Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Predictive coding has been unified with backpropagation

lesswrong.com

Post reply on HN
Print view View original

41–50 of 85 posts

Re: Predictive coding has been unified with backpropagation

#41

Post by SamBam »

klmadfejno wrote: post #31
cs702 wrote: post #13

EDIT: Before you read my comment below, please see https://news.ycombinator.com/item?id=26702815 and https://openreview.net/forum?id=PdauS7wZBfC for a different view. -- If the results hold, they seem significant enough to me that I'd go as far as saying the authors of the paper would end up getting an important award at some point, not just for unifying the fields of biological and artificial intelligence , but also…

I'm trying to imagine how that works. Imagine you've got a nueral net. One node identifies the number of feet. One node identifies that number of wings. One node identifies color. This feeds into a layer that tries to predict what animal it is. With backprop, you can sort of assume that given enough scale your algo will identify these important features. With local learning, wouldn't you get a tendency to identify th…

> Is there a need for a sort of middleman like a one arm bandit kind of thing that makes a decision to spawn and despawn child nodes to explore the space more?

What's the one-armed bandit? (Besides a slot machine.)

My knowledge of this field is rusty, but I actually wrote my MSc thesis on novel ways to get Genetic Algorithms to more efficiently explore the space without getting stuck, so it sounds up my alley.

Re: Predictive coding has been unified with backpropagation

#42

Post by eli_gottlieb »

autopoiesis wrote: post #30
andyxor wrote: post #25

Earlier quoted context omitted.

the thing is about every week there is a paper published with groundbreaking claims, with this question in particular being very popular, trying to unify neuroscience and deep learning in some way, in search for computational foundations of AI. Mostly this is driven by success of DL in certain industrial applications. Unfortunately most of these papers are heavy on theory but light on empirical evidence. If we follow…

The paper (arxiv:2103.04689) linked by eutropia above has some empirical evidence on the ML side, showing that performance of predictive coding is not so far off backprop. And there is no shortage of suggestions for how neural circuits might work around the strict requirements of backprop-like algorithms. cs702's original comment above is excessively hyperbolic: the compositional structure of Bayesian inversion is we…

>the compositional structure of Bayesian inversion is well known

/u/tsmithe's results on that are well known, now? I can scarcely find anyone to collaborate with who understands them!

Re: Predictive coding has been unified with backpropagation

#43

Post by 0lmer »

But does predictive coding perceived as a valid theory for cortical neurons functioning? There was a paper from 2017 drawing similar conclusions about backprop approximation with Spike-Timing-Dependent Plasticity: https://arxiv.org/abs/1711.04214 Looks more grounded to current models of neuronal functioning. Nevertheless, it changed nothing in the field of deep learning since then.

Re: Predictive coding has been unified with backpropagation

#44

Post by tsmithe »

eli_gottlieb wrote: post #40
tsmithe wrote: post #32

Earlier quoted context omitted.

In fact, the compositional structure underlying that of predictive coding [0,1] is abstractly the same as that underlying backprop [2]. (Disclaimer: [0,1] are my own papers; I'm working on a more precise and extensive version of [1] right now!) [0] https://arxiv.org/abs/2006.01631 [1] https://arxiv.org/abs/2101.10483 [2] https://arxiv.org/abs/1711.10455

Hurry and publish before I have manuscripts ready applying these results.

Hey, Eli :-)

I'm working on it; I'll send you an e-mail. Things quickly turned out to be more general than I realized last year.

Re: Predictive coding has been unified with backpropagation

#45

Post by hctaw »

andyxor wrote: post #16
hctaw wrote: post #11

I don't know enough about biology or ML to know if what I'm posting below is totally wrong, but here goes. "Backprop" == "Feedback" of a non-linear dynamical system. Feedback is mathematical description of the behavior of systems, not a literal one. I don't know of BNNs are incapable of backprop anymore than an RLC filter is incapable of "feedback" when analyzing the ODE of the latter tells you that there's a feedbac…

The back-prop learning algorithm requires information non-local to the synapse to be propagated from output of the network backwards to affect neurons deep in the network. There is simply no evidence for this global feedback loop, or global error correction, or delta rule training in neurophysiological data collected in the last 80 years of intensive research. [1] As for "why", biological learning it is primarily sha…

I think there's a significant difference worth illustrating that "there is no feedback path in the brain" is not at all equivalent to "learning by feedback is not possible in the brain."

It's well known in dynamics that feed-forward networks are no longer feed-forward when outputs are coupled to inputs, an example of which would be a hypothetically feed-forward network of neurons in an animal and environmental conditioning teaching it the consequences of actions.

I'm very curious on the biological constraints, but I'd reiterate my point above that feedback is a mathematical or logical abstraction for analyzing the behavior of the things we call networks - which are also abstractions. There's a distinction between the physical behavior of the things we see and the mathematical models we construct to describe them, like electromechanical systems where physically no such coupling from output-to-input appears to exist, yet its existence is crucially important analytically.

Re: Predictive coding has been unified with backpropagation

#46

Post by fancy_pantser »

SamBam wrote: post #41
klmadfejno wrote: post #31

Earlier quoted context omitted.

I'm trying to imagine how that works. Imagine you've got a nueral net. One node identifies the number of feet. One node identifies that number of wings. One node identifies color. This feeds into a layer that tries to predict what animal it is. With backprop, you can sort of assume that given enough scale your algo will identify these important features. With local learning, wouldn't you get a tendency to identify th…

> Is there a need for a sort of middleman like a one arm bandit kind of thing that makes a decision to spawn and despawn child nodes to explore the space more? What's the one-armed bandit? (Besides a slot machine.) My knowledge of this field is rusty, but I actually wrote my MSc thesis on novel ways to get Genetic Algorithms to more efficiently explore the space without getting stuck, so it sounds up my alley.

I wonder if you thought of it as a type of optimal stopping problem locally on each node and explore-exploit (multi-armed bandit) globally? For example, if each node knows when to halt when it hits a [probably local] minima, the results can be shared at that point and the best-performing models can be cross-pollinated or whatever the mechanism is at that point. Since both copying the models and continuing without gaining ground are both wastes of time, you want to dial in that local halting point precisely. An overseeing scheduler would record epoch-level results and make the decisions, of course.

Re: Predictive coding has been unified with backpropagation

#47

Post by andyxor »

0lmer wrote: post #43

But does predictive coding perceived as a valid theory for cortical neurons functioning? There was a paper from 2017 drawing similar conclusions about backprop approximation with Spike-Timing-Dependent Plasticity: https://arxiv.org/abs/1711.04214 Looks more grounded to current models of neuronal functioning. Nevertheless, it changed nothing in the field of deep learning since then.

as long as the model requires delta rule, or 'teacher signal' based error correction it is not biologically plausible.

Re: Predictive coding has been unified with backpropagation

#48

Post by himinlomax »

JackFr wrote: post #17
cs702 wrote: post #13

EDIT: Before you read my comment below, please see https://news.ycombinator.com/item?id=26702815 and https://openreview.net/forum?id=PdauS7wZBfC for a different view. -- If the results hold, they seem significant enough to me that I'd go as far as saying the authors of the paper would end up getting an important award at some point, not just for unifying the fields of biological and artificial intelligence , but also…

My background is as an interested amateur, but > also for making it trivial to train models in a fully distributed manner, with all learning done locally seems like a really huge development. At the same time I remain pretty skeptical of claims of unifying the fields of biological and artificial intelligence. I think the recent tremendous successes in AI & ML lead to an unjustified over confidence that we are close t…

Indeed, it's worth mentioning we still have absolutely no idea how memory works.

Re: Predictive coding has been unified with backpropagation

#49

Post by andyxor »

himinlomax wrote: post #48
JackFr wrote: post #17

Earlier quoted context omitted.

My background is as an interested amateur, but > also for making it trivial to train models in a fully distributed manner, with all learning done locally seems like a really huge development. At the same time I remain pretty skeptical of claims of unifying the fields of biological and artificial intelligence. I think the recent tremendous successes in AI & ML lead to an unjustified over confidence that we are close t…

Indeed, it's worth mentioning we still have absolutely no idea how memory works.

we know a lot about memory, but most AI researchers are simply ignorant in neuroscience or cognitive psychology and stick with their comfort zone.

Saying "we have no idea" is just being lazy.

Re: Predictive coding has been unified with backpropagation

#50

Post by jwmullally »

0lmer wrote: post #43

But does predictive coding perceived as a valid theory for cortical neurons functioning? There was a paper from 2017 drawing similar conclusions about backprop approximation with Spike-Timing-Dependent Plasticity: https://arxiv.org/abs/1711.04214 Looks more grounded to current models of neuronal functioning. Nevertheless, it changed nothing in the field of deep learning since then.

Some general background on STDP for the thread:

Biological neurons don't just emit constant 0...1 float values, they communicate using time sensitive bursts of voltage known as "spike trains". Spiking Neural Networks (SNN) are a closer aproximation of natural networks than typical ML ANNs. [0] gives a quick overview.

Spike-Timing-Dependant-Plasticity is a local learning rule experimentally observed in biological neurons. It's a form of Hebbian learning, aka "Neurons that fire together wire together."

Summary from [1]. The top graph gives a clear picture of how the rule works.

> With STDP, repeated presynaptic spike arrival a few milliseconds before postsynaptic action potentials leads in many synapse types to Long-Term Potentiation (LTP) of the synapses, whereas repeated spike arrival after postsynaptic spikes leads to Long-Term Depression (LTD) of the same synapse.

---

[0]: https://towardsdatascience.com/deep-learning-versus-biologic...

[1]: http://www.scholarpedia.org/article/Spike-timing_dependent_p...

Post reply on HN