a neurotech pulse check….
breaking down neurotech and where we are at
main question at Hand:
Creating a paradigm shift in how we read and write to the brain by iterating on current neuroimaging tools full-stack from the atom to the machine learning.
State of Play of Neurotech | Where Current Technologies Fall Short?
Currently, the range of neuroimaging technologies falls on a bi-dimensional map with the two axes being two resolutions: temporal and spatial.
On this graph, we see our familiar MRI and EEG up on the right. This isn’t a good sign. Higher temporal resolution indicates that it takes longer for the technology to measure neural activity, and higher spatial resolution means that the imaging technology samples data from larger chunks of the brain.
The average neuron fires every 0.1 seconds, and at its largest, a neuron can have a cell body of 0.1mm. In a world where we fulfill the potential of brain-machine interfaces (Matrix, brain2brain comms, etc.), we want to read a signal from every single neuron in our brain with a temporal resolution that accounts for every action potential of a neuron: create a whole-brain interface. Clearly, EEGs and MRIs don’t cut it. The technologies in the red, on the other hand, seem to align much more with the BCI reality, however, in reality, they too are quite far from this idealized future.
“The problem with current tools is that it paints only broad strokes of brain activity, like a series of satellite images over a hurricane that shows the storm moving but not the formation and disruption of individual clouds.”
- Mikael @ Global Citizen Forum ‘21
The Objective Checkpoint to Get to the “BCI Future”
To get to the idealized “BCI Future” that everyone is hoping for, we must be able to measure the brain on a neuronal level at a very high throughput & field of view.
To get to that level of sophistication in the Neurotech field, there needs to be a wide array of technological improvements.
Putting into perspective what measuring every single neuron in our brains looks like:
- An average cubic millimetre of cortex contains about 40,000 neurons
- Each neuron has synaptic connections to as many as 1,000 — sometimes as high as 10,000 — other neurons. With around 20 billion neurons in the cortex, that means there are over 20 trillion individual neural connections in the cortex (and as high as a quadrillion connections in the entire brain).
- Due to neuroplasticity, the voltages of each neuron would be constantly changing, as many as hundreds of times per second. And the tens of millions of synapse connections in our cortex would be regularly altering sizes, disappearing, and reappearing
The milestone to make significant headway with every neurotech enthusiast’s dream is to reach a point where we can simultaneously read from 1 million individual neurons.
Stevenson’s law is basically the “Moore’s law of Neurotech,” graphing the number of neurons simultaneously recorded over time.
As of right now, we are doubling every 7.4 years. At this rate, we will reach the 1-million-neuron goal by the end of the century.
We want to give the neurotech industry steroids; Stevenson’s law should mirror Moore’s.
To fulfill the BCI future in our lifetimes, we want to create a completely different framework for reading and writing to the brain.
Coming back to us and our work with Neuronic….
What both of us realized as we continued working in this field, reading state of the art research papers, and talking to fellow enthusiasts, was that reaching the ubiquitous BCI was not a matter of finding the best application of the technology but rather a matter of just making the tool better. Just like how the breakthroughs in computer hardware caused the software industry to explode, the intelligence-enhancing, matrix-tier solutions will only bloom once we make breakthroughs in neuroimaging. Now the question becomes…
What is the tool to iterate on? Does it already exist? Are we yet to conceive of it?
These are the most pressing questions of the neurotech industry. Progress in this space is stunted by the quality of brain data we can get our hands on. Stagnancy is found in both academia and industry because of the current technological norms. Neurotech research projects fail to unlock novel insights with real significance, and downstream, application-oriented neurotech startups struggle to provide solutions that have competitive value propositions in relation to simpler alternatives. The killer app of neural interfaces can’t be discovered unless the fundamental questions of what tool will bring us to the BCI future are answered. We’ve taken on the journey to answer these questions ourselves.
Our current path to reach the idealized future of Neurotech is roughly similar to the one Tim Urban outlined in his Wait But Why Post titled, “Neuralink and the Brain’s Magical Future.”
At Neuronic, we are on the path of:
Developing Cutting-Edge BMIs to Increase the Derivative of Innovation and Growth in Neurotechnlogy.
We cannot do this alone, so we wanted to reach out and give a formal introduction. We are sole believers that each and every one of you reading this article today can drastically change the outlook of our project, and we would love to have a conversation with you. The future of neurotech is a long and enjoyable journey; come along for the show.
We want to talk to you!
Mikael Haji →
📫 email: mikaelhaji@gmail.com
🕴 linkedin: mikaelhaji
Anush Mutyala →
📫 email: mutyalaanush@gmail.com
🕴 linkedin: anushmutyala
