Amoebas are smarter than they look, and a team of US physicists think they know why. The group has built a simple electronic circuit that is capable of the same “intelligent” behaviour as Physarum, a unicellular organism – and say this could help us understand the origins of primitive intelligence.
In recent years, the humble amoeba has surprised researchers with its ability to behave in an “intelligent” way. Last year, Liang Li and Edward Cox at Princeton University reported that the Dictyostelium amoeba is twice as likely to turn left if its last turn was to the right and vice versa, which suggests the cells have a rudimentary memory.
This year, Toshiyuki Nakagaki at Hokkaido University in Sapporo, Japan, won an Ig Nobel prize for his work on amoeba intelligence after his team found further evidence of the amoeba memory effect. They exposed Physarum amoeba to temperatures fluctuating regularly between cold and warm. It was already known that the cells become sluggish during cold snaps, but Nakagaki’s team found that the amoeba slowed down in anticipation of cold conditions, even when the temperature changes had stopped (Physical Review Letters, DOI: 10.1103/PhysRevLett.100.018101).
In the past, biologists have suggested that there are natural oscillators within the cells that can change their frequency in response to a changing environment. But that can’t be the complete picture, say the researchers, because the amoeba’s response is short-lived. For instance, although Physarum “learned” to anticipate cold spikes in Nakagaki’s experiments, it quickly “realised” that those cold spikes were no longer occurring and stopped anticipating them.
Instead, Di Ventra’s team thinks there is an intrinsic memory storage device within the amoeba. As with the human brain, that device can strengthen and store memories for some time. But if the memory isn’t used, it gradually fades away.
Now they have identified a potential storage device. The amoeba’s interior contains a watery sol – a solid suspended in liquid – within a thick viscous gel. The sol flows through the gel like water through a sponge, creating a network of low-viscosity channels. Those channels are strengthened as long as the amoeba continues to respond to a static environment, but if that environment changes the channels gradually break down and a new network appears as the amoeba adapts. For a short while, though, the amoeba retains a “memory” of those earlier conditions.
Di Ventra’s team took advantage of the development this year of memristors – electrical resistors that retain a memory of earlier voltages or currents applied and vary their resistance accordingly – to design a simple circuit that models the amoeba’s gel-sol system. Their circuit contained just four basic elements: a resistor, capacitor, inductor and memristor. By changing the external voltage in a regular way they could model the changing temperature conditions studied by Nakagaki’s team. When they did this, they found that their circuit could “learn” and predict future voltage fluctuations.
“It appears that our model describes pretty well the experiments on amoebas’ learning,” says Di Ventra. He cautions there is a huge gap between the simple response of single-celled animals and the cognitive abilities of developed species, but adds there is no doubt that a combined set of simple circuit models will have more complex behaviour. “This is in fact what we are now interested in studying,” he says.
But others remain unconvinced that the behaviour of amoebas can be explained by circuit models. “In my view, simulation is not an adequate approach for such a deep biological problem as intelligence,” says Guenter Albrecht-Buehler at Northwestern University Medical School in Chicago. “We need to find out how it’s really done in a living cell.” He points out that all sorts of electronic models could have simulated genetic memory but wouldn’t have helped to understand how cells achieve that particular trick. “Finding the double helix of DNA was the only achievement that really mattered,” he says.
Klaas Hellingwerf at the University of Amsterdam in the Netherlands questions the Di Ventra team’s definition of the amoeba’s behaviour. “I have reservations in calling this learning’, let alone intelligence’,” he says. Detecting and anticipating regular signals doesn’t imply learning, he explains. “I think learning implies more – for instance, associative memory.” An example of associative memory would be if the cell learned a response to one stimulus and could then use that response when faced with a second stimulus that is similar to the first.
A pre-print of the Di Ventra paper is available at arXiv: 0810.4179v2.
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