Neural Connections and Brain Functioning

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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?

Multi-layer perceptron Signup and view all the answers

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

Strengthening synapses based on correlated activity Signup and view all the answers

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

Hebbian learning Signup and view all the answers

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

Strengthened synapses lead to stronger paths through the network. Signup and view all the answers

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

Synaptic strength Signup and view all the answers

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

By the amount of neurotransmitter released Signup and view all the answers

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

It gets strengthened Signup and view all the answers

How are synaptic strengths in computational neurons influenced?

By feedback, experience, or observation Signup and view all the answers

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

May produce spikes in the post-synaptic neuron Signup and view all the answers

What is the main idea behind Hebbian Learning?

Unused synapses get weaker while strengthened synapses become stronger Signup and view all the answers

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

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

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

Weighted sum of inputs exceeding a certain threshold Signup and view all the answers

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

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

Why are Perceptrons considered brittle in computational models?

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

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?

Which concept is involved in adjusting the synaptic weights based on correlated activity of pre- and post-synaptic neurons?

In machine learning, what term is used to describe a type of artificial neuron that can learn and make decisions?

Which methodology aims to overcome the limitations of a single-layer perceptron by introducing multiple layers for complex decision-making?

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

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

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

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

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

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

How are synaptic strengths in computational neurons influenced?

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

What is the main idea behind Hebbian Learning?

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

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

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

Why are Perceptrons considered brittle in computational models?

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