  We propose a novel Chaotic Associative Memory model using a network of
chaotic Rossler systems and investigate the storage capacity and retrieval
capabilities of this model as a function of increasing periodicity and chaos.
In early models of associate memory networks, memories were modeled as fixed
points, which may be mathematically convenient but has poor neurobiological
plausibility. Since brain dynamics is inherently oscillatory, attempts have
been made to construct associative memories using nonlinear oscillatory
networks. However, oscillatory associative memories are plagued by the problem
of poor storage capacity, though efforts have been made to improve capacity by
adding higher order oscillatory modes. The chaotic associative memory proposed
here exploits the continuous spectrum of chaotic elements and has higher
storage capacity than previously described oscillatory associate memories.
