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...

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.

The task of riding a bicycle presents an interesting challenge, whether for human or for computer. We do not have great insight as to how we ride a bicycle, and we do not have much useful advice for someone who is learning. In fact, in the course of this project, I had the chance to ride a “virtual bicycle” on the computer, and I was surprised to find how counterintuitive it is...