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Our Brains Use Binary Logic, Say Neuroscientists (sciencedaily.com)
"The brain's basic computational algorithm is organized by power-of-two-based logic," reports Sci-News, citing a neuroscientist at Augusta University's Medical College.
hackingbear writes:
He and his colleagues from the U.S. and China have documented the algorithm at work in seven different brain regions involved with basics like food and fear in mice and hamsters. "Intelligence is really about dealing with uncertainty and infinite possibilities," he said. "It appears to be enabled when a group of similar neurons form a variety of cliques to handle each basic like recognizing food, shelter, friends and foes. Groups of cliques then cluster into functional connectivity motifs to handle every possibility in each of these basics. The more complex the thought, the more cliques join in."
It starts out obvious, but then factors like conduction speed, receptor sensitivity, calcium channel recharge rates, etc. etc. all factor in to make a "wet" neural network quite a bit more complex and nuanced than an electronic network of NAND gates.
One of the open questions in "brain replication" is: can you get the same end result without the delays, varying sensitivity, numbing from multiple firing, etc.?
OP seems to be saying that they think the hierarchy is using binary structures, but not that the firing/not firing is a simple 0 or 1 condition.
I mean, maybe it's just bad reporting, but seriously:
At the heart of the Theory of Connectivity is the algorithm, N=2i–1, which defines how many cliques are needed for an FCM and which enabled the authors to predict the number of cliques needed to recognize food options, for example, in their testing of the theory.
“N is the number of neural cliques connected in different possible ways; 2 means the neurons in those cliques are receiving the input or not; i is the information they are receiving; and -1 is just part of the math that enables you to account for all possibilities,” Dr. Tsien said.
First off, that's not an algorithm but an equation. i is the information they're receiving? But in context, i is a number. Finally, "the part of the math that enables you to account for all possibilities"? what does that even mean?
factors like conduction speed, receptor sensitivity, calcium channel recharge rates, etc. etc. all factor in to make a "wet" neural network quite a bit more complex and nuanced than an electronic network of NAND gates.
Clocks, wire delay, V-F curves and other things are all issues in electronic networks. It's all analog down there. And then it's all quantum under that.
One of the open questions in "brain replication" is: can you get the same end result without the delays, varying sensitivity, numbing from multiple firing, etc.?
I would suspect that the processes in brain are as robust as necessary for the survival of the biological organism and to minimize energy consumption, and as non-robust as necessary for the same reasons. Digital electronics common today is also sufficiently robust when operating under the specification as to allow it to do useful work over the whole manufacturing batch. As near-threshold computing ideas spread into the digital domain, perhaps brain-style of robustness comes a theme in some areas of digital electronics as well.
I've been waiting for a good opportunity to take this new toy out for a spin. Semantic Scholar claims to have brain science almost completely covered.
* author search
Not bad.
* topic search
Not blindingly great. But the third link down is a primary hit.
Theory of Connectivity: Nature and Nurture of Cell Assemblies and Cognitive Computation
There's not a lot of related material here that I'd have gone chasing after the hard way. Apparently, either this research result or this search engine is still too new.
Nevertheless, I retain high hopes.
There's an interesting theory running around Neuroscience (and, let me tell you what a Harvard educated neuroscientist said about virtually all neuroscience and brain studies: "they're great theories, they've got tiny experimental observations to back them up, they're basically pulling all of this out of their asses.") so, anyway, the theory goes that "thoughts" are encoded as repeating firing patterns. As far as I understand the theory and what little I read of OP, the patterns themselves are more nuanced than binary, but one "idea" might be encoded as firing pattern like ..-..- ..-..- while a competing idea might be encoded as a different firing pattern: ...---... ...---... so, there can be thousands of distinct firing patterns (continuing the morse code analogy, even a simple 3 letter code holds over 17000 unique patterns, make those inter-firing intervals analog and a virtual infinity of patterns can be encoded with 8 to 10 intervals) so, groups of neurons start "singing" their songs based on inputs from other areas of the brain/body, usually several different patterns based on competing inputs from different "source" areas. The neurons in a "processing node" sort of fight it out, each repeating their idea of the "right" firing pattern until they reach some form of consensus, then that coherent pattern becomes a "valid" input to the next level of processing, which itself may be getting inputs from a bunch of different areas that it has to hash out until it can reach a coherent pattern to pass along. Eventually, these "ideas" fire off motor control routines and actuate the body to move, speak, etc.
The thing that's most intriguing to me about this theory is that it's somewhat repeated in human society. We get together, repeat ideas among small groups, pass them along to "higher levels" and eventually act as societies to do things. Now, with the internet, we have billions of people acting in some ways like neurons in a brain, reaching consensus about some things and chanting in chaotic disagreement about others.