representational capacity

USAGE SUMMARY

The phrase "representational capacity" is correct and usable in written English.
It can be used in contexts discussing the ability of something to represent or depict information, ideas, or entities. Example: "The artist's work demonstrates a remarkable representational capacity, capturing the essence of the subject with great detail."

✓ Grammatically correct

Science

Academia

Human-verified examples from authoritative sources

Exact Expressions

17 human-written examples

The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex.

Nature

It naturally inherits the representational capacity enlargement property of NFL and offers many other benefits in accuracy and efficiency.

Neurocomputing

The main point made in this article is that the representational capacity and inherent function of any neuron, neuronal population or cortical area is dynamic and context-sensitive.

Brain Research Bulletin

With this in mind, we herein explore the representational capacity of two related forms of neural networks: synfire chains, and polychronic networks.

Procedia Computer Science

The incorporation of delay based computing, or polychronicity, into models of neural networks has helped to increase the memory and representational capacity of spiking neural networks.

Procedia Computer Science

And Shaftesbury, too, localizes beauty to the representational capacity of the mind.

SEP

Human-verified similar examples from authoritative sources

Similar Expressions

43 human-written examples

Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available (Kurup & Chandrasekaran, 2007).

Biologically Inspired Cognitive Architectures

Though not ultimately successful, a first and plausible reply to the representation problem is to deny that mental representation is a problem for an overarching naturalism on the grounds that representational capacities are to be found throughout the natural, physical world.

Evolution: Education and Outreach

We reveal their similarities and study their strengths and weaknesses in terms of the representational capacities, intuitiveness, exactness, compactness, efficiency and other criteria.

Computer-Aided Design

We find that the computational capacity of such cellular assembly based networks increases with the size of between-neural-pool time delays and that for relatively small changes in time delay, linear increases in network representational capacities are obtained.

Procedia Computer Science

The most recent generation of artificial neural networks, third generation networks, consist of spiking neuron models that attempt to mimic the complex dynamic features exhibited by real biological neurons in the hopes of improvements in computational and representational capacities.

Procedia Computer Science

Expert writing Tips

Best practice

When discussing cognitive systems or neural networks, use "representational capacity" to refer to the ability to encode and process information effectively. This term highlights the limitations and capabilities of the system in handling complex data.

Common error

Avoid using "representational capacity" interchangeably with representational fidelity. Capacity refers to the amount of information that can be represented, while fidelity refers to the accuracy and faithfulness of the representation.

Linguistic Context

The primary grammatical function of "representational capacity" is as a noun phrase. It typically acts as the subject or object of a sentence, describing the ability or potential of a system to represent information. Ludwig provides examples across various scientific domains, illustrating its widespread use.

Expression frequency: Uncommon

Frequent in

Science

65%

Academia

25%

Encyclopedias

10%

Less common in

News & Media

0%

Formal & Business

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "representational capacity" is a noun phrase denoting the ability of a system to represent information, commonly used in scientific and academic contexts. Ludwig's analysis reveals that it appears frequently in scientific literature, particularly when discussing neural networks, cognitive systems, and machine learning models. While grammatically correct, it's important not to confuse it with "representational fidelity". As Ludwig AI underlines, using precise alternatives such as "depictive capability" or "expressive potential" can enhance clarity depending on the specific context.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

depictive capability

Emphasizes the ability to depict or portray something.

expressive potential

Focuses on the potential for expression or communication.

cognitive bandwidth

Highlights the processing resources available for representation.

symbolic depth

Emphasizes the depth and complexity of symbolic representation.

modeling competence

Focuses on the ability to create accurate models or representations.

illustrative power

Highlights the power to illustrate or make something clear through representation.

encoding proficiency

Focuses on the efficiency and skill in encoding information.

descriptive range

Emphasizes the breadth and scope of descriptive capabilities.

interpretive flexibility

Highlights the ability to interpret and represent information in various ways.

conceptual grasp

Focuses on the degree of understanding and ability to conceptualize.

FAQs

How is "representational capacity" used in cognitive science?

In cognitive science, "representational capacity" refers to the amount and complexity of information that a cognitive system, such as the human brain or an artificial neural network, can effectively encode, store, and process.

What factors influence the "representational capacity" of a neural network?

The "representational capacity" of a neural network is influenced by factors such as the number of neurons, the network architecture, the types of activation functions used, and the training data it receives.

How does "representational capacity" relate to machine learning performance?

"Representational capacity" is a key factor in machine learning performance. A model with insufficient "representational capacity" may underfit the data, while a model with excessive "representational capacity" may overfit the data.

What are some alternatives to "representational capacity"?

You can use alternatives like "depictive capability", "expressive potential", or "cognitive bandwidth" depending on the specific context.