Types of Neural Networks: 19 Architectures for 2026

A neural network is a computing architecture consisting of nodes organized in layers to detect patterns in data. There are at least 19 different types being used, and each type is designed to handle a unique type of data input – an image, a sequence, a graph, or unlabeled data. This guide classifies all 19 types in six categories, compares them in one table, and links each type to its best-suited input data.

What Is a Neural Network? 

In a neural network, there are three layers in which the data is processed, and they include the input layer, one or more hidden layers, and finally the output layer. The connections between the nodes have weights assigned to them. These nodes use activation functions on their inputs before passing any values further. Backpropagation helps reduce errors by comparing the predicted output with the actual output. 

The Neural Network Family Tree

There are nineteen architectures that fit into six broad categories according to the data type being processed and the learning model used.

• Feedforward architectures – work on static inputs and have no memory capacity: Feedforward Neural Networks, Multilayer Perceptrons
• Sequence architectures – work on ordered data types: Recurrent Neural Networks, LSTM, GRU
• Spatial/pattern architectures – work on grid-based data and part-whole relationships: CNN, Radial Basis Function Networks, Capsule Networks
• Generative architectures – generate new data types: GANs, Autoencoders, Diffusion Models
• Attention and graph architectures – work on long-range/relational data types: Transformers, Graph Neural Networks, MoE, State-Space Models
• Specifically unsupervised and specialized architectures: Self-Organizing Maps, Deep Belief Networks, Siamese Networks, Spiking Neural Networks

The Foundational Types

Feedforward Neural Networks (FNN)

Feedforward neural networks transfer information only in one direction, from the input layer to the output layer, without any loop or memory component in them. Information is transferred in the form of products of the weights and inputs, which are then sent to nodes through activation functions. The disadvantage of feedforward neural networks is that the number of inputs to be processed is constant

Multilayer Perceptrons (MLP)

It is an artificial neural network that has at least two hidden layers and uses backpropagation in training to minimize the error in output. It can recognize complex relationships due to its multi-layer architecture and is used in tasks like predicting customer churn and detecting fraud. Its drawback is similar to other feedforward neural networks in that they lack spatial and temporal dependencies.

Convolutional Neural Networks (CNN)

The CNN uses sliding filters referred to as kernels to recognize the patterns such as edges, textures, and shapes in the grid-like input data. The CNN comprises three types of layers which include convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully-connected layers for classification. The modern CNN was proposed by Yann LeCun with his invention of LeNet-5 in 1998. CNNs are used for the processing of visual data in applications such as diagnostic imaging and self-driving cars. 

Recurrent Neural Networks (RNN)

In a recurrent neural network, the output of each time-step becomes the input to the next step of the network and this way short-term memory is formed. The recurrence makes the RNN suitable for processing sequential data, such as speech, financial time-series and text data. However, one drawback of RNNs is that they suffer from vanishing gradients as in longer sequences the effect of information diminishes. It resulted in the creation of LSTM networks in 1997

Long Short-Term Memory Networks (LSTM)

LSTM stands for long short-term memory, which is an RNN type with three different gates, namely input gate, forget gate, and output gate. These gates decide what information is going to stay in the memory of the network in the long run. LSTMs were developed by Sepp Hochreiter and Jürgen Schmidhuber in 1997. The use of gates enables the LSTM to store its context for hundreds of time steps, which helps in the process of machine translation and voice assistant speech recognition.

Gated Recurrent Units (GRU)

The gated recurrent unit (GRU) is an improved version of an LSTM, which consists of two gates rather than three, making it simpler and requiring fewer parameters to be learned by the neural network. The GRU architecture was proposed in 2014 by Kyunghyun Cho et al. A GRU learns more quickly than an LSTM and delivers similar performance in certain applications.

Transformers

The transformer is capable of handling an entire sequence at once through self-attention, a method whereby the relevance of all tokens within the sequence is calculated to every other token within the same sequence. Self-attention was developed by Ashish Vaswani and his team at Google, and they describe their architecture in a paper published in 2017, “Attention Is All You Need.” Self-attention is the feature that enables transformers to undergo parallel processing while training and hence trains faster than RNNs when compared to the same scale. Transformers are still the prevailing architecture behind large language models in 2026, and self-attention is the core component of diffusion transformers used for image and video generation.

Generative Adversarial Networks (GAN)

The architecture of GAN consists of two models – one is the generator producing synthetic data and the other one is the discriminator determining whether the input data is synthetic or not. The term GAN was coined by Ian Goodfellow et al. in 2014. During the training process, the generator creates more and more realistic data; thus, they have practical uses like synthetic image generation and augmentation for other models’ training. It is rather difficult to train and is subject to the problem of mode collapse. 

Autoencoders

The autoencoder reduces the size of input data using the encoder part, followed by reconstructing the input data with the decoder part. The middle part is the reduced latent space which carries the most valuable information about the input data. The applications of the autoencoder include denoising images and detecting anomalies in manufacturing sensor data. Variational autoencoders were introduced in 2013 by Diederik Kingma and Max Welling.

Radial Basis Function Networks (RBFN)

An RBFN classifies the input depending on how far it is from a group of learned reference points, without resorting to sums of weights for all inputs. The architecture was formally introduced by David Broomhead and David Lowe in 1988. An RBFN learns more quickly than conventional feedforward networks, and performs well at function approximation and time series prediction, especially when applied to small data sets.

Self-Organizing Maps (SOM)

A self-organizing map is an unsupervised neural network that projects high-dimensional input vectors into a two-dimensional space where the relative distances between vectors are maintained. This algorithm was proposed by Teuvo Kohonen in 1982 and is also known as Kohonen Map due to this reason. The nodes compete for winning in SOM and the winning node changes its weights such that it can match the input vector more precisely. 

Deep Belief Networks (DBN)

Deep Belief Network (DBN) consists of many layers of Restricted Boltzmann Machine, whereby the outputs of the hidden layers of the previous layer act as the inputs to the hidden layer of the subsequent layer. DBN was introduced by Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh in 2006. It is due to the use of this method that some of the earliest deep networks having more than two or three layers of hidden units could be created.

Specialized and Niche Architectures

Siamese Neural Networks

The Siamese neural network processes two different inputs through two similar but identical sub-networks with shared weights and calculates the similarity of the output produced by the two. The concept was developed by Jane Bromley et al. in 1993 for use in signature verification. Siamese networks help in developing systems for facial recognition and image similarity search.

Capsule Networks (CapsNet)

In capsule nets, the neurons are organized into capsules, which encode the presence of a feature and its pose, for example, orientation and scale. The concept was proposed by Sara Sabour, Nicholas Frosst, and Geoffrey Hinton in 2017. Capsule nets retain the part-whole structure relationship which is lost in the pooling operation of CNNs and therefore outperform them in tasks involving orientation of objects.

Spiking Neural Networks (SNN)

The spiking neural network (SNN) communicates by using electrical spikes to convey information instead of continuously valued signals. According to Wolfgang Maass, SNN belongs to a different class of models introduced in 1997. The SNN has reduced energy requirements as compared to traditional models in neuromorphic systems, making it suitable for low-power and edge devices. 

The 2026 Frontier: Architectures Built for Today’s Workloads

Graph Neural Networks (GNN)

A Graph Neural Network is one that operates on input data organized in terms of nodes and edges. The first formulation was proposed by Franco Scarselli et al. in 2009. The graph convolutional network is another popular version of GNNs, invented by Thomas Kipf & Max Welling in 2017. Some of the applications of GNNs include predicting molecular properties in drug discovery and detecting fraud rings in transactions using graphs where data connections matter as much as the data itself. 

Diffusion Models

A diffusion model creates samples by first beginning with random noise and then undoing the gradual addition of noise step by step. The mathematical theory behind this idea was originally developed by Jascha Sohl-Dickstein et al., in 2015. In 2020, Ho et al. created denoising diffusion probabilistic models, which enabled the diffusion model to be practically applied to images. In 2023, William Peebles and Saining Xie combined the transformer architecture with diffusion, resulting in the creation of the diffusion transformer (DiT). The diffusion transformer creates the video outputs of architectures such as Sora and Veo from OpenAI and Google respectively. 

Mixture of Experts (MoE)

Instead of feeding each input into the whole network, the mixture of experts’ neural network model divides an input into a subset of specialized sub-networks that are known as experts. The model was first proposed by Robert Jacobs, Michael Jordan, Steven Nowlan, and Geoffrey Hinton in 1991. In 2017, Noam Shazeer et al. proposed a large-scale and sparse variant of the mixture of experts model for use in deep learning. By mid-2026, Mixture-of-Experts had become the leading architecture pattern used by open-weight large language models, including DeepSeek V4-Pro and Llama 4 Maverick. This architecture allows a model to scale up the number of parameters while keeping the cost of each computation unchanged.

State Space Models (Mamba and Successors)

State space models selectively update an internal state continuously on the basis of input rather than doing a comparison of each token with every other token like self-attention. Mamba is the architecture that brought popularity to this technique for deep learning and was developed by Albert Gu and Tri Dao in 2023. While state space models have linear scalability with respect to sequence length, self-attention within the transformer framework scales quadratically. As of 2026, transformers continue to be the most widely used architecture for general language modeling whereas state space models are more used in specialized long sequences.

Comparing All 19 Types at a Glance

The table below compares every architecture across the dimensions that determine which one fits a given project: data type, core mechanism, primary strength, and primary limitation.

Architecture	Best For (Data Type)	Core Mechanism	Primary Strength	Primary Limitation
Feedforward (FNN)	Fixed, non-sequential data	One-directional weighted sum	Simple, fast to train	No memory or spatial awareness
Multilayer Perceptron	Tabular, non-linear data	Hidden layers + backpropagation	Learns non-linear relationships	No spatial/temporal structure
CNN	Images, video	Convolution + pooling	Automatic spatial feature extraction	Needs large labeled datasets
RNN	Short sequences	Feedback loop	Retains short-term context	Vanishing gradient on long sequences
LSTM	Long sequences	Input/forget/output gates	Retains context over long sequences	Slower to train, memory-heavy
GRU	Long sequences (lighter)	Reset/update gates	Faster training than LSTM	Slightly less expressive than LSTM
Transformer	Language, long-range context	Self-attention	Parallel training, scales to huge datasets	Requires massive compute and data
GAN	Synthetic data generation	Generator vs. discriminator	Highly realistic synthetic output	Unstable training, mode collapse
Autoencoder	Compression, anomaly detection	Encoder-decoder + latent space	Compact unsupervised representations	Can memorize instead of generalize
RBFN	Function approximation	Distance from reference points	Fast training on small datasets	Weak on high-dimensional data
SOM	Clustering, visualization	Competitive learning on a 2D grid	Reveals structure in unlabeled data	Limited to clustering/visualization
DBN	Layer-wise feature extraction	Stacked restricted Boltzmann machines	Enabled early deep pretraining	Largely superseded by newer methods
Siamese Network	Similarity comparison	Shared-weight twin subnetworks	Learns from very few examples per class	Needs paired training data
Capsule Network	Images with pose/orientation	Capsules encode pose + presence	Preserves part-to-whole relationships	Computationally expensive
Spiking Neural Network	Low-power/edge inference	Discrete spike-based signaling	Very low power on neuromorphic hardware	Limited software/hardware ecosystem
Graph Neural Network	Graph-structured data	Message passing between nodes	Captures relationships between data points	Struggles on very large, dense graphs
Diffusion Model	Image/video generation	Iterative denoising from noise	State-of-the-art generation quality	Slow, multi-step inference
Mixture of Experts	Large-scale language modeling	Sparse routing to expert subnetworks	Scales parameters without scaling per-token compute	High memory footprint
State Space Model	Very long sequences	Selective, linear-time state updates	Linear scaling with sequence length	Less mature tooling than transformers

How to Choose the Right Neural Network for Your Project

Match the Architecture to Your Data Type

Image and video information work best with Convolutional Neural Networks when the signal lies in spatial patterns such as edges or shapes. Sequences such as text and time-series data work well with LSTM, GRU, and transformer models when order and context are important factors. Graphs such as social networks and molecules work well with graph neural networks. The information that does not contain spatial or temporal information works well with Multilayer Perceptron.

Factor In Dataset Size and Compute Budget

Transformers and diffusion models need the biggest data sets and compute power, often surpassing the capacity of one single GPU to handle them in a realistic time frame. Radial Basis Function Networks and Self-Organizing Maps can be successfully trained using only a few thousands of samples in their data set. A mixture of expert architecture increases the number of parameters without the corresponding increase in prediction compute costs.

Common Selection Mistakes to Avoid

Three errors that degrade the model’s performance irrespective of the architecture’s correctness:

• Architecture selection based on popularity as opposed to being well-suited for the data. Multilayer Perceptron can perform on par with Transformer on small tabular datasets at a much lower cost of training.
• Omitting data pre-processing step. There isn’t any architecture that can make up for dirty, incomplete or unbalanced training data.
• Training deep architecture with a small amount of data without any form of regularization. Dropout, weight regularization, and early stopping reduce the risk of overfitting during training. 

A Short History of Neural Networks (1943–2026)

Nineteen architectures did not appear at once. Each one addressed a specific limitation in the architecture that came before it.

Year	Architecture / Milestone	Contributor(s)
1943	McCulloch-Pitts neuron (first mathematical neuron model)	Warren McCulloch, Walter Pitts
1958	Perceptron	Frank Rosenblatt
1982	Self-Organizing Map	Teuvo Kohonen
1986	Backpropagation popularized for MLP training	David Rumelhart, Geoffrey Hinton, Ronald Williams
1988	Radial Basis Function Network	David Broomhead, David Lowe
1991	Mixture of Experts (original concept)	Robert Jacobs, Michael Jordan, Steven Nowlan, Geoffrey Hinton
1993	Siamese Neural Network	Jane Bromley and colleagues
1997	LSTM; Spiking Neural Networks classified as a distinct model class	Sepp Hochreiter & Jürgen Schmidhuber; Wolfgang Maass
1998	CNN (LeNet-5)	Yann LeCun and colleagues
2006	Deep Belief Network	Geoffrey Hinton, Simon Osindero, Yee-Whye Teh
2009	Graph Neural Network (original formulation)	Franco Scarselli and colleagues
2013	Variational Autoencoder	Diederik Kingma, Max Welling
2014	GAN; GRU	Ian Goodfellow and colleagues; Kyunghyun Cho and colleagues
2015	Diffusion probabilistic models (theory)	Jascha Sohl-Dickstein and colleagues
2017	Transformer; Capsule Network; Sparse MoE; Graph Convolutional Network	Ashish Vaswani et al.; Sara Sabour & Geoffrey Hinton; Noam Shazeer et al.; Thomas Kipf & Max Welling
2020	Denoising Diffusion Probabilistic Models	Jonathan Ho and colleagues
2023	Diffusion Transformer (DiT); Mamba (state space model)	William Peebles & Saining Xie; Albert Gu & Tri Dao
2026	Mixture-of-Experts becomes the dominant open-weight LLM design pattern	Industry-wide adoption

Frequently Asked Questions

The Bottom Line

The nineteen architectures are not alternatives performing the same task. The architectures solve specific problems in data: Images, sequences, graphs, or unlabeled clusters. Matching the architecture to the data, the size of the dataset, and computational power contributes more to the performance of the model than the complexity of the architecture itself.