computer science

Modular neural network

Modular 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 Modular neural network rather than just read about it. In short: A modular neural network is an artificial neural network characterized by a series of independent neural networks moderated by some intermediary, such as a cohomological structure of Cohomology Theory. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform.

Key takeaways

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

Reference excerpt

A modular neural network is an artificial neural network characterized by a series of independent neural networks moderated by some intermediary, such as a cohomological structure of Cohomology Theory. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform. The intermediary takes the outputs of each module and processes them to produce the output of the network as a whole. The intermediary only accepts the modules' outputs—it does not respond to, nor otherwise signal, the modules. As well, the modules do not interact with each other.

Biological basis As artificial neural network research progresses, it is appropriate that artificial neural networks continue to draw on their biological inspiration and emulate the segmentation and modularization found in the brain. The brain, for example, divides the complex task of visual perception into many subtasks. Within a part of the brain, called the thalamus, lies the lateral geniculate nucleus (LGN), which is divided into layers that separately processes color and contrast: both major components of vision. After the LGN processes each component in parallel, it passes the result to another region to compile the results. Some tasks that the brain handles, like vision, employ a hierarchy of sub-networks. However, it is not clear whether some intermediary ties these separate processes together. Rather, as the tasks grow more abstract, the modules communicate with each other, unlike the modular neural network model.

Design Unlike a single large network that can be assigned to arbitrary tasks, each module in a modular network must be assigned a specific task and connected to other modules in specific ways by a designer. In the vision example, the brain evolved (rather than learned) to create the LGN. In some cases, the designer may choose to follow biological models. In other cases, other models may be superior. The quality of the result will be a function of the quality of the design.

Complexity Modular neural networks reduce a single large, unwieldy neural network to smaller, potentially more manageable components. Some tasks are intractably large for a single neural network. The benefits of modular neural networks include:

Efficiency The possible neuron (node) connections increase quadratically as nodes are added to a network. Computation time depends on the number of nodes and their connections, any increase has drastic consequences for processing time. Assigning specific subtasks to individual modules reduce the number of necessary connections.

Training A large neural network attempting to model multiple parameters can suffer from interference as new data can alter existing connections or just serve to confuse. Each module can be trained independently and more precisely master its simpler task. This means the training algorithm and the training data can be implemented more quickly.

Robustness Regardless of whether a large neural network is biological or artificial, it remains largely susceptible to interference at and failure in any one of its nodes. By compartmentalizing subtasks, failure and interference are much more readily diagnosed and their effects on other sub-networks are eliminated as each one is independent of the other.

Worked examples

Example 1 — a first encounter with Modular neural network

Start with the simplest possible case. Write down what Modular 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 Modular 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 Modular 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 Modular neural network

In research: Modular 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 Modular 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: Modular neural network is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational neuroscience, Modularity, Neural network architectures, so understanding it makes those chapters shorter.
In everyday life: Look for Modular 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.

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How to study Modular neural network in 20 minutes

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

Frequently asked questions

What is Modular neural network in simple terms?

Why does Modular 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 Modular 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 Modular neural network.