Liquid state machines have been engineered so that their dynamics hover near the “edge of chaos” [1], [2], where memory and representational capacity of the liquid were shown to be optimized. Previous work found the critical line between ordered and chaotic dynamics for threshold gates by using an analytic method similar to finding Lyapunov exponents [3]. In the present study, a self-tuning algorithm is developed for use with leaky integrate-and-fire (LIF) neurons that adjusts postsynaptic weights to a critical branching point between subcritical and supercritical spiking dynamics. The tuning algorithm stabilizes spiking activity in the sense that spikes propagate through the network without multiplying to the point of wildfire activity, and without dying out so quickly that information cannot be transmitted and processed. The critical branching point is also found to maximize memory and representational capacity of the network when used as liquid state machine.
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