Which of the following describes the concept of 'many to one' relation in the context of circuits and functions?

a$.$ Only one circuit can produce a particular desired input$-$output

function

b$.$ Many circuits can be designed to perform the same input$-$output

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function

c$.$ A single algorithm can produce many different input$-$output

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relations

d$.$ Different circuits and algorithms result in different outputs for the

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same inputs

e$.$ Algorithms are unique and no two algorithmic processes can produce

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the sameWhich of the following statements best describes the fundamental principle behind how Generative Adversarial Networks $($GANs$)$ work?In what ways is a human brain different from a Neural Network?

Select all that apply:

Human brains have many different types of neurons, while most

artificial neural network architectures combine only one or two types of

neuron.

Neural networks typically start from a "blank slate", while the human

brain leverages many generations of evolutionary fine$-$tuning.

At a coarse level, the neurons in the brain use signals from other

neurons, but artificial Neural Nets don't.

At a fine level, brains have a spiking mechanism but Neural Nets don't.In what ways is a human brain different from a Neural Network?

Select all that apply:

Human brains have many different types of neurons, while most

artificial neural network architectures combine only one or two types of

neuron.

Neural networks typically start from a "blank slate", while the human

brain leverages many generations of evolutionary fine$-$tuning.

At a coarse level, the neurons in the brain use signals from other

neurons, but artificial Neural Nets don't.

At a fine level, brains have a spiking mechanism but Neural Nets don't.

a$.$ GANs consist of a single neural network that learns to generate new

data that mimics a training dataset.

b$.$ GANs consist of two networks, a generator and a learner, where the

generator creates new training data for the learner.

c$.$ GANs consist of a single neural network that predicts new data by

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learning from a labeled dataset

d$.$ GANs consist of two neural networks, a generator and a

discriminator, which compete against each other during training.