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Artificial neurons compute outputs from weighted inputs using nonlinear activation functions.
Neural networks are built from interconnected artificial neurons organized into layers. Each neuron receives real-valued signals from neurons in the previous layer through weighted connections, sums (or otherwise aggregates) those inputs, and applies a nonlinear activation function to produce its output. Signals propagate from an input layer through one or more hidden layers to an output layer, and a network is considered “deep” when it has at least two hidden layers, enabling it to learn hierarchical representations. The signal flow is determined by the network’s architecture. In feedforward networks (e.g., multilayer perceptrons), information moves in one direction from inputs to outputs. Convolutional neural networks (CNNs) use convolutional layers and downsampling to detect local patterns while reusing weights across spatial locations. Recurrent neural networks (RNNs) introduce looped connections so a hidden state can carry information across time steps, making them suitable for sequential data. Transformers use attention mechanisms to relate every token to every other token, supporting modeling of long-range dependencies. During training, the weights are adjusted to reduce prediction error. A common approach is backpropagation, which efficiently computes how changes in weights affect the loss by propagating error gradients backward through the layers. This component-wise signal flow—forward computation of activations and backward computation of gradients—enables optimization of the network parameters using data and a chosen loss function.
Artificial neurons compute outputs from weighted inputs using nonlinear activation functions.
Signal flow typically goes from input layer through hidden layers to output layer; “deep” networks have multiple hidden layers.
Architectures change signal flow: feedforward (one-way), CNNs (convolution/downsampling), RNNs (loops and hidden state), and Transformers (attention across tokens).
Training adjusts connection weights using loss-driven learning, commonly via backpropagation to propagate gradients backward through the network.
A computational unit that aggregates incoming signals using weights and applies an activation function to produce an output.
A learnable parameter on a connection that scales the strength of a signal passed between neurons.
A nonlinear function applied to a neuron’s aggregated inputs to produce its output.
A group of neurons that collectively transform signals as they pass through the network.
The direction and pattern by which information moves through a neural network from inputs to outputs (and, during training, gradients back through layers).
A neural network with unidirectional information flow from inputs to outputs, typically without internal memory loops.
A network architecture that uses convolutional filters and downsampling to detect local features and reuse weights across space.
A network with looped connections that maintain a hidden state to model sequential or time-dependent data.
An architecture that uses attention to compute relationships between all tokens in the input, enabling long-range dependency modeling.
An algorithm that computes gradients of the loss with respect to weights by propagating error information backward through differentiable layers.
A measure of prediction error that training seeks to minimize by updating network weights.
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