How acupuncture works, why we dream, and finding fulfillment by cultivating flow

Friday Brainstorm S2 E3 🧠

Hi friends,

I hope you all had a happy and safe Thanksgiving! Welcome to the ~100 new readers that joined us since the last edition, really happy to have you.

I usually send this newsletter out on Fridays, but the past three editions have gone out on Saturdays due to poor planning on my part. Honestly I would just change the name, but Saturday Brainstorm doesn’t have the same ring to it. Anyway, you probably don’t care; I’m writing this bit mostly to guilt myself into publishing on Fridays again.

You’re going to like this week’s issue if you’re curious about the frontier of biological and computational neuroscience. You can expect:

  • the science of cell communication 🔬

  • why we dream (a computational lens) 🛌

  • (podcast) on cultivating flow states 🍳

Let’s get into it!


The emerging science of cell communication 🔬

Jon Lieff’s book, The Secret Language of Cells, is a comprehensive exploration of the groundbreaking new research findings in the field of cellular communication.

He explains the surprising science of how very different cells—bacteria and brain cells, blood cells and viruses—all speak the same language. [. . .] it has applications for immunity, chronic pain, weight loss, depression, cancer treatment, and virtually every aspect of health and biology.

Not only is this is first effort at making these findings accessible to a general audience, but it also represents a paradigm shift in how we understand health and disease.

The book has personal significance to me because I grew up in a family that had a strong distrust for Western medicine, relying instead on alternative treatments like acupuncture and energy-based therapy. Until recently, scientific explanations for these treatments didn’t exist in the mainstream academic literature.

Let’s take acupuncture — it’s most often used to relieve pain by inserting thin needles into “acupuncture points” that are distant from the area in question. For example, the feet are a particularly rich body part when it comes to these points.

Until recently, pain was thought to be caused by well-known brain circuits. It turns out that circuits that influence pain also involve immune cells and are decentralized, in that they converse with one another from many locations throughout the body.

Recent research showed that electrical stimulation of immune cells (T cells) altered a neural pain circuit in a distant organ, by sending molecular signals to a nearby neuron that served as the intermediate messenger. To tie this back to acupuncture, it starts to make sense how using thin needles to stimulate pockets of immune cells can work to decrease pain and inflammation in other parts of the body.

Understanding these complex circuits will enable new types of medical treatments and justify the efficacy of alternative treatments that have existed for thousands of years.

Ultimately, Lieff’s synthesis of the research opens more questions than it answers.

One issue facing scientists is the fact that we don’t know if all the types of signals have been discovered yet. As well as chemical and electrical signals, there could be other types, such as electromagnetic fields, photons, and quantum states, which might be directing information flow.

I’m excited about the prospect of more attention being given to these understudied signaling mechanisms. Earlier this year, I highlighted an emerging field that provides a novel framework for studying energy-based therapies. It turns out that all living systems generate a dynamic electro-magnetic field, called a biofield.

If you’re curious, I give a high-level overview in my article — Biological WiFi.


Why we dream from a computational lens 🛌

You’ve probably heard that the brain consolidates your memories when you sleep, but have you ever wondered how this happens? It turns out that dreams are a crucial part of this process, and not for the reasons you might think. Computational neuroscience studies have revealed the functional role of dreams in helping us preserve memories.

As a bit of background, there are two types of long-term memory:

  • episodic memory: involves the actual details of an event, situation or experience (e.g. where did I park today)

  • semantic memory: related to meaning, knowledge, significance, or relationships (e.g. where do I usually park?)

The brain is responsible for both remembering specifics (episodic) and extracting generalities over multiple experiences (semantic). To support these incompatible goals, there are two separate brain circuits for learning: hippocampus and neocortex.

Episodic information is initially encoded in the hippocampus, where it can be stored rapidly without interfering with existing knowledge. This turns into semantic memory when it’s slowly consolidated to the neocortex, which contains all your accumulated memories and experiences.

Behavioral evidence suggests that we consolidate new experiences from the day while we sleep. In an experiment, participants were taught new information and split into two groups — Group 1 was deprived of sleep the same night whereas Group 2 didn’t sleep the following night. After two days, the participants were tested and those from Group 1 performed worse even though Group 2 was more tired. This demonstrated that sleep plays a crucial role in same-day memory consolidation. The findings extends to declarative learning, perceptual learning, and skill learning.

How does the memory consolidation process work?

In short, the theory is that the hippocampus replays memories back to the neocortex during sleep. Researchers tried to model the integration of new info, but ran into a problem — all the old info was being immediately unlearned in the simulations.

Consider the paired associates learning paradigm. Here’s how it works:

  1. A set of paired associations, AB, are learned (ex: window-reason, bicycle-garbage)

  2. A new set of associations, AC, are learned for half of the old pair (ex: window-telephone, bicycle-desk)

  3. Both sets of associations, AB and AC, are tested (ex: window - ?, bicycle - ?)

In humans, we see a modest and gradual loss of memory for AB set as the number of AC set learning trials increases. This is expected, since half of the AC associations are overwriting the previously learned AB associations in memory.

The neural network model, on the other hand, was able to initially learn the AB set but the AB associations are completely overwritten when it starts to learn the AC set. In other words, the model wasn’t able to integrate the new info as well as humans can.

This puzzled researchers for a long time, until they discovered that the problem was the training regimen they used. Their assumption was that the hippocampus simply replayed new memories to the neocortex, but the simulations showed that this heavily interfered with older knowledge.

It turns out that the brain uses a slightly less efficient method called interleaved training, where new info is regularly jumbled with old and inconsistent knowledge. While this means that learning is slightly slower, it allows for the new info to coexist with the older knowledge in memory.

Imagine this process as slowly sifting flour (new info) into the dough of old knowledge.

This is where dreams come in. While we’re sleeping, our brain cycles between slow-wave (deep) sleep, where recent memories are integrated, and REM (dreaming) sleep, where connections damaged by new learning are revisited and repaired. Without dreams, old memories are more likely to be overwritten by new info being integrated.

Dreams are a byproduct of your brain bringing up random old information to aid in the memory consolidation process.

I always found it fascinating that the role of dreams was discovered only after trying to model the brain’s neural architecture and running into same problems that nature did.

I also wanted to thank my professor, Jon Cohen, for his brilliant lecture that inspired this post.


Living a fulfilling life by cultivating flow 🍳

I’m excited to announce that my new podcast, Mind Gym, has finally launched.

I teamed up with John Alvarez to explore the neuroscience of optimal cognition. Each episode will be 15 - 20 minutes and full of actionable insights. Think The Happiness Lab, but with a focus on mental states and cognitive neuroscience.

In the first episode, we discuss how life can be made more enjoyable by recognizing the conditions that are favorable to flow and working to actively cultivate them.

For the keen reader, I know that I promised to focus on cultivating flow at work in the podcast teaser but it ended up being too much content to cover in 20 minutes. I hope to cover it in a future episode — in the meantime, check out this one!


I hope you’ve enjoyed this issue of Friday Brainstorm! What got you thinking? Anything to add? Let me know by replying to this email.

— Shamay