The organization of seismicity on fault networks.

Although models of homogeneous faults develop seismicity that has a Gutenberg-Richter distribution, this is only a transient state that is followed by events that are strongly influenced by the nature of the boundaries. Models with geometrical inhomogeneities of fracture thresholds can limit the sizes of earthquakes but now favor the characteristic earthquake model for large earthquakes. The character of the seismicity is extremely sensitive to distributions of inhomogeneities, suggesting that statistical rules for large earthquakes in one region may not be applicable to large earthquakes in another region. Model simulations on simple networks of faults with inhomogeneities of threshold develop episodes of lacunarity on all members of the network. There is no validity to the popular assumption that the average rate of slip on individual faults is a constant. Intermediate term precursory activity such as local quiescence and increases in intermediate-magnitude activity at long range are simulated well by the assumption that strong weakening of faults by injection of fluids and weakening of asperities on inhomogeneous models of fault networks is the dominant process; the heat flow paradox, the orientation of the stress field, and the low average stress drop in some earthquakes are understood in terms of the asperity model of inhomogeneous faulting.

Self-organized phase transitions in neural networks as a neural mechanism of information processing.

Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity patterns encoding usual entities. We propose that the infinitesimal and short-term synaptic changes based on the Hebbian learning rule are the driving force for the transition. The phase transition between the following two dynamical stable states is studied in detail, the state where the firing pattern is changed temporally so as to itinerate among several patterns and the state where the firing pattern is fixed to one of several patterns. The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbian learning process under a weak input relevant to the fixed pattern. The reverse transition may be induced by the Hebbian unlearning process without input. The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input. To ensure that information processing based on the phase transition can be made by the infinitesimal and short-term synaptic changes, it is absolutely necessary that the network always stays near the critical state corresponding to the phase transition point.