more capable and efficient.
Why Look Back at Biology?
While backpropagation and gradient descent have proven to be powerful tools in AI, they do not closely resemble how the brain learns. Biological systems rely on decentralized feedback, adaptive plasticity, and mechanisms that work efficiently under energy and memory constraints. Many researchers suspect that adopting some of these principles could lead to AI systems that are more efficient and robust while maintaining high accuracy.
Three areas of research are particularly relevant:
- Alternatives to backpropagation
- New network structures
- Different activation and attention mechanisms
Rethinking Learning Rules
Since the 1980s, backpropagation has been the dominant method for training neural networks. Yet, it suffers from two biological plausibility issues: the weight transport problem (no known biological equivalent of transferring weights backward through layers) and the update-locking problem (biological neurons update asynchronously rather than waiting for a full signal pass).
To address these, researchers have turned to biologically inspired approaches.
Hebbian Learning is one of the earliest and simplest ideas, built on the principle that “neurons that fire together, wire together.” While elegant, naive implementations of Hebbian learning lead to runaway growth, making it unstable. Modifications like Oja’s Rule or spike-timing-dependent plasticity introduce mechanisms to limit this growth and better mirror brain activity. Recent experiments suggest that combining Hebbian principles with gradient descent—rather than replacing it entirely—can yield competitive results, though not yet surpassing traditional methods.
Another direction is Feedback Alignment (FA), introduced in 2016, which replaces exact backward weight calculations with fixed random feedback signals. This avoids the weight transport issue altogether. Variants like Direct Feedback Alignment push this further by simplifying how gradients are passed through layers. Interestingly, FA methods sometimes approach or even outperform backpropagation in specific setups, though results are highly dependent on network design and dataset choice.
Some researchers have experimented with combining Hebbian updates and FA, blending two biologically inspired strategies. While still early, these hybrid methods suggest that integrating multiple biological mechanisms could open new pathways for AI training.
Beyond Accuracy: Efficiency and Practicality
One of the most compelling reasons to explore biology-inspired AI is efficiency. Studies have shown that models built with biologically grounded principles can reduce memory demands, shorten training times, and lower energy use—valuable advantages for deploying AI on resource-limited hardware. Even if accuracy does not always surpass backpropagation, gains in efficiency may make these methods highly attractive for industry applications.
What Comes Next
Backpropagation remains the workhorse of deep learning, but it is not necessarily the final word. The brain offers countless examples of learning strategies that differ from traditional machine learning approaches. Spiking neural networks, graph-based architectures, and attention mechanisms inspired by biological processes are already demonstrating their potential.
The future may not lie in replacing existing AI methods with purely biological ones, but rather in combining the best of both worlds. By integrating biological insights into network structures, learning algorithms, and activation functions, researchers could design systems that are not only powerful but also more efficient, adaptable, and sustainable.
Conclusion
Nature has spent millions of years perfecting intelligence through evolution, and AI research has only just begun to scratch the surface of what is possible. While backpropagation and other current methods dominate today, biologically inspired alternatives hold promise for shaping the next era of AI. By learning from the brain and other natural systems, researchers can continue to push AI beyond its current limits—toward models that are smarter, faster, and closer to the efficiency of biological intelligence.
