The complexity of latching transitions in large scale cortical networks

Abstract. We study latching dynamics, i.e. the ability of a network to hop spontaneously from one discrete attractor state to another, which has been proposed as a model of an infinitely recursive process in large scale cortical networks, perhaps associated with higher cortical functions, such as language. We show that latching dynamics can span the range from deterministic to random under the control of a threshold parameter U. In particular, the interesting intermediate case is characterized by an asymmetric and complex set of transitions. We also indicate how finite latching sequences can become infinite, depending on the properties of the transition probability matrix and of its eigenvalues.

• Type: Journal
• Year: 2007
• Where: NC
• Authors: Emilio Kropff, Alessandro Treves
• Keywords: Discrete Attractor State, NC 2007, Neural Networks, Scale Cortical Networks, Threshold Parameter U