In PyBrain, a neural network is represented as a hierarchical structure composed of layers and connections between neurons. The architecture of a neural network in PyBrain typically consists of the following components:

Layers : A neural network in PyBrain is organized into layers, with each layer containing a group of neurons. There are different types of layers, including input layers, hidden layers, and output layers. Input layers receive the input data, hidden layers process the data through nonlinear transformations, and output layers produce the final output of the network.

Neurons : Neurons are the basic processing units in a neural network. Each neuron receives input signals, computes a weighted sum of these inputs, applies an activation function to the sum, and produces an output signal. PyBrain supports various types of neurons with different activation functions, including linear, sigmoid, tanh, and softmax neurons.
Connections : Connections represent the flow of information between neurons in different layers. In PyBrain, connections can be either feedforward or recurrent. Feedforward connections propagate signals from neurons in one layer to neurons in the next layer, while recurrent connections allow signals to loop back to neurons in the same layer or previous layers. Connections are typically represented by weight matrices, where each element corresponds to the strength of the connection between two neurons.

Network Structure : The structure of a neural network in PyBrain is defined by specifying the number of neurons in each layer and the type of connections between layers. PyBrain provides a flexible interface for constructing custom network architectures, allowing users to create networks with arbitrary topologies and configurations.

Learning Algorithms : PyBrain includes various learning algorithms for training neural networks, such as backpropagation, gradient descent, and reinforcement learning algorithms like Q-learning and policy gradients. These algorithms adjust the weights of connections between neurons based on the difference between the network's output and the desired output, with the goal of minimizing a specified loss function.