Zoologists studied the nervous systems of insects to investigate principles of biological brain computation and possible effects on machine learning and artificial intelligence. Specifically, they analysed how insects learn to associate sensory information in their environment with a food reward, and how they can recall this information later in order to solve complex tasks such as the search for food.

Living organisms show remarkable abilities in coping with problems posed by complex and dynamic environments. They are able to generalize their experiences in order to rapidly adapt their behaviour when the environment changes. The zoologists investigated how the nervous system of the fruit fly controls its behaviour when searching for food.

Using a computer model, they simulated(模拟) and analysed the computations in the fruit fly's nervous system in response to scents coming from the food source. They initially trained their model of the fly brain in exactly the same way as insects are trained in experiments. They presented a specific scent in the simulation together with a reward and a second scent without a reward.

"The model rapidly learns a strong representation of the rewarded scent after just a few scent presentations and is then able to find the source of this scent in a complex environment," said computer scientist Dr Hannes Rapp, who created the model.

The model created is thus capable of generalizing from its memory and to apply what it has learned previously in a completely new and complex environment, while learning required only a very small database of training samples.

The results suggest that the transformation of sensory information into memories in the brain can inspire future machine learning and artificial intelligence applications to solving complex tasks.