# recurrent neural network > class of artificial neural network where connections between units form a directed graph along a temporal sequence **Wikidata**: [Q1457734](https://www.wikidata.org/wiki/Q1457734) **Wikipedia**: [English](https://en.wikipedia.org/wiki/Recurrent_neural_network) **Source**: https://4ort.xyz/entity/recurrent-neural-network ## Summary A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed graph along a temporal sequence, enabling the processing of sequential data by maintaining hidden states over time. It is a subclass of artificial neural networks and is distinguished from feedforward networks by its ability to retain information through recurrent connections. RNNs are foundational in machine learning, particularly for tasks involving time-series data, natural language processing, and sequential decision-making. ## Key Facts - A subclass of artificial neural network, specifically designed for sequential data processing. - Connections between units form a directed graph along a temporal sequence. - Opposite of feedforward neural networks, which lack recurrent connections. - Includes specialized variants such as echo state networks, Hamming neural networks, and bidirectional associative memory. - Related to reservoir computing and winner-take-all computational principles. - Includes architectures like gated recurrent units (GRUs) and long short-term memory (LSTMs). - Discovered by Shun'ichi Amari, a Japanese engineer and neuroscientist. - Shortened commonly as "RNN" or "РНС" in different languages. - Used in applications like fuzzy string matching (e.g., DeezyMatch) and hierarchical data processing (e.g., recursive neural networks). - Preceded by transformer architectures in some contexts. - Identified by GND ID 4379549-3 and MeSH codes G17.485.937 and L01.224.050.375.605.937. - Has aliases in multiple languages, including "reseau de neurones recurrents" (French) and "再帰型ニューラルネットワーク" (Japanese). - Linked to topics on platforms like Quora, GitHub, and Stack Exchange. - Includes entries in encyclopedias such as the Gran Enciclopèdia Catalana and Enciclopedia della Scienza e della Tecnica. ## FAQs **What is the primary function of a recurrent neural network?** A recurrent neural network processes sequential data by maintaining hidden states over time, allowing it to retain information from previous inputs and apply it to current processing steps. **How does a recurrent neural network differ from a feedforward neural network?** Unlike feedforward neural networks, which process data in a single direction without loops, recurrent neural networks have connections that form directed graphs along a temporal sequence, enabling them to retain information over time. **What are some specialized variants of recurrent neural networks?** Specialized variants include echo state networks, Hamming neural networks, diagonal recurrent neural networks, bidirectional associative memory, reservoir computing, and winner-take-all networks. **Who discovered recurrent neural networks?** Recurrent neural networks were discovered by Shun'ichi Amari, a Japanese engineer, neuroscientist, and mathematician. **What are some common applications of recurrent neural networks?** Recurrent neural networks are used in tasks such as natural language processing, time-series analysis, and hierarchical data processing, including applications like fuzzy string matching and candidate ranking. ## Why It Matters Recurrent neural networks are significant in machine learning due to their ability to process sequential data, making them essential for applications like speech recognition, language translation, and time-series forecasting. Their recurrent connections allow them to retain contextual information, which is critical for tasks where understanding the order and timing of data points is crucial. The development of RNNs, particularly in architectures like LSTMs and GRUs, has addressed the vanishing gradient problem in traditional RNNs, enabling more effective training and deployment in complex sequential tasks. Their impact extends to fields such as neuroscience, engineering, and computer science, where they serve as foundational models for understanding and simulating temporal dynamics. ## Notable For - Being a subclass of artificial neural networks specialized for sequential data processing. - Including architectures like LSTMs and GRUs to mitigate the vanishing gradient problem. - Being discovered by Shun'ichi Amari, contributing to advancements in neural network theory. - Having applications in fuzzy string matching and hierarchical data processing. - Being linked to topics on platforms like Quora, GitHub, and Stack Exchange. - Having entries in multiple encyclopedias and being referenced in scientific literature. ## Body ### Classification and Structure Recurrent neural networks are a subclass of artificial neural networks, distinguished by their directed graph connections that form along a temporal sequence. This structure allows them to process sequential data by maintaining hidden states over time, unlike feedforward neural networks, which lack such recurrent connections. The directed graph structure enables the network to retain information from previous inputs, making it suitable for tasks involving time-series data, natural language processing, and sequential decision-making. ### Variants and Specializations Recurrent neural networks include specialized variants such as echo state networks, which feature a sparsely connected hidden layer, and Hamming neural networks, which are used for pattern recognition. Other variants include diagonal recurrent neural networks, bidirectional associative memory, and reservoir computing, which are derived from RNN theory. Architectures like gated recurrent units (GRUs) and long short-term memory (LSTMs) are notable for their ability to address the vanishing gradient problem, making them more effective for training in complex sequential tasks. ### Historical Context and Discovery Recurrent neural networks were discovered by Shun'ichi Amari, a Japanese engineer, neuroscientist, and mathematician. Amari's contributions have been foundational in advancing neural network theory and applications. The development of RNNs has been influenced by preceding architectures and succeeded by models like transformers, which have further evolved the field of machine learning. ### Applications and Use Cases Recurrent neural networks are used in various applications, including fuzzy string matching through approaches like DeezyMatch, and hierarchical data processing through recursive neural networks. Their ability to retain contextual information makes them essential for tasks such as speech recognition, language translation, and time-series forecasting. The applications of RNNs extend to fields like neuroscience, engineering, and computer science, where they serve as models for understanding and simulating temporal dynamics. ### Related Entities and Connections Recurrent neural networks are related to other computational models, including bidirectional associative memory, winner-take-all networks, and Hopfield networks. They are also connected to frameworks like reservoir computing and principles such as the winner-take-all computational principle. These connections highlight the interdisciplinary nature of RNNs and their role in advancing machine learning and computational neuroscience. ### Identification and References Recurrent neural networks are identified by various identifiers, including GND ID 4379549-3 and MeSH codes G17.485.937 and L01.224.050.375.605.937. They have aliases in multiple languages and are referenced in encyclopedias such as the Gran Enciclopèdia Catalana and Enciclopedia della Scienza e della Tecnica. These references underscore the widespread recognition and application of RNNs in academic and professional contexts. ### Digital Presence and Community Recurrent neural networks have a strong digital presence, with topics and discussions available on platforms like Quora, GitHub, and Stack Exchange. They are also linked to topics on platforms like GitLab and Microsoft Academic, reflecting their relevance in the broader digital and academic communities. The presence of RNNs in these spaces highlights their importance in shaping current and future developments in machine learning and artificial intelligence. ## References 1. Freebase Data Dumps. 2013 2. BabelNet 3. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)