Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Per-Unit Sequence Models

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...

Neuron Structure

Neuron Structure

Neuron Structure

Neuron Structure

Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Further evidence for embodied cognition: The link between spontaneous respiratory oscillations and number processing.

PloS one·2026

Same author

Comment on "Integrating large language models and active inference to understand eye movements in reading and dyslexia" by Donnarumma et al.

Physics of life reviews·2026

Same author

Biomarkers of stroke recovery using EEG-based resting-state functional connectivity: a systematic review.

Journal of neuroengineering and rehabilitation·2026

Same author

MolVE: An Open-Source Web Platform for Visualizing and Evaluating AI-Designed Molecules to Aid in Prioritization.

Journal of chemical information and modeling·2026

Same author

Green design in living and bedroom spaces: exploring environmental restorativeness and affective qualities of spaces.

Frontiers in psychology·2025

Same author

Obesity accelerates brain ageing: a multimodal imaging study.

Brain communications·2025

Same journal

Pronoun Resolution in Turkish: The Interplay of Referential Form, Word Order, and Implicit Causality.

Cognitive science·2026

Same journal

What's in a Color?: Language, Synesthesia, and Categorical Perception.

Cognitive science·2026

Same journal

Reasoning Beyond Explicit Rules: Adults' and Children's Use of Closure Principles in Novel Cases.

Cognitive science·2026

Same journal

Intermediary Object States Are Activated by Sentences Describing Completed Events.

Cognitive science·2026

Same journal

Large Language Models Estimate Fine-Grained Human Color-Concept Associations.

Cognitive science·2026

Same journal

Computational Models of Causal Reasoning: Bayesian Accounts of Normative Violations.

Cognitive science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 10, 2026

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

Learning Orthographic Structure With Sequential Generative Neural Networks.

Alberto Testolin^1,2, Ivilin Stoianov^2,3, Alessandro Sperduti⁴

¹Department of Developmental Psychology and Socialisation, University of Padova.

Cognitive Science

|June 16, 2015

Summary

This summary is machine-generated.

Sequential Restricted Boltzmann Machines (RBMs) learn English orthographic structure, accurately modeling graphotactics and generating pseudowords. These stochastic recurrent neural networks show promise for understanding temporal cognition.

Keywords:

Connectionist modeling Generative models Orthographic structure Probabilistic graphical models Recurrent neural networks Restricted Boltzmann machines Statistical sequence learning Unsupervised learning

More Related Videos

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Apr 10, 2026

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Learning event sequence structure is crucial for cognition, especially language.
Probabilistic generative models offer a neurobiologically plausible approach but are underutilized in connectionist modeling.
Restricted Boltzmann Machines (RBMs) are stochastic recurrent neural networks adept at unsupervised learning of high-order data structures.

Purpose of the Study:

To investigate the capacity of sequential Restricted Boltzmann Machines (RBMs) for learning orthographic structure in English monosyllables.
To assess the performance of sequential RBMs in building a generative model of letter sequences.
To compare sequential RBMs against other models for sequence learning and generation.

Main Methods:

A sequential version of the Restricted Boltzmann Machine (RBM) was employed for unsupervised generative learning.
The network was trained on a corpus of English monosyllable letter sequences to learn graphotactic regularities.
Performance was evaluated based on predictive accuracy and the quality of autonomously generated pseudowords, with comparisons to extended simple recurrent networks, n-grams, and hidden Markov models.

Main Results:

The sequential RBM successfully learned an accurate probabilistic model of English graphotactics.
The model demonstrated proficiency in predicting subsequent letters within a given context.
High-quality pseudowords were autonomously generated by the network, indicating successful learning of orthographic structure.

Conclusions:

Sequential RBMs are effective in learning and modeling the sequential structure of language, specifically English orthography.
Stochastic recurrent neural networks, including sequential RBMs, present promising avenues for computational modeling of temporal cognition.
The findings support the utility of these connectionist models for understanding sequence processing in cognitive systems.