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

Neural Circuits01:25

Neural Circuits

2.5K
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...
2.5K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.6K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.6K
Neural Regulation01:37

Neural Regulation

43.0K
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.
43.0K
Machines01:19

Machines

523
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
523
Spinal Cord: Information Processing01:10

Spinal Cord: Information Processing

3.0K
The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
Sensory Information Processing
Sensory information processing begins at the sensory receptors located in the skin and other tissues, which detect somatic sensory stimuli such as touch, temperature, or pain. These receptors function as catalysts, initiating...
3.0K
Machines: Problem Solving II01:30

Machines: Problem Solving II

606
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
606

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Distance Computation Based on Coupled Spin-Torque Oscillators: Application to Image Processing.

Physical review applied·2026
Same author

MATRIX: Mental heAlth diagnostics Through Real time Intelligent unified X-AI attribution reasoning.

Frontiers in digital health·2026
Same author

Foci, waves, excitability: Self-organization of phase waves in a model of asymmetrically coupled embryonic oscillators.

Physical review. E·2026
Same author

Pattern Formation in Cell Cultures.

Annual review of biophysics·2026
Same author

Impact of Low Hematocrit on On-Pump Coronary Artery Bypass Graft Surgery Outcomes.

Cureus·2025
Same author

Outbreak of Mumps among school children in a military setting in a South-West coastal district of India: November-December 2023.

Medical journal, Armed Forces India·2025
Same journal

Family of magnetic field-boosted superconductors in rhombohedral graphene.

Nature·2026
Same journal

What's the human cost of US research turmoil? A new film finds out.

Nature·2026
Same journal

Daily briefing: Ovaries start a second job after menopause.

Nature·2026
Same journal

Audio long read: Is the peptide craze backed by science? The promise behind the hype.

Nature·2026
Same journal

Scientists fight back against far-right plans to restrict academic freedom in Germany.

Nature·2026
Same journal

How AI can crack open the 'hidden curriculum' for neurodivergent students.

Nature·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Jan 3, 2026

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

10.7K

Hacia una inteligencia de máquina basada en picos con computación neuromórfica

Kaushik Roy1, Akhilesh Jaiswal2, Priyadarshini Panda2

  • 1Purdue University, West Lafayette, IN, USA. kaushik@purdue.edu.

Nature
|November 29, 2019
PubMed
Resumen
Este resumen es generado por máquina.

La computación neuromórfica, inspirada en la función cerebral, tiene como objetivo crear inteligencia artificial eficiente en energía. Este campo combina algoritmos y hardware, centrándose en el procesamiento basado en picos y sistemas impulsados por eventos para futuros avances de IA.

Más Videos Relacionados

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.2K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.4K

Videos de Experimentos Relacionados

Last Updated: Jan 3, 2026

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

10.7K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.2K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.4K

Área de la Ciencia:

  • Ciencias de la computación
  • La neurociencia
  • Inteligencia artificial

Sus antecedentes:

  • La computación neuromórfica está inspirada en las redes neuronales de picos similares al cerebro.
  • Su objetivo es desarrollar soluciones de inteligencia artificial (IA) energéticamente eficientes.
  • El campo integra circuitos de silicio con principios de procesamiento neuronal biológico.

Objetivo del estudio:

  • Para proporcionar una visión general de los avances de la computación neuromórfica.
  • Para resaltar los desarrollos tanto en algoritmos como en hardware.
  • Para hacer hincapié en la importancia del diseño de códigos de hardware de algoritmos.

Principales métodos:

  • Revisión de los algoritmos y el hardware neuromórficos existentes.
  • Análisis de la codificación basada en picos y representaciones basadas en eventos.
  • Discusión de los fundamentos del aprendizaje en marcos neuromórficos.

Principales resultados:

  • Progreso significativo en la implementación de marcos computacionales inspirados en el cerebro.
  • Evolución desde circuitos de silicio hasta complejos algoritmos y hardware.
  • Demostración de procesamiento impulsado por eventos para IA.

Conclusiones:

  • La computación neuromórfica es prometedora para la realización de una IA eficiente.
  • Se identifican los principales retos y las perspectivas de futuro.
  • El diseño del código de hardware de algoritmos es crucial para el desarrollo futuro.