We present evidence for chaos and generalised multistability in a mesoscopic model of the electroencephalogram (EEG). Two limit cycle attractors and one chaotic attractor were found to coexist in a two-dimensional plane of the tendimensional volume of initial conditions. The chaotic attractor was found to have a moderate value of the largest Lyapunov exponent (3.4 s−1 base e) with ∗Correspondin...

Attractor networks are widely believed to underlie the memory systems of animals across different species. Existing models have succeeded in qualitatively modeling properties of attractor dynamics, but their computational abilities often suffer from poor representations for realistic complex patterns, spurious attractors, low storage capacity, and difficulty in identifying attractive fields of ...

Maintaining a sense of direction requires combining information from static environmental landmarks with dynamic information about self-motion. This is accomplished by the head direction system, whose neurons - head direction cells - encode specific head directions. When the brain integrates information in sensory domains, this process is almost always 'optimal' - that is, inputs are weighted a...

We describe an integrate-and-fire attractor model of the decision-related activity of ventral premotor cortex (VPC) neurons during a vibrotactile frequency comparison task [Romo et al. (2004)Neuron, 41, 165-173]. Populations of neurons for each decision in a biased competition attractor network receive a bias input that depends on the firing rates of VPC neurons that code for the two vibrotacti...

How can we uncover the natural communities in a real network that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots the communities or groups in a network over time via local dynamic interaction. The basic idea is to envision a network as a dynamical system, and ...