Nh_7

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What is the aim of connectionist modelers in computational neuroscience?

To focus on replicating how the brain acquires language and develops ideas

To simulate and reproduce psychological phenomena observed in brain-damaged patients

To pay less attention to biological constraints and focus on how the brain works

To model biological neurons while abstracting some details and preserving others (correct)

In human neurons, what happens if the activation of a neuron reaches a certain minimum threshold?

The neuron will receive inhibitory inputs

The neuron will fire (correct)

The neuron will fail to fire

The neuron will undergo excitatory effect

How are weights in artificial neural networks (ANN) similar to human neural networks?

Weights determine the node's bias term in ANN

Weights correspond to the threshold necessary for activation in human neural networks

Weights indicate the connection strength between neurons in human neural networks

Weights determine the overall behavior of the network like excitatory and inhibitory effects in human neural networks (correct)

How do artificial neural networks (ANN) organize nodes or neurons?

In layers akin to human neural networks Signup and view all the answers

What is the role of the bias term in artificial neural networks (ANN)?

It specifies the minimum threshold for node/neuron activation Signup and view all the answers

How do computational neuroscientists approach modeling biological neurons?

By abstracting some biological details while preserving others Signup and view all the answers

What is the purpose of assigning weights to connections between neurons in different layers of a neural network?

To help the neurons recognize patterns in the input data Signup and view all the answers

How does adding a bias to the weighted sum affect the activation of a neuron?

It sets a threshold for when the neuron should become active Signup and view all the answers

What is the role of the second hidden layer in a neural network?

Recognizing specific subcomponents like loops or lines in an image Signup and view all the answers

In a neural network, what does the activation level of a neuron in the output layer represent?

How much the system believes an image corresponds with a digit Signup and view all the answers

How does backpropagation contribute to learning in a neural network?

By iteratively updating weights and biases based on prediction errors Signup and view all the answers

What is the main function of neurons in the third hidden layer of a neural network?

Capture subcomponents like loops or lines from lower layers Signup and view all the answers

What range of values typically represents the level of activation for each unit in an artificial neural network?

-1 to +1 Signup and view all the answers

Which type of synapse corresponds to a negative weight in an artificial neural network?

Inhibitory Signup and view all the answers

What function yields no output signal in an artificial neural network until the total input reaches the threshold?

Activation level function Signup and view all the answers

What is used in backpropagation learning to reduce the 'mistakes' made by a neural network?

Changing the weights of connections between neurons Signup and view all the answers

How does an artificial neural network learn in terms of modifying connections between neurons?

By modifying the strengths of connections between neurons Signup and view all the answers

What type of problems are neural networks particularly well-suited for?

Perceptual problems involving pattern recognition Signup and view all the answers

What distinguishes problems tackled by good old-fashioned AI (GOFAI) from those tackled by neural networks?

Structured and sharply defined nature Signup and view all the answers

What is the connection ratio of each cortical neuron to the neurons in the surrounding square millimeter of cortex?

3% Signup and view all the answers

How many neurons can a single cortical column contain?

200,000 Signup and view all the answers

Which statement is true about artificial neural networks and biological feedback?

Biological learning does not involve detailed feedback similar to supervised networks. Signup and view all the answers

In terms of neuron unit count, how do typical artificial neural networks compare to cortical columns?

Cortical columns have more units than artificial neural networks. Signup and view all the answers

What is a significant difference between the structure of the neocortex and a typical neural network?

The neocortex lacks organization in cortical columns present in typical neural networks. Signup and view all the answers

Which learning profile would be expected in a good model simulating the learning of linguistic rules?

A slow, gradual learning profile similar to actual language learners. Signup and view all the answers

What is the main difference between linearly separable functions and functions that are not linearly separable?

The number of layers in the network Signup and view all the answers

What is Hebbian learning primarily based on?

Local interactions between neurons Signup and view all the answers

In the context of neural networks, what does the Perceptron Convergence Rule share with Hebbian learning?

Both involve changing weights based on local interactions Signup and view all the answers

How is supervised learning different from unsupervised learning in the context of neural networks?

Supervised learning requires feedback on correctness, while unsupervised learning does not Signup and view all the answers

Why did Frank Rosenblatt introduce the Perceptron Convergence Rule?

To allow single-layer networks to adjust weights and thresholds based on errors Signup and view all the answers

What is a key characteristic of linearly separable functions?

They do not necessitate any hidden layers Signup and view all the answers

What is the role of hidden layers in neural networks?

To enable the network to learn non-linearly separable functions Signup and view all the answers

What is the correct application of Bayes’ Theorem in the context provided?

Determining the probability of having the disease given a positive test result Signup and view all the answers

How does fuzzy logic differ from traditional logic?

Fuzzy logic allows for values between 0 and 1 for representation Signup and view all the answers

In the context of fuzzy logic, what does the 'fuzzy AND operation' involve?

Selecting the lowest rating among all variables Signup and view all the answers

How are fuzzy sets typically combined to make decisions in fuzzy logic?

By taking the minimum value Signup and view all the answers

Which statement accurately describes the application of fuzzy logic in practical scenarios?

Utilized in control systems, image processing, and decision-making Signup and view all the answers

What misconception do people often have when interpreting medical diagnosis probabilities shown in the provided text?

Overestimating the reliability of the test results Signup and view all the answers

What is the main consequence of increasing the number of hidden layers much more than what is sufficient in a neural network?

Causes the network to overfit the training set Signup and view all the answers

Which neural network architecture learns to produce a simplified representation of the input?

Autoencoder Signup and view all the answers

What technological applications have been made possible by Convolutional Neural Networks (CNNs)?

Facial recognition software in cellphones Signup and view all the answers

What is the key feature of ConvNets that allows them to filter out specific features from an image?

Localized feature detectors Signup and view all the answers

What concept is associated with networks where one can navigate from any point to any other point in only a small number of steps?

Small-world networks Signup and view all the answers

Which term describes the brain's ability to rewire and reroute after damage, forming new pathways?

Neuroplasticity Signup and view all the answers

What did Latora and Marchiori discover regarding the separation of neurons in mammalian brains?

The average separation between neurons is around two to three steps Signup and view all the answers

How did researchers find the brain of an individual with schizophrenia differs in terms of network organization?

"It tends to be less of a small-world network" Signup and view all the answers