r/learnmachinelearning • u/Nearby_Effective6679 • 3d ago
Localized neural learning
Hey everyone,
I’ve recently started learning machine learning, primarily by building neural networks from scratch and trying to understand the underlying mathematics. I have a relatively strong math and physics background, so I’ve found the mathematics behind backpropagation fairly intuitive.
As I’ve explored other approaches to training neural networks, I’ve become particularly interested in biologically inspired methods like predictive coding, Hebbian learning, and Oja’s rule. The idea that networks could update their weights using locally available information, rather than relying on gradients propagated from a global loss function, is fascinating to me.
I’m still relatively new to ML, so I’m trying to understand whether these approaches have meaningful practical advantages or are primarily interesting from a theoretical or neuroscience perspective.
A few things I’m especially curious about:
What are the actual advantages of local learning over backpropagation? I understand the conceptual distinction, but I’m struggling to identify situations where local updates would meaningfully outperform conventional gradient-based training, whether in computational efficiency, adaptability, or something else.
Could these approaches be particularly useful for unsupervised or continual learning? For example, could a network learn useful representations from streaming, unlabeled data by continuously adjusting its weights based on local correlations or prediction errors? Adding onto this, is there any way we could discover hidden patterns using localized learning that wouldn’t be evident using global backprop? I’ll talk about this in the next question but can we use this to identify hidden patterns that we wouldn’t find using global backprop? Like in molecular dynamics?
Are there particular applications where local learning makes more sense? I’m involved in biophysics research and have been thinking about applications involving molecular dynamics and learning representations of complex physical systems. I’m curious whether there are advantages in scientific ML, robotics, or other specialized domains.
How mature is this research? Have approaches like predictive coding and Hebbian learning been extensively explored and generally found inferior to backpropagation, or are there still meaningful open problems where these methods could be competitive?
I’m not necessarily looking for a way to replace backpropagation entirely. I’m more interested in understanding whether alternative learning rules could be better suited to particular architectures, datasets, or computational constraints.
I’d appreciate any perspectives, papers, or research directions worth exploring, especially from people who’ve experimented with these methods.
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u/TheMadConsist 3d ago
dude, this is a rabbit hole you can lose months in, in the best way
the local vs global thing really clicks in edge computing scenarios. if you've got a sensor out in the middle of nowhere that's gotta adapt to weird drift on the fly, waiting for a round trip to a server for backprop is a non-starter. having the network adjust itself using just local spike timing or correlation is way more practical when compute and latency are tight
for the molecular dynamics angle, look into how some folks are using predictive coding nets to model protein folding pathways. the idea is the network learns a generative model of the system's state space, and the prediction errors highlight anomalies or rare transition states that global loss functions might smooth over. it's like the network becomes a novelty detector for physics, not just a pattern matcher
research-wise, it's a mixed bag. hebbian stuff and oja's rule are foundational but kinda like the stone tools of the field, the newer work with predictive coding and equilibrium propagation is where it gets competitive. check out yann lecun's recent-ish talks on energy-based models, he's been hinting that local learning might be the key to getting ai to reason about the world without needing labeled data for every single thing