computer science

Liquid state machine

Liquid state machine is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Liquid state machine rather than just read about it. In short: A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons).

Key takeaways

Liquid state machine belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Liquid state machine to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Liquid state machine from memory before moving on to harder problems.

Reference excerpt

A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. The spatio-temporal patterns of activation are read out by linear discriminant units. The soup of recurrently connected nodes will end up computing a large variety of nonlinear functions on the input. Given a large enough variety of such nonlinear functions, it is theoretically possible to obtain linear combinations (using the read out units) to perform whatever mathematical operation is needed to perform a certain task, such as speech recognition or computer vision. The word liquid in the name comes from the analogy drawn to dropping a stone into a still body of water or other liquid. The falling stone will generate ripples in the liquid. The input (motion of the falling stone) has been converted into a spatio-temporal pattern of liquid displacement (ripples). LSMs have been put forward as a way to explain the operation of brains. LSMs are argued to be an improvement over the theory of artificial neural networks because:

Circuits are not hard coded to perform a specific task. Continuous time inputs are handled "naturally". Computations on various time scales can be done using the same network. The same network can perform multiple computations. Criticisms of LSMs as used in computational neuroscience are that

LSMs don't actually explain how the brain functions. At best they can replicate some parts of brain functionality. There is no guaranteed way to dissect a working network and figure out how or what computations are being performed. There is very little control over the process.

Universal function approximation 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.

See also Echo state network: similar concept in recurrent neural network Reservoir computing: the conceptual framework Self-organizing map

Libraries LiquidC#: Implementation of topologically robust liquid state machine with a neuronal network detector [1]

References

Maass, Wolfgang; Natschläger, Thomas; Markram, Henry (November 2002), "Real-time computing without stable states: a new framework for neural computation based on perturbations" (PDF), Neural Comput, 14 (11): 2531–60, CiteSeerX 10.1.1.183.2874, doi:10.1162/089976602760407955, PMID 12433288, S2CID 1045112, archived from the original on February 22, 2012. {{citation}}: Cite uses deprecated parameter |citeseerx= (help) Wolfgang Maass; Thomas Natschläger; Henry Markram (2004), "Computational Models for Generic Cortical Microcircuits" (PDF), In Computational Neuroscience: A Comprehensive Approach, Ch 18, 18: 575–605

Worked examples

Example 1 — a first encounter with Liquid state machine

Start with the simplest possible case. Write down what Liquid state machine claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Liquid state machine before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Liquid state machine ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Liquid state machine

In research: Liquid state machine appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Liquid state machine in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Liquid state machine is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial neural networks, so understanding it makes those chapters shorter.
In everyday life: Look for Liquid state machine outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Liquid state machine in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Liquid state machine means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Liquid state machine out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Liquid state machine in simple terms?

A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons).

Why does Liquid state machine matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Liquid state machine?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Liquid state machine.