Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Observational Learning01:12

Observational Learning

1.1K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

436
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
436
Neural Regulation01:37

Neural Regulation

43.6K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
43.6K
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

534
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
534
Reinforcement01:23

Reinforcement

992
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
992
Neural Control of Respiration01:18

Neural Control of Respiration

5.1K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
5.1K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Prediction of Trends and Bioclimatic Factors Influencing the Monthly Incidence of Zoonotic Cutaneous Leishmaniasis Using Arima and Sarima Time Series Models in Maraveh Tappeh County, Golestan Province, Iran.

Journal of arthropod-borne diseases·2026
Same author

Recovering Reward Functions From Distributed Expert Demonstrations via Bi-Level Maximum-Likelihood Optimization.

IEEE transactions on neural networks and learning systems·2026
Same author

The crossroads between osteosarcopenia and intrinsic capacity-a narrative review.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

The interplay between osteosarcopenia and intrinsic capacity: insights and associations with all-cause mortality in the Toledo Study for Healthy Aging.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Bayesian Topology Inference of Regulatory Networks under Partial Observability.

Results in control and optimization·2026
Same authorSame journal

Pareto-Optimal Interventions in Gene Regulatory Networks using Signal Temporal Logic.

Proceedings of the ... American Control Conference. American Control Conference·2026

Video Experimental Relacionado

Updated: Feb 24, 2026

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.1K

Aprendizaje profundo por refuerzo para la intervención de redes regulatorias parcialmente observables

Seyed Hamid Hosseini1, Mahdi Imani1

  • 1Department of Electrical and Computer Engineering at Northeastern University.

Proceedings of the ... American Control Conference. American Control Conference
|February 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un marco de aprendizaje profundo por refuerzo para optimizar las intervenciones en redes regulatorias génicas (GRN) con datos incompletos. El método gestiona eficazmente la incertidumbre para controlar la actividad génica, superando a los enfoques existentes.

Palabras clave:
aprendizaje profundo por refuerzoredes regulatorias génicasintervenciónobservabilidad parcialgestión de la incertidumbrebiología computacionalbiología de sistemasbioinformática

Más Videos Relacionados

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K

Videos de Experimentos Relacionados

Last Updated: Feb 24, 2026

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.1K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K

Área de la Ciencia:

  • Biología Computacional
  • Biología de Sistemas
  • Bioinformática

Sus antecedentes:

  • Las redes regulatorias génicas (GRN) controlan las funciones celulares.
  • El análisis de GRN en el mundo real se ve desafiado por la observabilidad parcial y los datos ruidosos.
  • Los métodos de intervención existentes a menudo asumen información completa del estado del sistema.

Objetivo del estudio:

  • Desarrollar un marco de aprendizaje profundo por refuerzo para políticas de intervención óptimas en GRN parcialmente observables.
  • Abordar las limitaciones de los métodos existentes que asumen una observabilidad completa.
  • Gestionar las incertidumbres en los datos de expresión génica y la estocasticidad de la actividad génica.

Principales métodos:

  • Extensión de los modelos de redes booleanas para incorporar la observabilidad parcial.
  • Formulación de políticas de intervención óptimas en el espacio de creencias, utilizando estados de creencias para representar las distribuciones posteriores del estado.
  • Aplicación de la red Q profunda (DQN) para la aproximación escalable de políticas óptimas.
  • Demostración analítica de la convergencia a soluciones óptimas de programación dinámica bajo incertidumbre reducida.

Principales resultados:

  • El marco propuesto de aprendizaje profundo por refuerzo maneja eficazmente la observabilidad parcial en las GRN.
  • El estado de creencia captura con éxito la incertidumbre de los datos y la estocasticidad de la actividad génica.
  • Los experimentos numéricos en una GRN de melanoma muestran un rendimiento mejorado para mantener los estados deseados y reducir la activación de genes relacionados con el cáncer en comparación con los métodos existentes.

Conclusiones:

  • El marco desarrollado proporciona un enfoque robusto para diseñar estrategias de intervención en sistemas biológicos complejos y parcialmente observables.
  • El aprendizaje profundo por refuerzo ofrece una solución escalable para optimizar las intervenciones en GRN.
  • El método es prometedor para aplicaciones en medicina de precisión y control de enfermedades al dirigirse a vías regulatorias génicas específicas.