Reddit developer's Hebbian AI learns without backpropagation, shows emergent self-healing
A Hebbian model uses only 5-7% of parameters and recovers from damage naturally…
A Reddit developer (u/Antiqueity_Camp) built a Hebbian architecture AI model that does not use backpropagation or gradients. The substrate started as a 1000k (1 million) neuron model and scaled to 100k between versions. During CIFAR-10 training (50 epochs), the substrate settles using only 5–7% of total parameters, with connections emerging "naturally." Two emergent behaviors appeared: accuracy dips followed by jumps that exceed previous best scores, and intentional damage to active pathways that leads to recovery nearly matching epoch 1's baseline—then surpassing it. All runs were done on an RTX 3060 12GB.
- Model uses Hebbian learning with no backpropagation or gradients, relying on emergent connection formation.
- Substrate uses only 5-7% of its total parameters after training on CIFAR-10 for 50 epochs.
- Two emergent behaviors: accuracy dips-then-Jumps, and recovery from targeted damage that surpasses baseline.
Why It Matters
This suggests more efficient, self-healing AI systems could emerge from biologically plausible architectures without massive compute.