Computational Model Reveals How the Brain Manages Short-Term Memories

As a neuroscientist, I can tell you with high confidence that many if not most neuroscientists would agree with you.

You might also be interested in "Could a Neuroscientist Understand a Microprocessor?"

https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

> Neuroscience is so laughably primitive.

Its hard to comprehend how complex of a machinery the brain is when you are "just" a computer scientist. I often see people from the AI field eager to do a short rotation in a neuroscience lab to "learn how the brain works" and then go back to their company and develop a novel algorithm to make a paradigm shift in AI field - it's enthusiastic but laughably primitive.

The technology available to understand this complex machine is primitive when you compare it to electronics or AI tech. This is not because of the lack of funding and talent being invested in the field but because of the highly complex and efficient nature of the brain that has evolved over millions of years but only started to be investigated anatomically for less than a couple of centuries. Arguably, the same problem exists in the AI field too when it comes to understanding the black-box models. We don't even entirely comprehend how the artifical neural network based models work in many use-cases; there are ideas floating around to make sense of it which use the same strategy of "plug in/out and play" to understand what parameter affects what in your model which to neuroscientists seems (yet again) laughably primitive.

If you look at almost any complex phenomena that humans didn't actually create, you will see little actual knowledge. Neuroscience, much of biology and economics are examples.

Most areas of the brain are rather homogenous. They've done experiments rewiring inputs going to the visual cortex to the auditive cortex in rodents and they could see. Further, artificial neural nets demonstrate that extremely simple repetitive structures can implement a vast function space simply by training them by stochastic gradient descent. The most complex structures in the brain may be hardwired social behaviors, including facial expression and recognition, but these may not be necessary for the intelligence part as individuals strongly impaired in hardwired social behavior (autists) are sometimes capable of complex thought.

Being a mathematician who's dabbled in neuroscience a bit, I think that's essentially right (though perhaps I wouldn't have said "laughable"). But I also think this reflects the differences between how science and engineering progress -- our ability to exploit a natural phenomenon (or class of phenomena) often outstrips our ability to explain what's going on. As examples, I'd argue that for much of aviation history (and perhaps now too, I don't know), our ability to build flying machines far outstrips our understanding of how birds and insects (or even planes) fly.

I guess neuroscience mostly compares to the condensed matter / semiconductor field, in the computer analogy. They know everything about the transistors but guess at why they're connected like they are. And some in their respective fields will also be very interested in the high-level connections although maybe with less depth..

Unfortunately, in the neuroscience area, there is not yet any analogy to a computer programmer.

This is explained in the Nature article (summary on page 133) and shown as a diagrams in figure 6...

> In summary, these findings imply that robust inhibition of oppositely tuned inhibitory subpopulations is critical for memory maintenance in our RNN model. For example, a positive cue stimulus activates the inhibitory and excitatory subgroups selective for that stimulus and deactivates the negative stimulus subgroups. During the delay period, the inhibition strength between these two inhibitory subgroups dictates the stability of the cue-specific activity patterns generated during the stimulus window. The positive feedback provided by the similarly tuned excitatory neurons sustains the stimulus-specific activity of the inhibitory subgroups.

Optimistic guess: This information can be found in the paywall'ed article at [1]

> Dissecting our recurrent neural network model revealed strong inhibitory-to-inhibitory connections underlying a disinhibitory microcircuit as a critical component for long neuronal timescales and WM maintenance

If the article is worth the money, i would expect it to go into detail on it.

[1] https://www.nature.com/articles/s41593-020-00753-w

A model based on some core principles or constraints of the natural system will often solve the problem by discovering the same structure used by nature. And so facts of the model can reveal facts of the natural system. Of course such discoveries need to be validated against the real thing.

It can predict, but that’s a different thing entirely.

>That could be an easy fix!

Famous last words?

I agree with this sentiment. To be fair though, the authors did attempt to generate a biologically plausible RNN, which they published in a previous article...

https://www.pnas.org/content/pnas/116/45/22811.full.pdf

I'm not fully sold on this model, but the article is fairly dense and I dont fully understand it. (Sejnowski, the senior author, was on my dissertation committee; he fell asleep during my defense lol)

Here's the GitHub with their code for "Working Memory Spiking RNN Model" (This repository provides the code for the model and analyses presented in the paper): https://github.com/rkim35/wmRNN