Activity-dependent modification of inhibitory synapses in models of rhythmic neural networks

The faithful production of rhythms by many neural circuits depends critical ly on the strengths of inhibitory synaptic connections. We propose a model in which the strengths of inhibitory synapses in a central pattern-generati ng circuit are subject to activity-dependent plasticity. The strength of ea ch synapse is modified as a function of the global activity of the postsyna ptic neuron and by correlated activity of the pre- and postsynaptic neurons . This allows the self-assembly, from random initial synaptic strengths, of two cells into reciprocal oscillation and three cells into a rhythmic trip hasic motor pattern. This self-assembly illustrates that complex oscillator y circuits that depend on multiple inhibitory synaptic connections can be t uned via simple activity-dependent rules.