Cognitive Psychology: Neural Network Approach

Artificial Neural Network (ANN)

Computational model inspired by biological neural networks.

Node

Basic unit in an artificial neural network.

Layer

Group of nodes; includes input, output, hidden.

Weight

Value that adjusts the strength of connections.

Activation function

Determines output based on input sum.

Bias

Value added to activation function to adjust output.

Training

Process of adjusting weights using data.

Testing/validation

Evaluating model performance on unseen data.

Perceptron

Simple model of a single neuron.

Axon terminal

End point of a neuron where signals transmit.

Synapse

Connection point between two neurons.

Multi-layer perceptron

ANN with multiple layers for complex tasks.

Deep neural network

ANN with many layers for advanced learning.

Supervised learning

Learning from labeled training data.

Unsupervised learning

Learning from unlabeled data to find patterns.

Reinforcement learning

Learning through rewards and penalties.

Input database

Data set used for training the ANN.

Training and testing split

Dividing data for training and validation purposes.

Network architecture

Structure defining layers and connections in ANN.

Electrical brain stimulation

Modifies bias to influence neuron activity.

Atari Breakout

Example of unsupervised learning application.