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 those 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.
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, and which solves some simple problems in a manner more human-like than machine-like.
These machines, called neural networks, 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, in recent years, closer to the concept that bacteria might possess a crude intelligence—though very 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, for it, 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, such as when some strains of
Escherichia coli commit suicide upon infection by a virus, 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
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
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.
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.