A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network.
The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes.
The soup of recurrently connected nodes will end up computing a large variety of nonlinear functions on the input.
The input (motion of the falling stone) has been converted into a spatio-temporal pattern of liquid displacement (ripples).
LSMs are argued to be an improvement over the theory of artificial neural networks because: Criticisms of LSMs as used in computational neuroscience are that If a reservoir has fading memory and input separability, with help of a readout, it can be proven the liquid state machine is a universal function approximator using Stone–Weierstrass theorem.