An advantage of chaotic neural dynamics

One hypothesis about how biological neural systems work suggests that they use attractor dynamics to define their behaviour. Such behaviour can be modelled using recurrent neural network models. It has been shown that such systems can perform a wide range of computational tasks by learning abstract grammars. Here we show that chaotic neural dynamics in recurrent neural systems is advantageous in the sense that it facilitates the encoding of grammars describing complex behaviour. This result may explain why it is common the observation of chaotic dynamics in biological neural systems.