Backprop Alternative: Augmented Lagrangian Predictive Coding
pub.sakana.ai
pub.sakana.ai
I really liked this paper that showed using a predictive coding learning rule leads to the exact same gradients as backprop in arbitrary networks:
Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs https://direct.mit.edu/neco/article/34/6/1329/110646/Predict...
It also pointed at the possibility that the learning algorithms brain uses might be, like the paper has demonstrated, less compute-optimal and data-optimal than backprop - but far easier to implement in a localized, distributed fashion. Because backprop requires activation storage and global connectivity - and both "hot" data storage and connectivity of any kind are extremely expensive for the brain.
“ Probably the most ambitious and all-encompassing version of the ‘Bayesian turn’ in cognitive science is the free energy principle (FEP). The FEP is a mathematical framework, developed by Karl Friston and colleagues (Friston, Kilner, and Harrison 2006; Friston et al. 2010; Friston 2010; Friston et al. 2017a; Friston 2019), which specifies an objective function that any self-organizing system needs to minimize in order to ensure adaptive exchanges with its environment. One major appeal of the FEP is that it aims for (and seems to deliver) an unprecedented integration of the life sciences (including psychology, neuroscience, and theoretical biology). The difference between the FEP and earlier inferential theories (e.g., Gregory 1980, Grossberg 1980, Rao and Ballard 1999, Lee and Mumford 2003) is that not only perceptual processes, but also other cognitive functions such as learning, attention, and action planning can be subsumed under one single principle: the minimization of free energy through the process of active inference (Friston 2010; Friston et al. 2017). ”
I think the remarkable result of this paper is that they add a local Lagrange multiplier λ at each layer, which accumulates constraint/prediction error over the inference dynamics.
At equilibrium, in the linear case, those local multipliers converge to exactly the same gradient signal that backpropagation would calculate globally
Now what is Predictive coding: its a network that can minimize prediction errors through local recurrent interactions instead of an explicit global backward pass.
Now I am making the leap to Fristons more philosophical and mathematical work not the paper - so that is me making the allusion
But a light bulb moment for me dawned when I read it
This process (PC-ALM) gives us a concrete example of how globally coherent inference/credit assignment can emerge from purely local dynamical interactions.
PC-ALM lets a recurrent dynamical system relax toward a state in which the backprop gradient is represented locally throughout the network.
That distinction is potentially important for neuroscience.
A brain doesn’t obviously have a central routine saying:
loss.backward()
it certainly has recurrent neural populations whose states continuously influence neighboring populations.
This paper is demonstrating that, at least mathematically, those sorts of local recurrent dynamics can generate the same credit information that backprop obtains through the chain rule. The authors explicitly motivate predictive coding as a biologically plausible local-learning alternative because standard BP requires globally coordinated error variables and update ordering.
Think about it also give plausible evolutionary to chain intelligence through cells coming together and creating nested networks
This has got to be how the neurological intelligence sausage gets made
What it eventually means for ML I’m Not sure but hopeful it opens a door
- can it be used to relax timing order requirement in pipeline parallelism? Each node update lambda on communication, and optimize weights at other time. - given that BP is using SGD, can batches and T share the same timeline in optimization, while keeping the descent direction?
Is a man a father or a son? It's a false dilemma, it can be both.
Is a training step pretraining or finetuning? It's the same mathematical operation, with the only difference the intention or reason of applying the training step.
They say in the paper what it's about: mostly just scientific curiousity but such approaches might be useful for making DNNs more energy efficient via neuromorphic hardware in future.
For continual learning at the weight level there's the business model issue. The labs are already deep in the red, the last thing they want is to give up shared weights. The I/O and storage costs of that would make it infeasible. Already KV caches are a sort of dynamic 'fast weights' and those are expensive!
Nothing but economics.
Inference requires dramatically more memory, meaning it gets radically more expensive, if you are doing backprop alongside. This is why it is not done, despite the obvious advantages. If this doesn't require storing as much data for updates, it might make such systems more feasible.
Okay, so no efficiency improvement?
How does it do on CIFAR-10, or even better, ImageNet?
Interesting research, not sure it's a backprop alternative.
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EDIT: accuracy on MNIST is not ~90%. It's ~85%.
I didn't see ImageNet. TinyImageNet is something else.
Training a Predictive Coding Network on ImageNet using Equilibrium Propagation Tugdual Kerjan, Rasmus Høier, Benjamin Scellier https://arxiv.org/abs/2606.03584
It's quite an undertaking.
The obvious example is if it has different behaviour around local minima, it could be an altenate pathway out.
I have often wondered if doing training with radically different aproaches for the first few iterarions would avoid any method specific artifacts before the weights had time to denoise.
But the submission title calls it a "backprop alternative." It is not, at least not yet.
Their goal is to understand how distributed systems which cannot do backprop (the brain) can still do learning.
If we puke, the brain will not do general aversive learning, it will learn to avoid specifically the last thing eaten, because it instinctively knows about food poisoning.
Imprinting is absolutely fascinating. Some newborn animals will run a very simple pattern detector like looking for a red dot or something and use that to bootstrap their conception of their parent.
For fully general learning I have a hunch that it can be done using local history plus a semi-global reward scalar (global neurotransmittor levels).
For another, there are about 200k promotor regions (including non-coding) in the human genome.
A promotor region might have say 6 to 15 bits of information.
Can you compress 2025 or even 2024 era LLM intelligence into 3 megabit = ~400 kB ? I think not. I think a lot of compression is still possible, but 400 kB?
So I think we can box up the idea of "dirty secrets of the braing: not learning but hard coding". There is a lot of hard coding in biology, but brains are evolved specifically to enable learning within the individual lifetime instead of only learning by natural selection.
I also don't buy the following argument:
> If we puke, the brain will not do general aversive learning, it will learn to avoid specifically the last thing eaten, because it instinctively knows about food poisoning.
Each time it happens that I end up puking, I do feel aversion and try to avoid puking at all, sometimes I succeed but sometimes is just puke. There must be fundamental puke reflexes (which one fails to avoid) and avertable puke reflexes.
There are a few extra levels of interpretation (like protein synthesis) that are more like a transpiler than compression (imo), over a 4-base language that is read in a sliding window and is affected by surrounding conditions, so the same "token" sequence may produce different things depending on external factors. Some biologists I used to collaborate with talked about 7 layers to this process, I have only described one level here
> There are a few extra levels of interpretation (like protein synthesis) that are more like a transpiler than compression (imo), over a 4-base language that is read in a sliding window and is affected by surrounding conditions, so the same "token" sequence may produce different things depending on external factors. Some biologists I used to collaborate with talked about 7 layers to this process, I have only described one level here
so the same "token" sequence may produce different things depending on external factors.
yes, non-hereditary learning depends on external factors, thank you for paraphrasing me while shifting attention.
the multi-scale nature (transpilers etc.) doesn't change the theorems in probability and information theory which seriously constrain the maximum amount of information a message can store.
The whole point of a brain is that it is an organ dedicated to storing, retrieving and timely utilising information one can't afford to store in a genome.
I was never paraphrasing you, but thank you for attempting rhetorical antics?
I'm not convinced my molecules follow the laws of thermodynamics.
1. A determinist transformation can't create more information than its inputs
you can't derive (2)
2. Later stages don't contain any new information.
without restraining your model to a monotonic chain of transformations that can't tap into the information content of the environment.
We instinctively know that there are other intelligent beings, and we have the the machinery to model them. We are born with the capacity for language. It must still be learned, the specific words aren't hardcoded, but the concept of language is. We are born with the capacity to store and replay memories. The brain knows some aspects of how the world is supposed to look like visually, and if it doesn't it will try to correct that. People who used optics to see the world upside down have found that after a brief time their brain learned to flip the world right side up.
I think my initial assessment is correct: interesting research, but not an alternative, at least not yet.