Task 4 Flashcards
(41 cards)
What is connectionist research?
A field that models how neural networks contribute to thought, emphasizing connections among simple neuron-like structures.
What are two alternative names for connectionist research?
Neural networks and Parallel Distributed Processing (PDP).
What are the two main classes of connectionist models?
Local representations – Each neuron-like unit represents a specific concept or proposition.
Distributed representations – Concepts are distributed across multiple neuron-like units.
How do connectionist networks perform parallel constraint satisfaction?
By adjusting activation levels across many units simultaneously to find a stable, consistent state.
What are the basic components of a connectionist network?
Units – Neuron-like components that activate based on input.
Links – Connections between units, which can be excitatory (positive) or inhibitory (negative).
What is the difference between one-way and symmetric links?
One-way links – Activation flows in a single direction.
Symmetric links – Activation flows back and forth between two units.
How are concepts represented in local vs. distributed networks?
Local networks – Individual units correspond to specific concepts (e.g., “computer geek”).
Distributed networks – Concepts are spread across multiple units, making the network more flexible and robust.
What is a recurrent network?
A network where activation from output units feeds back into input units, creating cyclical processing.
How do neural networks solve problems?
Through spreading activation – units pass signals to connected units until the network settles into a stable state.
What is the concept of “relaxation” in neural networks?
The process of adjusting activation across all units until they reach a stable, consistent state.
How do connectionist models handle decision-making?
They balance positive constraints (actions/goals that support each other) and negative constraints (conflicting actions/goals).
What is an example of a real-world constraint satisfaction problem?
Scheduling university classes while avoiding conflicts with rooms, professors, and student availability.
What are the two main ways learning occurs in neural networks?
Adding new units to the network.
Changing the weights on links between units.
What is Hebbian learning?
A rule stating that “neurons that fire together, wire together”, meaning that connections between co-activated neurons strengthen over time.
Why is Hebbian learning considered unsupervised?
The network learns without a teacher, simply by reinforcing frequent co-activations.
What is backpropagation, and how does it work?
A supervised learning algorithm where errors are propagated backward through the network to adjust connection weights.
What are some limitations of backpropagation as a model of human learning?
It requires a supervisor (feedback on right/wrong answers).
It is slow, needing hundreds or thousands of examples to learn patterns.
What is pattern association in neural networks?
The process of learning to associate one input pattern with a specific output pattern (e.g., associating a word with its meaning).
What is autoassociation?
A type of pattern association where the input and output patterns are identical, helping with memory recall.
How does Hebbian learning apply to pattern association?
Connections between simultaneously active input and output units are strengthened, making recall more efficient.
What is competitive learning?
An unsupervised learning process where output units compete, and only the most active unit strengthens its connections.
What are the three phases of competitive learning?
Excitation – Input activates multiple output units.
Competition – Output units inhibit each other, with the strongest unit winning.
Weight adjustment – The winner strengthens its connection to the input, improving future recognition.
What happens if a competitive network lacks proper weight control?
One unit may become dominant, preventing other units from learning new patterns (a “winner-takes-all” effect).
What is a Brain-Computer Interface (BCI)?
A system that translates brain activity into computer commands, allowing direct brain control of external devices.
