Feedforward neural network¶
An artificial neural network whose within-evaluation signals move from input to output through an acyclic directed graph.
Core Idea¶
A feedforward neural network is a parameterized artificial neural network whose signal graph is directed and acyclic for one evaluation. An input representation passes through weighted computational units and optional nonlinear transformations to an output; connections may be layered, convolutional, or skip paths as long as none sends a later signal back into an earlier state. The defining contrast is a recurrent network, whose reusable state carries feedback or history into subsequent computation.
Feedforward does not name one training algorithm. Backpropagation can calculate gradients through an acyclic graph, but changing weights across training steps is not an inference-time feedback edge; recurrent models can also be trained through unrolled computations. A simple linear single-layer network may satisfy the structural test, while deeper nonlinear networks have different expressive power. Recurrence changes the reusable signal architecture, not the mathematical possibility of differentiating it. LeNet-5 illustrates an attested convolutional feedforward application to document recognition.
Scope of Application¶
Feedforward classification concerns the within-evaluation signal graph, not the training algorithm.
- Function approximation. Represent static mappings from typed inputs to outputs.
- Image and document recognition. Use acyclic learned feature stages for visual classification.
- Architecture comparison. Distinguish feedforward, convolutional, skip, and recurrent signal paths.
- Training analysis. Separate backward derivative computation from the forward inference graph.
Clarity¶
For one evaluation, draw the input-to-output signal graph and test for a directed cycle. A layered or convolutional network can qualify, as can an acyclic skip path. A recurrent state dependency is the nearest exclusion. Backpropagation sends derivatives backward during training but does not alter the forward inference graph; conversely recurrence can be differentiated through unrolled time. The label alone supplies no accuracy or generalization claim.
Manages Complexity¶
The acyclic graph organizes many parameterized unit operations into a composed map that can be evaluated in dependency order. This makes forward inference and gradient accounting tractable, but hides data, optimization, and validation assumptions under an architecture label. Calling a network feedforward says how signals are wired, not that it learned a good function.
Abstract Reasoning¶
- Name the input, output, computational units, and weighted connections.
- Draw the within-evaluation direction of every signal dependency.
- Check that a topological ordering exists and no later state feeds an earlier one.
- Classify convolutions or skip paths by cycle structure rather than visual layout.
- Assess training and task performance separately from the structural classification.
Knowledge Transfer¶
The acyclic input-to-output criterion transfers across tabular MLPs, convolutional models, and other finite directed neural graphs. LeNet-5's image preprocessing, learned parameters, and recognition results do not transfer to an arbitrary feedforward net. A recurrent network unrolled for a finite time produces an acyclic computation graph for that particular unrolling, but the reusable architecture retains state feedback and is not thereby reclassified.
Relationships to Other Abstractions¶
Current abstraction Feedforward neural network Domain-specific
Parents (1) — more general patterns this builds on
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Feedforward neural network is a kind of, conditional Machine-Learning Model Domain-specific
Supports fitted feedforward network models, not the bare architecture alone.
Condition / exception Supports fitted feedforward network models, not the bare architecture alone.
Children (1) — more specific cases that build on this
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Convolutional neural network Domain-specific is a kind of Feedforward neural network
A convolutional neural network is a feedforward neural network specialized by learned convolutional filters.
Hierarchy path (1) — routes to 1 parentless root
- Feedforward neural network → Machine-Learning Model
Neighborhood in Abstraction Space¶
Feedforward neural network sits in a crowded region of the domain-specific corpus (33rd percentile for distinctiveness): several abstractions share nearly its structure, so a description that fits it tends to fit its neighbors too.
Family — Operators, Functions & Data Abstractions (11 abstractions)
Nearest neighbors
- Artificial Neural Network — 0.92
- Image-Processing Method — 0.90
- Network mapping — 0.88
- Self-supervised learning — 0.88
- Integral Transform — 0.88
Computed from structural-signature embeddings · 2026-10-08