‘Cyberslug’ is based on Pleurobranchaea californica, a grapefruit-sized sea slug which crawls across the floor of the Pacific Ocean, attempting to eat most creatures it encounters. Unlike most other artificially intelligent entities, Cyberslug has very basic self-awareness.

“That is, it relates its motivation and memories to its perception of the external world and it reacts to information on the basis of how that information makes it feel,” said Professor Rhanor Gillette, a molecular and integrative physiology expert at Illinois, who led the project with software engineer Mikhail Voloshin.

Cyberslug has learnt what other types of virtual sea slugs are good to eat, which ones are less tasty and which could fight back if it attempts to eat them. According to Gillette, sea slugs typically choose between three responses when encountering other creatures in the wild: fleeing, feeding or mating.

In order to make the correct choice and avoid embarrassment (or injury), sea slugs must be aware of their own state by recognising hunger, take scent cues from its environment and recollect past encounters with similar creatures.

In the software simulation, Cyberslug interacts with creatures which could be nutritious, but may also have defensive toxic spines. When the virtual slug is hungrier, it may attempt to eat these spiny creatures. The public can play with cyberslug online and experiment with the virtual beast themselves.

“Their default response is avoidance, but hunger, sensation and learning together form their ‘appetitive state’, and if that is high enough the sea slug will attack,” said Gillette. “When P. californica is super hungry, it will even attack a painful stimulus. And when the animal is not hungry, it usually will avoid even an appetitive stimulus; this is a cost-benefit decision.”

Previously, Gillette and his colleagues had mapped some of the brain circuitry of sea slugs, down to individual neurons. Now, they have charted enough of the brain in order to build a fairly accurate software simulation of the sea slug which uses more sophisticated algorithms to simulate the creature’s decision-making processes.

The slug’s brain has only a few thousand neurons; enough to allow it to learn from previous experiences and make informed decisions. Gillette says that despite the simplicity of these brains, the simulation could help us learn a lot about how our own brains work.

“I think the sea slug is a good model of the core ancient circuitry that is still there in our brains that is supporting all the higher cognitive qualities,” said Gillette. “Now we have a model that’s probably very much like the primitive ancestral brain. The next step is to add more circuitry to get enhanced sociality and cognition.”

Artificial neural networks are a type of machine-learning program modelled approximately on the structure of a real brain. In February, Austrian researchers announced that they had translated the entire neural system of a simple worm into software and taught it to balance a pole on the tip of its virtual tail.

‘Cyberslug’ is based on Pleurobranchaea californica, a grapefruit-sized sea slug which crawls across the floor of the Pacific Ocean, attempting to eat most creatures it encounters. Unlike most other artificially intelligent entities, Cyberslug has very basic self-awareness.

“That is, it relates its motivation and memories to its perception of the external world and it reacts to information on the basis of how that information makes it feel,” said Professor Rhanor Gillette, a molecular and integrative physiology expert at Illinois, who led the project with software engineer Mikhail Voloshin.

Cyberslug has learnt what other types of virtual sea slugs are good to eat, which ones are less tasty and which could fight back if it attempts to eat them. According to Gillette, sea slugs typically choose between three responses when encountering other creatures in the wild: fleeing, feeding or mating.

In order to make the correct choice and avoid embarrassment (or injury), sea slugs must be aware of their own state by recognising hunger, take scent cues from its environment and recollect past encounters with similar creatures.

In the software simulation, Cyberslug interacts with creatures which could be nutritious, but may also have defensive toxic spines. When the virtual slug is hungrier, it may attempt to eat these spiny creatures. The public can play with cyberslug online and experiment with the virtual beast themselves.

“Their default response is avoidance, but hunger, sensation and learning together form their ‘appetitive state’, and if that is high enough the sea slug will attack,” said Gillette. “When P. californica is super hungry, it will even attack a painful stimulus. And when the animal is not hungry, it usually will avoid even an appetitive stimulus; this is a cost-benefit decision.”

Previously, Gillette and his colleagues had mapped some of the brain circuitry of sea slugs, down to individual neurons. Now, they have charted enough of the brain in order to build a fairly accurate software simulation of the sea slug which uses more sophisticated algorithms to simulate the creature’s decision-making processes.

The slug’s brain has only a few thousand neurons; enough to allow it to learn from previous experiences and make informed decisions. Gillette says that despite the simplicity of these brains, the simulation could help us learn a lot about how our own brains work.

“I think the sea slug is a good model of the core ancient circuitry that is still there in our brains that is supporting all the higher cognitive qualities,” said Gillette. “Now we have a model that’s probably very much like the primitive ancestral brain. The next step is to add more circuitry to get enhanced sociality and cognition.”

Artificial neural networks are a type of machine-learning program modelled approximately on the structure of a real brain. In February, Austrian researchers announced that they had translated the entire neural system of a simple worm into software and taught it to balance a pole on the tip of its virtual tail.