Neural Connections and Brain Function Quiz
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Questions and Answers

What is a limitation of a perceptron model?

  • It requires extensive computational power
  • It is not suitable for classification tasks
  • It cannot learn complex patterns
  • It can only solve linearly separable problems (correct)

    • In the context of the text, who is described as an expert in Financial Markets and Algorithmic Trading Analytics?

  • Watanabe
  • Dr. Anand Jayaraman (correct)
  • Marc Chagall
  • Vincent van Gogh

    • What does the text suggest is the best learning system known to us?

  • Logistic regression model
  • Linear regression model
  • Simple perceptron model
  • Biological neural system (correct)

    • What animal was able to discriminate between paintings by Van Gogh and Chagall with 95% accuracy?

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

    What do mice in the text have the ability to memorize?

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

    Which unit is considered fundamental in biological neural networks?

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

    What is used to overcome the limitations of a perceptron model?

    <p>Multi-layer perceptron</p> Signup and view all the answers

    What is the key idea behind Hebbian Learning?

    <p>Strengthening synapses that are used more</p> Signup and view all the answers

    In the context of machine learning, what is the primary focus in determining synaptic strength?

    <p>Finding the optimal weights consistent with the given data</p> Signup and view all the answers

    What characteristics does a Perceptron model capture?

    <p>Input, Weights, Bias, Dot Product</p> Signup and view all the answers

    Why are Perceptrons considered brittle?

    <p>Their sensitivity to small changes in input data</p> Signup and view all the answers

    How does a Perceptron make decisions?

    <p>By weighing up evidence from inputs</p> Signup and view all the answers

    What method is used to train a Perceptron effectively?

    <p>Changing weights to minimize error</p> Signup and view all the answers

    What is the relationship between a neuron's input and its weights in the Perceptron model?

    <p><em>z = w ∙ x</em> models the weighted sum of inputs</p> Signup and view all the answers

    What triggers the release of neurotransmitter substances at the synapse?

    <p>Spikes travelling along the axon of the pre-synaptic neuron</p> Signup and view all the answers

    According to Donald Hebb, how does a synapse get strengthened?

    <p>By releasing more neurotransmitter</p> Signup and view all the answers

    In computational neurons, what determines the input to the next neuron based on synaptic strength?

    <p>Strength of the synaptic connections</p> Signup and view all the answers

    What happens when the strength of a synaptic connection is weak?

    <p>Results in weaker paths through the network</p> Signup and view all the answers

    Which component influences one neuron on another and is learnt by feedback or observation?

    <p>Synaptic strengths</p> Signup and view all the answers

    How does Donald Hebb describe the learning process in neural networks?

    <p>By applying larger weights to connections that are used more</p> Signup and view all the answers

    What determines whether a synapse gets stronger or weaker according to Hebbian learning?

    <p>The frequency of use of the synapse</p> Signup and view all the answers

    What triggers the action potential in post-synaptic neurons?

    <p>Integration of excitatory and inhibitory signals</p> Signup and view all the answers

    What is the purpose of using a different activation function in a Sigmoid Neuron?

    <p>To ensure a smooth output transition from 0 to 1.</p> Signup and view all the answers

    In logistic regression, what do weight coefficients (W) represent?

    <p>Features or attributes of the data.</p> Signup and view all the answers

    How is the probability of a vehicle being fitted with a manual transmission estimated in logistic regression using the MTcars dataset?

    <p>By fitting a single logistic regression unit on the dataset.</p> Signup and view all the answers

    What is the purpose of using the sigmoid function in artificial neural models like the Perceptron?

    <p>To introduce non-linearity in the model.</p> Signup and view all the answers

    Which component of an artificial neural model estimates the probability of a binary outcome in logistic regression?

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

    What does a small change in weight result in for a Sigmoid Neuron's output?

    <p>A smooth change in output.</p> Signup and view all the answers

    How are weight coefficients determined in logistic regression for a classification task?

    <p>Learned from the dataset during training.</p> Signup and view all the answers

    In an artificial neural model, what does the sigmoid function output when z is large and positive?

    <p>~ 1</p> Signup and view all the answers

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