[Paleopsych] Intelligent Bacteria

[Thrst4knw at aol.com]

http://www.world-science.net/exclusives/050418_bactfrm.htm

Intelligent bacteria?
For the most primitive beings in the web of life, some researchers claim, “
simple” might not mean “stupid.” 
Posted April 18, 2005
Special to World Science

Bacteria are by far the simplest things alive, at least among things 
generally agreed on as being alive. Next to one of these single-celled beings, one 
cell of our bodies looks about as complex as a human does compared to a sponge. 



A colony of Paenibacillus dendritiformis bacteria, which some researchers say 
can organize themselves into different types of extravagant formations to 
maximize food intake for given conditions. According to some, this reflects a 
bacterial intelligence. (Courtesy Eshel Ben-Jacob, Tel Aviv University, Israel)



Yet the humble microbes may have a rudimentary form of intelligence, some 
researchers have found.

The claims seem to come as a final exclamation point to a long series of 
increasingly surprising findings of sophistication among the microbes, including 
apparent cases of cooperation and even altruism. 

But there is no clear measurement or test that scientists can use, based on 
the behavior alone, to determine whether it reflects intelligence.

Some researchers, though, have found a systematic way of addressing the 
question and begun looking into it. This method involves focusing not so much on 
the behavior itself as the nuts and bolts behind it—a complex system of chemical 
“signals” that flit both within and among bacteria, helping them decide what 
to do and where to go.

Researchers have found that this process has similarities to a type of 
human-made machine designed to act as a sort of simplified brain. These devices 
solve some simple problems in a manner more human-like than machine-like.

The devices, called neural networks, also run on networks of signals akin to 
those of the bacteria. The devices use the networks to “learn” tasks such as 
distinguishing a male from a female in photographs—typical sorts of problems 
that are easy for humans but hard for traditional computers.

The similarities in the bacterial and neural network signaling systems are 
far more than superficial, wrote one researcher, Klaas J. Hellingwerf, in the 
April issue of the journal Trends in Microbiology. He found that the bacterial 
system contains all the important features that make neural networks work, 
leading to the idea that the bacteria have “a minimal form of intelligence.”

Bacterial signaling possesses all four of the key properties that neural 
network experts have identified as essential to make such devices work, 
Hellingwerf elaborated. The only weak link in the argument, he added, is that for one of 
those properties, it’s not clear whether bacteria exhibit it to a significant 
extent. This may be where future research should focus, he wrote.

Cooperation and altruism

The comparison of bacterial signaling with neural networks is not the only 
evidence that has nudged researchers closer to the concept that bacteria might 
possess a crude intelligence—though few scientists would go as far as to use 
that word. 

One of the other lines of evidence is a simple examination of bacterial 
behavior.

This behavior is strikingly versatile, researchers have found in recent 
years; bacteria can cope with a remarkably wide range of situations by taking 
appropriate actions for each. For instance, the deadly Pseudomonas aeruginosa can 
make a living by infecting a wide variety of animal and plant tissues, each of 
which is a very different type of environment in which to live and find 
sustenance.

Furthermore, bacteria cooperate: they can group together to take on tasks 
that would be difficult or impossible for one to handle alone. In a textbook 
example, millions of individuals of the species Myxococcus xanthus can bunch up to 
form a “predatory” colony that moves and changes direction collectively 
toward possible food sources.

Some examples of bacterial cooperation have even led researchers to propose 
that bacteria exhibit a form of altruism. For instance, some strains of 
Escherichia coli commit suicide when infected by a virus, thereby protecting their 
bacterial neighbors from infection.

But until recently, few or no scientists had seriously suggested these 
behaviors reflected intelligence.  
For instance, bacterial “altruism” may be a simple outcome of evolution that 
has nothing to do with concern for the welfare of others, wrote the 
University of Bonn’s Jan-Ulrich Kreft in last August’s issue of the research journal 
Current Biology. Thus he didn’t suggest that any process akin to thinking was 
at work.

But one thing that ties these various behaviors together is that they all 
operate as a result of signaling mechanisms like the ones studied by Hellingwerf.

Mousetraps, learning and language

These mechanisms work in a way somewhat akin to the American board game 
Mousetrap. In this game, you try to catch your opponent’s plastic mouse using a 
rambling contraption that starts working when you turn a crank. This rotates 
gears, that push a lever, that moves a shoe, that kicks a bucket, that sends a 
ball down stairs and—after several more hair-raising steps of the sort—drops a 
basket on the mouse.

Molecular signals inside cells work through somewhat similar chain reactions, 
except the pieces involved are molecules. 

A typical way these molecular chain reactions work is that small clusters of 
atoms, called phosphate groups, are passed among various molecules. One 
example of what such a system could accomplish: a bit of food brushing against the 
cell could start a series of events that lead inside the cell and activate 
genes that generate the chemicals that digest the food.

A single bacterium can contain dozens of such systems operating 
simultaneously for different purposes. And compared to the board game, the cellular systems 
have additional features that make them more complicated and versatile.

For instance, some of these bacterial contraptions, when set in motion, lead 
to the formation of extra copies of themselves. These tricks can lead to 
phenomena with aspects of learning and language.

For example, a shortage of a nutrient in a bacterium’s neighborhood can 
activate a system that makes the microbe attract the nutrient toward itself more 
strongly. The system also produces extra copies of itself, researchers have 
found. Thus if shortage recurs later, the bacterium is better prepared. This is a 
form of “learning,” Hellingwerf and colleagues wrote in the August, 2001 
issue of the Journal of Bacteriology. 

Brain cells can operate in an analogous way: a brain cell can grow more 
sensitive to a signal that it receives repeatedly, resulting in a reinforcement of 
signaling circuits and learning. 

The bacterial versions of “mousetrap” have other tricks as well. For 
instance, some of them seem to contain components influenced by not just one 
stimulus, but by two or more. Thus the chain reactions merge. The component receiving 
these stimuli adds the strength of each to give a response whose strength is 
proportional to the sum.

Although the full complexities of bacterial signaling are far from 
understood, many researchers believe the systems helps bacteria to communicate.

For instance, some bacteria, when starving, emit molecules that serve as 
stress signals to their neighbors, write Eshel Ben-Jacob of Tel Aviv University 
and colleagues in last August’s issue of Trends in Microbiology. The signals 
launch a process in which the group can transform itself to create tough, walled 
structures that wait out tough times to reemerge later.

This transformation involves a complex dialogue that reveals a “social 
intelligence,” the researchers added. Each bacterium uses the signals to assess the 
group’s condition, compares this with its own state, and sends out a molecular 
“vote” for or against transformation. The majority wins.

Collectively, the researchers wrote, “bacteria can glean information from the 
environment and from other organisms, interpret the information in a ‘
meaningful’ way, develop common knowledge and learn from past experience.” Some can 
even collectively change their chemical “dialect” to freeze out “cheaters” 
who exploit group efforts for their own selfish interest, the researchers 
claimed.

Not everyone is convinced by these claims. 

Rosemary J. Redfield of the University of British Columbia, Vancouver, has 
argued that the supposed communication molecules actually exist mainly to tell 
bacteria how closed-in their surroundings are, which is useful information to 
them for various reasons.

Inside-out

To properly assess if bacterial signals constitute intelligence, whether of a 
social or individual brand, Hellingwerf and some other researchers work from 
the inside out. 

Rather than focusing on the behaviors, which are open to differing 
interpretations, they focus on the systems of interactions followed by the molecules. 
These systems, it is hoped, have distinct properties that can be measured and 
compared against similar interactions in known intelligent beings.

For instance, if these bacterial systems operate similarly to networks in the 
brain, it would provide a weighty piece of evidence in favor of the bacterial 
intelligence.

Hellingwerf has set himself a more modest goal, comparing bacterial signaling 
not to the brain, but to the brain-like, human-made neural network devices. 
Such an effort has a simple motivation. Demonstrating that bacterial signaling 
possesses every important feature of neural networks would suggest at least 
that microbial capabilities rival those of devices with proven ability to tackle 
simple problems using known rules of brain function—rather than robot-like 
calculations, which are very different.

To understand how one could do such a comparison requires a brief explanation 
of how neural networks work, and how they differ from traditional computers. 

Computers are good at following precise instructions, but terrible at even 
simple, common-sense tasks that lack definite rules, like the recognition of the 
difference between male and female.

Neural networks, like humans, can do this because they are more flexible, and 
they learn—even though they can be built using computers. They are a set of 
simulated “brain cells” set to pass “signals” among themselves through 
simulated “connections.”

Some information that can be represented as a set of numbers, such as a 
digitized photograph, is fed to a first set of “cells” in such a way that each 
cell gets a number. Each cell is then set to “transmit” all, part or none of 
that number to one or more other cells. How big a portion of the number is passed 
on to each, depends on the simulated “strength” of the connections that are 
programmed into the system.

Each of those cells, in turn, are set to do something with the numbers they 
receive, such as add them or average them—and then transmit all or part of them 
to yet another cell.

Numbers ricochet through the system this way until they arrive at a final set 
of “output” cells. These cells are set to give out a final answer—based on 
the numbers in them—in the form of yet another number. For example, the answer 
could be 0 for male, 1 for female.

Such a system, when new, will give random answers, because the connections 
are initially set at random. However, after each attempt at the problem, a human 
“tells” the system whether it was right or wrong. The system is designed to 
then change the strength of the connections to improve the answer for the next 
try. 

To do this, the system calculates to what extent a change in strength of each 
connection previously contributed to giving a right or wrong answer. This 
information tells the system how to change the strengths to give better results. 
Over many attempts, the system’s accuracy gradually improves, often reaching 
nearly human-like performance on a given task. 

Such systems not only work quite well for simple problems, many researchers 
believed they capture all the key features of real brain cells, though in a 
drastically simplified way.

The devices also have similarities to the messaging systems in bacteria. But 
how deep are the resemblances? To answer this, Hellingwerf looked at four 
properties that neural-network experts have identified as essential for such 
devices to work. He then examined whether bacterial signaling fits each of the 
criteria.

The four properties are as follows. 

First, a neural network must have multiple sub-systems that work 
simultaneously, or “in parallel.” Neural networks do this, because signals follow 
multiple pathways at once, in effect carrying out multiple calculations at once. 
Traditional computers can’t do this; they conduct one at a time. Bacteria do fit 
the standard, though, because they can contain many messaging networks acting 
simultaneously, Hellingwerf observes.

Second, key components of the network must carry out logical operations. This 
means, in the case of a neural network, that single elements of the network 
combine signals from two or more other elements, and pass the result on to a 
third according to some mathematical rule. Regular computers also have this 
feature. Bacteria probably do too, Hellingwerf argues, based on the way that parts 
of their signaling systems add up inputs from different sources.

The third property is “auto-amplification.” This describes the way some 
network elements can boost the strength of their own interactions. Hellingwerf 
maintains that bacteria show this property, as when, for example, some of their 
signaling systems create more copies of themselves as they run.

The fourth property is where the rub lies for bacteria. This feature, called 
crosstalk, means that the system must not consist just of separate chain 
reactions: rather, different chain reactions have to connect, so that the way one 
operates can change the way another runs.

Crosstalk is believed to underlie an important form of memory called 
associative memory, the ability to mentally connect two things with no obvious 
relationship. A famous example is the Russian scientist Ivan Pavlov’s dog, who 
drooled at the ring of a bell because experience had taught him food invariably 
followed the sound.

Crosstalk has been found many times in bacteria, Hellingwerf wrote—but the 
strength of the crosstalk “signals” are hundreds or thousands of times weaker 
than those that follow the main tracks of the chain reactions. Moreover, “clear 
demonstrations of associative memory have not yet been detected in any single 
bacterial cell,” he added, and this is an area ripe for further research. If 
bacteria can indeed communicate, it seems they may be holding quite a bit back 
from us. 
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