Hebbian architecture AI model [R]

Reddit r/MachineLearning 05/23/26, 01:21 PM Papers

hebbian-learning emergent-behavior no-backprop cifar-10 neuromorphic unsupervised-learning side-project

Summary

A side project presents a Hebbian architecture AI model that avoids backpropagation and gradients, achieving 50 epochs on CIFAR-10 with emergent behaviors like accuracy dips followed by jumps and recovery after targeted damage.

Hello , for some time now i have been hooked on a side project after work hours, these are the results for a Hebbian architecture AI model. The model does not use backpropagation or gradients, the substrate started as a 1000k neuron and scaled to 100k between versions. The results bellow are results from 50epochs training with CIFAR 10 the results are bellow. Note that the substrat is not a fixed model the connections between neurons emerge "naturally" during training and the substrat settled using inly 5%-7% of the total parameter count. There are 2 distinct behaviors that were not designed but rather emerged from the architecture, 1: the model experiences slight dips on acc followed by jumps that exceeds the best previews score, after the full training the substart is intentionally damaged targeting the active neurons and pathways and than enter a session of recovery that almost achives baseline acc from epoch 1 , and than proceeds on surpassing the baseline acc. Every run has been made on a consumer GPU RTX 3060 12gb vram

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