It Takes Two Neurons To Ride a Bicycle

Past attempts to get computers to ride bicycles have required an inordinate amount of learning time (1700 practice rides for a reinforcement learning approach [1], while still failing to be able to ride in a straight line), or have required an algebraic analysis of the exact equations of motion for the specific bicycle to be controlled [2, 3]. Mysteriously, humans do not need to do either of these when learning to ride a bicycle. Here we present a two-neuron network1 that can ride a bicycle in a desired direction (for example, towards a desired goal or along a desired path), which may be chosen or changed at run time. Just as when a person rides a bicycle, the network is very accurate for long range goals, but in the short run stability issues dominate the behavior. This happens not by explicit design, but arises as a natural consequence of how the network controls the bicycle.