Train a neural network for too long, and it “forgets.” Feed it noisy data, and accuracy collapses. Push the gradients too far, and learning becomes unstable. Unlike our biological brain, which sustains itself for decades while learning continuously, an artificial network often needs constant babysitting—dropout, batch normalization, early stopping, learning rate tuning.

This fragility inspired our research: can AI learn to stabilize and repair itself, just like the human brain?

Learning from Biology

The human brain is not just a learning machine—it is also a healing system. Neurons regulate their own activity, prune weak connections, and repair damaged pathways. This process, known as homeostasis, keeps the brain stable and functional even under stress.

We asked: what if artificial neurons could do the same?

The BioLogicalNeuron Layer

In our recent work, published in Scientific Reports (Nature Portfolio), we introduced a new neural network layer called BioLogicalNeuron, inspired directly by brain mechanisms.

It has three key features:

1. Calcium-based monitoring – Just like real neurons track calcium levels to gauge activity, our artificial neurons monitor their “health” during training.

2. Homeostatic assessment – Instead of waiting for failure, the layer continuously evaluates stability in real-time.

3. Adaptive repair – When instability is detected, the network heals itself by scaling down overactive pathways, reinforcing strong ones, and pruning weak ones.

In other words, the network doesn’t just learn—it self-repairs.

Why This Matters

This self-healing ability makes AI:

• More reliable in critical areas like healthcare, robotics, and autonomous vehicles.

• More efficient on edge devices (IoT, mobile), where resources are limited.

• More adaptable, capable of handling noisy or incomplete data without collapsing.

Our experiments showed not only improved stability, but also surprising gains in accuracy and generalization across molecular, graph, and image datasets.

A Step Toward Resilient AI

Biological intelligence has thrived for millions of years because it can regulate and repair itself. By borrowing this principle, we believe artificial intelligence can become more robust, adaptive, and sustainable.

The BioLogicalNeuron layer is just a first step—but it shows that AI can heal itself, and that the future of neural networks may look a lot more like the human brain than we ever imagined.

Read the full paper here: https://doi.org/10.1038/s41598-025-09114-8