The closed-loop design we discuss here consists of four unique elements:
1.Patterned stimulation to induce network plasticity. This low-frequency (~3 Hz) training stimulation differs from most studies of cultured networks, where plasticity was induced by high frequency tetanic stimulations [15],[17].
2.Continuous low-frequency background stimulation (~3 Hz) to stabilize accumulated plasticity [19], which is analogous to continuous sensory inputs and ongoing processing in the brain.
3.
Population coding for motor mapping. Population coding is considered a robust means to represent movement directions in the primary motor cortex [21].
4.
Adaptive selection of training stimulation. Because the connectivity in a cultured network is not predictable, the effects of a given training stimulation cannot be known a priori. Thus we delivered training stimulation contingent on the animat's performance in order to direct changes in network connectivity that further shift the animat's behavior toward the desired behavior.
We developed tools [1]–[3] for closing the sensory-motor loop between a cultured network and a robot or an artificial animal (animat) [4] in order to study learning directly through behavior of the artificial body and its interaction with its environment
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