– Recently developed computational model

– does not require information to be stored in some stable state of the system

→ the inherent dynamics of the system are used by a memory less readout function to compute the output

→ can be used for complex Tasks (pattern classification, function approximation, object tracking, …)

LSMs take the temporal aspect of the input into account


The figure shows a typical structure of a liquid State Machine.
Liquid State Machine

Reservoir/ Liquid

– large accumulation of recurrent interacting nodes
→ is stimulated by the input layer
– Liquid itself is not trained, but randomly constructed with the help of heuristics
– Loops cause a short-term memory effect
– preferably a Spiking Neural Network (SNNs)
→ are closer to biological neural networks than the multilayer Perceptron
→ can be any type of network that has sufficient internal dynamics

Running State

→ will be extracted by the readout function

– depend on the input streams they’ve been presented

Readout Function

– converts the high-dimensional state into the output

– since the readout function is separated from the liquid, several readout functions can be used with the same liquid

→ so different tasks can be performed with the same input

lsm readout fcts
different types of readout functions