Machine learning algorithms used to decode and enhance human memory

This study is interesting, but it's not really AI and it's not really novel.

The researchers fit a regression to predict word recall from high-frequency EEG activity when memorizing the word. We've known for several years that high-frequency activity predicts memory success, so this part isn't new.

In addition, several papers have tried to improve memory through high-frequency stimulation from brain implants, with various results. This paper proposes "closed-loop" stimulation, delivering stimulation only when the classifier predicts failure. They find that closed-loop is effective.

What the authors really want to claim is that closed-loop is more effective than open-loop, because otherwise their fancy "AI" classifier is useless. Surprisingly, this study does not compare closed-loop vs. open-loop.

Personally what I worry about is that there are too many conflicting, adaptive, self-correcting systems that we don't understand from first principles.

The body has it's own internal "AI" that also responds and adapts to these incoming pulses over time. You could probably snort some speed and get the same effect described here ... but if you keep doing it, it won't keep working. Now replace the Speed with AI that generates the pulses and can adapt the dosage in response to the bodies AI... we just don't know what it would do long-term.

The real problem IMO is that the AI prescribing the dosage doesn't have any of the sensory inputs the human brain does. So it might boost working memory in a way that is maladaptive to the situation.

All in all -- I think these technologies could be quite interesting for allowing us to hyper-evolve out of our mental limitations that are still over-fitted to living in the jungle... but might make us weak as a species in the long run by forcing us to have sensory stimulations that are overfitted to a particular prescribed state that we label as "good".

> The fact remains that while Kahana’s system can improve word recall in specific circumstances, he doesn’t know exactly how it’s improving function. That’s the nature of machine learning.

> Luckily, Kahana's team has thought this through, and some algorithms are easier to scrutinize than others. For this particular study, the researchers used a simple linear classifier, which allowed them to draw some inferences about how activity at individual electrodes might contribute to their model's ability to discriminate between patterns of brain activity.

Isn’t linear regression the easiest of all ML to understand? It’s neural networks that cause black boxes.

Better title: Tickling the brain with low-intensity electrical stimulation in a specific area can improve verbal short-term memory.

https://www.sciencedaily.com/releases/2018/01/180129134354.h...

The "AI" algorithm used in the paper is far from a black box. It's logistic regression, which is extremely well understood and has been used by statisticians and scientists for decades.

"The fact remains that while Kahana’s system can improve word recall in specific circumstances, he doesn’t know exactly how it’s improving function. That’s the nature of machine learning."

Seems like its also the nature of electro-stimulus to the brain.

Is the real story here in ML/AI, or in advances regarding 'when is it helpful to shock your brain a bit vs when is it not'?

You can see better improvement simply by learning how to use your memory properly. The memory palace technique has saved my life

A recent innovation we thought you should be aware of is related to the ability to provide for increased spatio-temporal resolution of the underlying EEG data set as a pre processing step prior to feeding the recorded EEG data into machine learning algorithms. TRUUST has pioneered this and is seeing fantastic results Pre-Clinically on MEA's in drug discovery research for Fragile X and Epilepsy indications. The technology was developed with Epilepsy in mind however. If anyone would have an interest feel free to reach out to us info@truustneuroimaging.com and below are related publications and resources. We thought it made more sense to enhance the data quality first rather than trying to optimize the crap out of algorithms given the problem generally results in better outcomes when better data goes in; garbage in garbage out sort of deal.

Published paper in Journal for Neuroscience Methods: https://www.clearslide.com/view/mail?iID=3f3TTfMPJNBRhXhRDJD...

Published Poster with Scripps at SfN for Fragile X: https://www.clearslide.com/view/mail?iID=C5dp3gjmMWnMxKktk44...

Cool video showing what is possible with recorded EEG: https://www.youtube.com/watch?v=rhRwpAA1KeA

It would be great if people started referring to classification and regression algorithms as Statistical Learning instead of Machine Learning. But then no one would write an article like this I guess.

>But people—and institutional review boards—aren’t usually amenable to cracking open skulls in the name of science.

I feel as if future civilizations (if we get there) will look back at the lack of practice quoted above with the same demeanor as we do now for geocentrism:

Should the needs of the many outweigh the needs of the few?

Summary: Researchers collaborated with epilepsy patients, who already had electrodes implanted in their brain to monitor seizures, to improve the patients' memory. The electrodes are capable of both reading brain patterns, and stimulating brain activity. ML algorithms learned what each patient's brain pattern looked like when they successfully memorized a word. The ML algorithms would then provide a jolt to mirror those successful-memorization-brain-patterns for words that the patient would historically not have memorized.

Thats not what we want. The memory should be recalled from external brainz. I just want to be a professor and a doctor of everything and clearly remember every paper written about everything complete with animated visuals and indexing.

A step toward building neural laces.

15%

Be careful what you wish for. Not all memories are good. Some are best left forgotten.

The irony is we keep failing to remember to consider unintended consequences.