  Large networks of spiking neurons show abrupt changes in their collective
dynamics resembling phase transitions studied in statistical physics. An
example of this phenomenon is the transition from irregular, noise-driven
dynamics to regular, self-sustained behavior observed in networks of
integrate-and-fire neurons as the interaction strength between the neurons
increases. In this work we show how a network of spiking neurons is able to
self-organize towards a critical state for which the range of possible
inter-spike-intervals (dynamic range) is maximized. Self-organization occurs
via synaptic dynamics that we analytically derive. The resulting plasticity
rule is defined locally so that global homeostasis near the critical state is
achieved by local regulation of individual synapses.
