Neural Connections and Brain Functioning
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Questions and Answers

What is the term used to describe the fundamental units in biological neural networks?

  • Hebbian learning
  • Computational neuron (correct)
  • Perceptron
  • Synaptic strengths
  • Which concept is involved in adjusting the synaptic weights based on correlated activity of pre- and post-synaptic neurons?

  • Hebbian learning (correct)
  • Synaptic strengths
  • Perceptron
  • Machine learning
  • In machine learning, what term is used to describe a type of artificial neuron that can learn and make decisions?

  • Computational neuron
  • Synaptic strengths
  • Perceptron (correct)
  • Hebbian learning
  • Which methodology aims to overcome the limitations of a single-layer perceptron by introducing multiple layers for complex decision-making?

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

    What is the primary focus of Hebbian learning in neural networks?

    <p>Strengthening synapses based on correlated activity</p> Signup and view all the answers

    Which process involves enhancing the synaptic connection between neurons when they are activated simultaneously?

    <p>Hebbian learning</p> Signup and view all the answers

    What did Donald Hebb postulate in 1949 regarding neural networks learning?

    <p>Strengthened synapses lead to stronger paths through the network.</p> Signup and view all the answers

    In computational neurons, what influences the input to the next neuron?

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

    What is one way synaptic strength in a biological neural network can be learned?

    <p>By the amount of neurotransmitter released</p> Signup and view all the answers

    What happens if a synapse is used more according to Hebbian learning theory?

    <p>It gets strengthened</p> Signup and view all the answers

    How are synaptic strengths in computational neurons influenced?

    <p>By feedback, experience, or observation</p> Signup and view all the answers

    What happens to the integration of excitatory and inhibitory signals in post-synaptic neurons?

    <p>May produce spikes in the post-synaptic neuron</p> Signup and view all the answers

    What is the main idea behind Hebbian Learning?

    <p>Unused synapses get weaker while strengthened synapses become stronger</p> Signup and view all the answers

    In the context of Machine Learning, how are synaptic strengths (weights) determined?

    <p>By adjusting weights in a way that minimizes errors in the output</p> Signup and view all the answers

    What key characteristic does a Perceptron model capture in decision-making?

    <p>Weighted sum of inputs exceeding a certain threshold</p> Signup and view all the answers

    How does a Perceptron differ from Hebbian Learning in terms of training?

    <p>Perceptron adjusts weights based on minimizing errors, while Hebbian Learning strengthens used synapses</p> Signup and view all the answers

    Why are Perceptrons considered brittle in computational models?

    <p>As they are sensitive to small changes in input leading to drastic changes in output</p> Signup and view all the answers

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