Answer Posted / sun
The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
1. Biological neural networks are made up of real biological neurons that are connected or functionally related in a nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
2. Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets.
This article focuses on the relationship between the two concepts; for detailed coverage of the two different concepts refer to the separate articles: biological neural network and artificial neural network
| Is This Answer Correct ? | 1 Yes | 0 No |
Post New Answer View All Answers
Describe the structure of artificial neural networks?
What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
What are neural networks and how do they relate to ai?
What are the population, sample, training set, design set, validation set, and test set?
What is backprop?
What are combination, activation, error, and objective functions?
How artificial neural networks can be applied in future?
What are conjugate gradients, levenberg-marquardt, etc.?
Who is concerned with nns?
How many kinds of nns exist?
How does an LSTM network work?
Which is the similar operation performed by the drop-out in neural network?
Explain neural networks?
How to avoid overflow in the logistic function?
List some commercial practical applications of artificial neural networks?
