29 Questions
What is the role of Dr. Anand Jayaraman according to the text?
Chief Scientist, Soothsayer Analytics
What academic background does Dr. Anand Jayaraman have as per the text?
Ph.D in Physics, Univ.of Pittsburgh, USA
What did pigeons achieve according to the text?
Discrimination between Van Gogh and Chagall paintings with high accuracy
What can mice do according to the text?
Memorize mazes and detect drugs/explosives
What is a fundamental unit in biological neural networks according to the text?
Neurons
What concept is discussed as the best learning system known to us in the text?
Biological neural system
Which model is mentioned in the text to overcome the limitations of a perceptron?
'Multi-layer perceptron'
'Automation is future' implies what according to the text?
Automation will dominate future technologies
What is the main idea behind using a different activation function in a Sigmoid Neuron?
To make the output switch smoothly instead of abruptly.
What does it mean when z is large and positive in a Sigmoid Neuron?
The output is close to 1.
In logistic regression, what are the independent variables used for classification tasks?
x
What happens if a synapse is used more in Hebbian Learning?
It gets strengthened
What does the weight coefficient 'w2' represent in logistic regression for classification?
Credit rating of the individual
Which characteristic is not captured by the Perceptron model of decision making?
Output
How can logistic regression models be utilized in predicting vehicle transmission types?
By estimating probabilities based on engine power and weight.
What is used to represent the weighted sum of inputs in a neuron?
Multiplication
What is the significance of the '18.8663 – 8.08035 wt + 0.0363 hp' equation in logistic regression examples?
It estimates the probability of a manual transmission based on engine power and weight.
Why are Perceptrons considered brittle?
Due to their inability to learn complex patterns
What role do weight coefficients play in logistic regression models?
They represent numerical values assigned to different features for prediction.
How is synaptic strength determined in Machine Learning?
By finding the optimal weights consistent with the given data
How does a Perceptron differ from logistic regression units in classification tasks?
Perceptrons utilize different activation functions compared to logistic regression units.
What is the purpose of changing weights in training a perceptron?
To minimize the error
What do Sigmoid Neurons offer compared to Perceptrons?
Better handling of complex patterns
What triggers the release of neurotransmitter substances at the synapse?
Spikes travelling along the axon of the pre-synaptic neuron
How does Donald Hebb propose a network learns?
By strengthening synapses that are used more
Where does the integration of excitatory and inhibitory signals take place?
Dendrite
What determines the contribution of signals in a post-synaptic neuron?
Strength of the synaptic connection
What influences synaptic strengths according to Computational Neuron Artificial Learning?
Feedback, experience, observation
What is associated with larger weights in neural connections?
More stimulation
Study Notes
Biological Neural Networks
- A biological neural network consists of 100 billion neurons and 1000 trillion synaptic connections.
- The fundamental units of a biological neural network are neurons, connected by synapses.
- The direction of signal transmission in a biological neural network is along the axon from the nucleus to the synapse.
Biological Inspiration
- The spikes traveling along the axon of the pre-synaptic neuron trigger the release of neurotransmitter substances at the synapse.
- The neurotransmitters cause excitation or inhibition in the dendrite of the post-synaptic neuron.
- The integration of the excitatory and inhibitory signals may produce spikes in the post-synaptic neuron.
- The contribution of the signals depends on the strength of the synaptic connection.
Learning in Biological Neural Networks
- In 1949, Donald Hebb postulated that if a synapse is used more, it gets strengthened, releasing more neurotransmitters.
- This causes the particular path through the network to get stronger, while others, not used, get weaker.
- Each connection has a weight associated with it, which influences the strength of the signal transmission.
Computational Neuron
- An artificial neuron has an input, weights, sum, and threshold.
- The synaptic strengths influence the input to the next neuron, based on the strength of the synaptic connection.
- Dendrites carry signals to the cell body, where they are summed, and if the sum exceeds the threshold, the neuron fires.
Hebbian Learning
- Hebbian learning states that if a synapse is used more, it gets strengthened, causing the particular path through the network to get stronger.
- Machine learning determines the synaptic strength (weights) by finding the optimal weights consistent with the given data.
Perceptron
- The perceptron is the first artificial neuron, and its structure is a model of decision making.
- It makes decisions by weighing up evidence and captures key characteristics: input, weights, sum, and threshold.
- The notation is a dot product of vectors of inputs and weights.
- Perceptrons are brittle, and changing weights can cause a spiky output, rather than a smooth change.
Sigmoid Neurons
- Sigmoid neurons use a different activation function, which produces a smooth output between 0 and 1.
- The output of a sigmoid neuron changes smoothly from 0 to 1 as the input changes.
Classification with Logistic Regression
- Logistic regression is a model that uses a classification task to estimate the probability of a binary outcome.
- The weight coefficients are learned from the data.
- The output of a logistic regression model is a probability between 0 and 1.
- The MT cars dataset is an example of using logistic regression to estimate the probability of a vehicle being fitted with a manual transmission.
Test your knowledge on concepts related to Hebbian Learning, Machine Learning, and the Perceptron model. Explore how synaptic strength impacts network pathways and how optimal weights are determined in machine learning.
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