as network effects

Abstract. It has been proposed that chaos can serve as a reservoir providing an infinite number of dynamical states [1, 2, 3, 4, 5]. These can be interpreted as different behaviors, search actions or computational states which are selectively adequate for different tasks. The high flexibility of chaotic regimes has been noted, as well as other advantages over regular regimes. However, the model neurons used to demonstrate these ideas could be criticized as lacking physical or biological realism. In the present paper we show that the same kind of rich behavior displayed by the toy models can be found with a more realistic neural model [6]. Furthermore, much of the complex behavior arises from network properties often overlooked in the literature. 1 Chaotic spatiotemporal neural chaos and its use Following the discovery of putative chaotic regimes in electrical signals from the brain, and much scientific speculation as to the possible roles of chaos in cognition, actual computational models were proposed [1, 2, 3, 4, 5]. These model