Neuroscientists as well as computer experts have long been curious
about how the brain is able to not only hold specific information, like a
computer, but - unlike even the most sophisticated technology - to
also categorize and generalize the information into abstract knowledge
Our brains have a basic algorithm that enables us to not just
recognize a traditional Thanksgiving meal, but the intelligence to
ponder the broader implications of a bountiful harvest as well as good
family and friends.
‘The brain may operate on an amazingly simple mathematical logic, suggested researchers.’
"A relatively simple mathematical logic underlies our complex brain
computations," said Dr. Joe Z. Tsien, neuroscientist at the Medical
College of Georgia at Augusta University, co-director of the Augusta
University Brain and Behavior Discovery Institute and Georgia Research
Alliance Eminent Scholar in Cognitive and Systems Neurobiology.
Tsien is talking about his Theory of Connectivity, a fundamental
principle for how our billions of neurons assemble and align not just to
acquire knowledge, but to generalize and draw conclusions from it.
"Intelligence is really about dealing with uncertainty and infinite
possibilities," Tsien 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, or FCMs, to handle every
possibility in each of these basics like extrapolating that rice is part
of an important food group that might be a good side dish at your
meaningful Thanksgiving gathering. The more complex the thought, the
more cliques join in.
That means, for example, we cannot only recognize an office chair,
but an office when we see one and know that the chair is where we sit in
"You know an office is an office whether it's at your house or the
White House," Tsien said of the ability to conceptualize knowledge, one
of many things that distinguishes us from computers.
Tsien first published his theory in a 1,000-word essay in October 2015 in the journal Trends in Neuroscience
Now he and his colleagues have documented the algorithm at work in
seven different brain regions involved with those basics like food and
fear in mice and hamsters. Their documentation is published in the
journal Frontiers in Systems Neuroscience
"For it to be a universal principle, it needs to be operating in
many neural circuits, so we selected seven different brain regions and,
surprisingly, we indeed saw this principle operating in all these
regions," he said.
Intricate organization seems plausible, even essential, in a human
brain, which has about 86 billion neurons and where each neuron can have
tens of thousands of synapses, putting potential connections and
communications between neurons into the trillions. On top of the
seemingly endless connections is the reality of the infinite things each
of us can presumably experience and learn.
"Many people have long speculated that there has to be a basic
design principle from which intelligence originates and the brain
evolves, like how the double helix of DNA and genetic codes are
universal for every organism," Tsien said. "We present evidence that the
brain may operate on an amazingly simple mathematical logic."
"In my view, Joe Tsien proposes an interesting idea that proposes a
simple organizational principle of the brain, and that is supported by
intriguing and suggestive evidence," said Dr. Thomas C. Südhof, Avram
Goldstein Professor in the Stanford University School of Medicine,
neuroscientist studying synapse formation and function and a winner of
the 2013 Nobel Prize in Physiology or Medicine.
"This idea is very much worth testing further," said Südhof, a
sentiment echoed by Tsien and his colleagues and needed in additional
neural circuits as well as other animal species and artificial
At the heart of Tsien's Theory of Connectivity is the algorithm,
n=2?-1, which defines how many cliques are needed for an FCM and which
enabled the scientists 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, Tsien
To test the theory, they placed electrodes in the areas of the brain
so they could "listen" to the response of neurons, or their action
potential, and examine the unique waveforms resulting from each.
They gave the animals, for example, different combinations of four
different foods, such as usual rodent biscuits as well as sugar pellets,
rice and milk, and as the Theory of Connectivity would predict, the
scientists could identify all 15 different cliques, or groupings of
neurons, that responded to the potential variety of food combinations.
The neuronal cliques appear prewired during brain development
because they showed up immediately when the food choices did. The
fundamental mathematical rule even remained largely intact when the NMDA
receptor, a master switch for learning and memory, was disabled after
the brain matured.
The scientists also learned that size does mostly matter, because
while the human and animal brain both have a six-layered cerebral cortex
- the lumpy outer layer of the brain that plays a key role in higher
brain functions like learning and memory - the extra longitudinal
length of the human cortex provides more room for cliques and FCMs,
And while the overall girth of the elephant brain is
definitely larger than the human brain, for example, most of its neurons
reside in the cerebellum with far less in their super-sized cerebral
cortex. The cerebellum is more involved in muscle coordination, which
may help explain the agility of the huge mammal, particularly its trunk.
Tsien noted exceptions to the brain's mathematical rule, such as in
the reward circuits where the dopamine neurons reside. These cells tend
to be more binary where we judge, for example, something as either good
or bad, Tsien said.
The project grew out of Tsien's early work in the creation of smart
mouse Doogie 17 years ago while on faculty at Princeton University, in
studying how changes in neuronal connections lay down memories in the