computer science

Large memory storage and retrieval neural network

Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network rather than just read about it. In short: A large memory storage and retrieval neural network (LAMSTAR) is a fast deep learning neural network of many layers that can use many filters simultaneously. These filters may be nonlinear, stochastic, logic, non-stationary, or even non-analytical.

Key takeaways

Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Large memory storage and retrieval neural network from memory before moving on to harder problems.

Reference excerpt

A large memory storage and retrieval neural network (LAMSTAR) is a fast deep learning neural network of many layers that can use many filters simultaneously. These filters may be nonlinear, stochastic, logic, non-stationary, or even non-analytical. They are biologically motivated and learn continuously. A LAMSTAR neural network may serve as a dynamic neural network in spatial or time domains or both. Its speed is provided by Hebbian link-weights that integrate the various and usually different filters (preprocessing functions) into its many layers and to dynamically rank the significance of the various layers and functions relative to a given learning task. This vaguely imitates biological learning that integrates various preprocessors (cochlea, retina, etc.) and cortexes (auditory, visual, etc.) and their various regions. Its deep learning capability is further enhanced by using inhibition, correlation and by its ability to cope with incomplete data, or "lost" neurons or layers even amidst a task. It is fully transparent due to its link weights. The link-weights allow dynamic determination of innovation and redundancy, and facilitate the ranking of layers, of filters or of individual neurons relative to a task. LAMSTAR has been applied to many domains, including medical and financial predictions, adaptive filtering of noisy speech in unknown noise, still-image recognition, video image recognition, software security and adaptive control of non-linear systems. LAMSTAR had a much faster learning speed and somewhat lower error rate than a CNN based on ReLU-function filters and max pooling, in 20 comparative studies. These applications demonstrate delving into aspects of the data that are hidden from shallow learning networks and the human senses, such as in the cases of predicting onset of sleep apnea events, of an electrocardiogram of a fetus as recorded from skin-surface electrodes placed on the mother's abdomen early in pregnancy, of financial prediction or in blind filtering of noisy speech. LAMSTAR was proposed in 1996 and was further developed Graupe and Kordylewski from 1997–2002. A modified version, known as LAMSTAR 2, was developed by Schneider and Graupe in 2008.

References

Worked examples

Example 1 — a first encounter with Large memory storage and retrieval neural network

Start with the simplest possible case. Write down what Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network

In research: Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network 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: Large memory storage and retrieval neural network is common in secondary-school and first-year university syllabi. It links to neighbouring topics Deep learning, so understanding it makes those chapters shorter.
In everyday life: Look for Large memory storage and retrieval neural network 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.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Large memory storage and retrieval neural network” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Large memory storage and retrieval neural network in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Large memory storage and retrieval neural network in simple terms?

Why does Large memory storage and retrieval neural network 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 Large memory storage and retrieval neural network?

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 Large memory storage and retrieval neural network.