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Inferring neural activity before plasticity for learning beyond backpropagation

nature.com

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Re: Inferring neural activity before plasticity for learning beyond backpropagation

#21

Post by mindcrime »

lukeinator42 wrote: post #19

It has been clear for a long time (e.g. Marvin Minsky's early research) that: 1. both ANNs and the brain need to solve the credit assignment problem 2. backprop works well for ANNs but probably isn't how the problem is solved in the brain This paper is really interesting, but is more a novel theory about how the brain solves the credit assignment problem. The HN title makes it sound like differences between the brain…

> The HN title makes it sound like differences between the brain and ANNs were previously unknown and is misleading IMO.

Agreed on both counts. There's nothing surprising in "there are differences between the brain and ANN's."

But their might be something useful in the "novel theory about how the brain solves the credit assignment problem" presented in the paper. At least for me, it caught my attention enough to justify giving it a full reading sometime soon.

Re: Inferring neural activity before plasticity for learning beyond backpropagation

#24

Post by pharrington »

Theories that brains predict the pattern of expected neural activity aren't new, (eg this paper cites work towards the Free Energy Principle, but not Embodied Predictive Interoception Coding works). I have 0 neuroscience training so I doubt I'd be able to reliably answer my question just by reading this paper, but does anyone know how specifically their Prospective Configuration model differs, or expands, upon the previous work? Is it a better model of how brains actually handle credit assign than the aforementioned models?

Re: Inferring neural activity before plasticity for learning beyond backpropagation

#25

Post by oatmeal1 »

> In prospective configuration, before synaptic weights are modified, neural activity changes across the network so that output neurons better predict the target output; only then are the synaptic weights (hereafter termed ‘weights’) modified to consolidate this change in neural activity. By contrast, in backpropagation, the order is reversed; weight modification takes the lead, and the change in neural activity is the result that follows.

What would neural activity changes look like in an ML model?

Re: Inferring neural activity before plasticity for learning beyond backpropagation

#26

Post by eli_gottlieb »

pharrington wrote: post #24

Theories that brains predict the pattern of expected neural activity aren't new, (eg this paper cites work towards the Free Energy Principle, but not Embodied Predictive Interoception Coding works). I have 0 neuroscience training so I doubt I'd be able to reliably answer my question just by reading this paper, but does anyone know how specifically their Prospective Configuration model differs, or expands, upon the pr…

The FEP is more about what objective function the brain (really the isocortex) ought to optimize. EPIC is a somewhat related hypothesis about how viscerosensory data is translated into percepts.

Prospective Configuration is an actual algorithm that, to my understanding, attempts to reproduce input patterns but can also engage in supervised learning.

I'm less clear on Prospective Configuration than the other two, which I've worked with directly.

Re: Inferring neural activity before plasticity for learning beyond backpropagation

#27

Post by pharrington »

eli_gottlieb wrote: post #26
pharrington wrote: post #24

Theories that brains predict the pattern of expected neural activity aren't new, (eg this paper cites work towards the Free Energy Principle, but not Embodied Predictive Interoception Coding works). I have 0 neuroscience training so I doubt I'd be able to reliably answer my question just by reading this paper, but does anyone know how specifically their Prospective Configuration model differs, or expands, upon the pr…

The FEP is more about what objective function the brain ( really the isocortex) ought to optimize. EPIC is a somewhat related hypothesis about how viscerosensory data is translated into percepts. Prospective Configuration is an actual algorithm that, to my understanding, attempts to reproduce input patterns but can also engage in supervised learning. I'm less clear on Prospective Configuration than the other two, whi…

Thanks!

Re: Inferring neural activity before plasticity for learning beyond backpropagation

#28

Post by Lerc »

yongjik wrote: post #15

The title of the paper is: "Inferring neural activity before plasticity as a foundation for learning beyond backpropagation" The current HN title ("Brain learning differs fundamentally from artificial intelligence systems") seems very heavily editorialized.

As https://news.ycombinator.com/item?id=42260033 said, the difference is not a new discovery, not surprising, and not the focus of the paper.

Making the 'fundimental difference' the focus seems like laying the foundation to a claim that AI lacks some ability because of the difference. The difference does mean you cannot infer abilities present in one by detecting them in the other. This is the similar to, and as about as profound as, saying that you cannot say that rocks can move fast because of their lack of legs. Which is true, but says nothing about the ability of rocks to move fast by other means.

Re: Inferring neural activity before plasticity for learning beyond backpropagation

#29

Post by dawnofdusk »

lukeinator42 wrote: post #19

It has been clear for a long time (e.g. Marvin Minsky's early research) that: 1. both ANNs and the brain need to solve the credit assignment problem 2. backprop works well for ANNs but probably isn't how the problem is solved in the brain This paper is really interesting, but is more a novel theory about how the brain solves the credit assignment problem. The HN title makes it sound like differences between the brain…

Are there any results about the "optimality" of backpropagation? Can one show that it emerges naturally from some Bayesian optimality criterion or a dynamic programming principle? This is a significant advantage that the "free energy principle" people have.

For example, let's say instead of gradient descent you want to do a Newton descent. Then maybe there's a better way to compute the needed weight updates besides backprop?

Re: Inferring neural activity before plasticity for learning beyond backpropagation

#30

Post by TheOtherHobbes »

skissane wrote: post #18
isaacimagine wrote: post #4

Wait, my brain doesn't do backprop over a pile of linear algebra after having the internet rammed through it? No way that's crazy /s tl;dr: paper proposes a principle called 'prospective configuration' to explain how the brain does credit assignment and learns, as opposed to backprop. Backprop can lead to 'catastrophic interference' where learning new things abalates old associations, which doesn't match observed bio…

> Backprop can lead to 'catastrophic interference' where learning new things abalates old associations, which doesn't match observed biological processes. Most people find that if you move away from a topic and into a new one your knowledge of it starts to decay over time. 20+ years ago I had a job as a Perl and VB6 developer, I think most of my knowledge of those languages has been evacuated to make way for all the…

Is it replaced, or does it decay without reinforcement?
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