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Questions and Answers

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?

    <p>In layers akin to human neural networks</p> Signup and view all the answers

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

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

    How do computational neuroscientists approach modeling biological neurons?

    <p>By abstracting some biological details while preserving others</p> Signup and view all the answers

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

    <p>To help the neurons recognize patterns in the input data</p> Signup and view all the answers

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

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

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

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

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

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

    How does backpropagation contribute to learning in a neural network?

    <p>By iteratively updating weights and biases based on prediction errors</p> Signup and view all the answers

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

    <p>Capture subcomponents like loops or lines from lower layers</p> Signup and view all the answers

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

    <p>-1 to +1</p> Signup and view all the answers

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

    <p>Inhibitory</p> Signup and view all the answers

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

    <p>Activation level function</p> Signup and view all the answers

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

    <p>Changing the weights of connections between neurons</p> Signup and view all the answers

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

    <p>By modifying the strengths of connections between neurons</p> Signup and view all the answers

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

    <p>Perceptual problems involving pattern recognition</p> Signup and view all the answers

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

    <p>Structured and sharply defined nature</p> 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?

    <p>3%</p> Signup and view all the answers

    How many neurons can a single cortical column contain?

    <p>200,000</p> Signup and view all the answers

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

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

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

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

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

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

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

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

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

    <p>The number of layers in the network</p> Signup and view all the answers

    What is Hebbian learning primarily based on?

    <p>Local interactions between neurons</p> Signup and view all the answers

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

    <p>Both involve changing weights based on local interactions</p> Signup and view all the answers

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

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

    Why did Frank Rosenblatt introduce the Perceptron Convergence Rule?

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

    What is a key characteristic of linearly separable functions?

    <p>They do not necessitate any hidden layers</p> Signup and view all the answers

    What is the role of hidden layers in neural networks?

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

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

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

    How does fuzzy logic differ from traditional logic?

    <p>Fuzzy logic allows for values between 0 and 1 for representation</p> Signup and view all the answers

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

    <p>Selecting the lowest rating among all variables</p> Signup and view all the answers

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

    <p>By taking the minimum value</p> Signup and view all the answers

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

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

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

    <p>Overestimating the reliability of the test results</p> 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?

    <p>Causes the network to overfit the training set</p> Signup and view all the answers

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

    <p>Autoencoder</p> Signup and view all the answers

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

    <p>Facial recognition software in cellphones</p> Signup and view all the answers

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

    <p>Localized feature detectors</p> 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?

    <p>Small-world networks</p> Signup and view all the answers

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

    <p>Neuroplasticity</p> Signup and view all the answers

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

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

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

    <p>&quot;It tends to be less of a small-world network&quot;</p> Signup and view all the answers

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