TL;DR: After the analysis in this post, I started using SLANT PRICE DOUGH BALMY, but it may not be the best sequence for you.

There are lots of great videos and articles on the optimal Wordle strategy. I particularly enjoyed this YouTube video by 3Blue1Brown on using the information theory to come up with the best guesses.

But I can’t play like that. First, I’m lazy, and second, my slow human brain can’t do information theory calculations or depth-first searches.

Instead, every time I play Wordle, I enter the same sequence of words until it feels like there aren’t many solutions left, at which point I try to make more tailored guesses.

Here are a couple examples of my recent games:

Until today, my go-to words were STALE, CHIRP, MOUND, and GAWKY, picked mostly by gut feel while trying to check as many frequently used letters as possible. So far I’ve been doing alright with them.

But can we do better?

Yes! Let’s use math and programming to do better.

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Suppose you want to collect some signals from neurons in the brain. What kind of wire should you stick into the brain to do that? Could it be just any wire, or do you need some special brain-grade wire?

That’s something that I’ve been curious about for a while now, so I’ve read a bunch of papers on the subject.

Metal electrodes

Surprisingly, off-the-shelf 44 AWG insulated copper wire is not a completely terrible choice. Its 51μm diameter (plus insulation) is a bit on the high end for a typical intracortical electrode, but it’s about the right order of magnitude. If you stick it in, for a short time it has a decent chance of picking up electric spikes that nearby neurons send when they get excited.

But it’s still a terrible choice. First, the copper will quickly oxidize and corrode in the brain’s electrolyte-rich fluid. And second, copper is toxic to the neural tissue. So don’t put copper electrodes into anyone you love.

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A bit over two months ago I posted my plan to study electrical engineering, and got a lot of great feedback on HackerNews.

Overall, the advice came down to several categories:

• “Just read The Art of Electronics“. Or, alternatively, “The Art of Electronics has problems; read that book instead.”

• “This plan is too broad to accomplish. Find what piques your interest, and focus on that.”

• “Need more hands-on work; EE is very hands-on.”

Now that I’ve made it partway into the study plan, all of this advice is quite on point.

Two months in, here’s an updated version of the plan.

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As part of my electrical engineering study plan, over the past month, I’ve worked on a couple of FPGA projects.

If you’d like to get into FPGAs as well, here’s the path that worked very well for me.

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Why?

I’ve been interested in brain-computer interfaces for a while, and last summer I finally decided to do something about it. I’ve read through the Fundamental Neuroscience, The Hippocampus, and a bunch of other books and review papers. Now I think I have a basic grasp of what’s going on in the brain, and enough knowledge to be able to read journals, which I intend to keep doing.

What I really lack now is engineering knowledge. I’ve graduated from university with an applied math degree, and then spent 13 years in the workforce, first as a quant analyst at a hedge fund and then as a software developer and a technical lead. In that time, I had zero opportunity to learn or use electrical engineering, and now I need to make that up.

This is intended to plug that hole.

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So when we see something in front of us, like a pencil, how exactly does the brain understand that yep, that’s a pencil?

Like, what is the detailed sequence of steps that happens? The eyes presumably convert the light to neural signals (how?), then these get sent to the brain (where?), and then the brain does… what?

I’ve been reading through a bunch of neuroscience textbooks and papers for the past six months, and I think I’m at the point where I’ve now got the whole picture.

So here are the core pieces of this picture, with a lot of details omitted to fit it into a short blog post.

Let’s start with the input.

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As I read more neuroscience, I run into more interesting neural circuits. One of these circuits is the Vestibulo-Ocular Reflex (VOR) that helps us keep the eyes locked on a target when the head moves.

What’s interesting about it are both its simple wiring (at first glance), and the extensive hidden support circuitry that can tell us more about how the brain works.

VOR is not the only circuit that helps us keep our eyes locked on target; there are others that work in parallel. I suspect that they probably use the same underlying principles.

(Note: some links here are behind a paywall. Remember that it’s illegal to go to Sci-Hub and search for the DOI link of the article to get free access to it.)

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There’s also a YouTube version of this article.

The picture above shows some (far from everything!) of what we know about the cells and neural circuits in the human retina.

What’s interesting is - the retina is not just a light detection organ. It’s a complex electro-chemical calculator with three neural layers, and it uses these layers to extract a number of useful signals from the light that hits our eyes.

Below, we’ll step through some of the more interesting features of the retina, but if you’d like to learn more, the above picture (and this article) are mostly based on this review article published in Feb 2020 in the journal Progress in Retinal and Eye Research, and on chapter 17 of the latest edition of Ryan’s Retina.

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And here’s the same content as the last blog post, but now in YouTube format.

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A few weeks ago, for various reasons I decided to start reading a neuroscience textbook (Fundamental Neuroscience). Though dense, it’s packed with interesting information that filled a lot of gaps in my knowledge about the brain.

One of those gaps was about how exactly the information travels through a neuron. Up to this point, all I knew was “something involving electric pulses”.

The reality, it turns out, is more interesting: neurons use ion channels to create and strengthen electric signals, and propagate information along their lengths.

So here is more or less how I understand this so far.

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