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Researchers study learning of a behavior through the use of operant conditioning. This type of learning involves associating the behavior with a consequence, which is a reward or punishment. If the consequence is a reward, it leads to reinforcement of the desired behavior. One type of reinforcement approach is positive reinforcement, where the behavior is rewarded with an artificial, natural, or social reinforcer. Studies using positive reinforcement as a tool can help tease out important...
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Reinforcement01:23

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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:
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Dense connective tissue contains more collagen fibers than loose connective tissue. As a consequence, it displays greater resistance to stretching. There are two major categories of dense connective tissue— regular and irregular.
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This article contains a set of protocols for the development of human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) networks cultured on multiwell MEA plates to reversibly electroporate the cell membrane for action potential measurements. High-throughput recordings are obtained from the same cell sites repeatedly over...
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The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
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Video Experimental Relacionado

Updated: Jan 20, 2026

Positive Reinforcement-Operant Conditioning Studies
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Aprendizaje por refuerzo en redes biológicas densamente recurrentes

Miles Walter Churchland1,2, Jordi Garcia-Ojalvo1

  • 1Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Dr Aiguader, 88, 08003 Barcelona, Spain.

iScience
|January 19, 2026
PubMed
Resumen

Desarrollamos un nuevo método de entrenamiento de IA, Evolutionary Nonlinear Optimization with Mesh Adaptive Direct search (ENOMAD), para entrenar eficientemente redes neuronales complejas. Este enfoque inspirado biológicamente mejora el rendimiento de la red en tareas específicas.

Palabras clave:
método bioinformáticoneurociencia

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Área de la Ciencia:

  • Neurociencia computacional
  • Inteligencia artificial
  • Computación evolutiva

Sus antecedentes:

  • El entrenamiento de redes neuronales recurrentes (RNN) es un desafío debido a problemas de gradiente (explosión/desvanecimiento) y la lenta convergencia de los métodos evolutivos.
  • Los métodos existentes tienen dificultades con la optimización eficiente de arquitecturas neuronales complejas inspiradas biológicamente.

Objetivo del estudio:

  • Introducir un marco de optimización novedoso y sin gradientes para el entrenamiento de redes neuronales recurrentes.
  • Evaluar el método propuesto en tareas biológicamente relevantes utilizando el conectoma de Caenorhabditis elegans.
  • Investigar la eficacia de los priors de peso derivados biológicamente en el refinamiento de redes neuronales.

Principales métodos:

  • Desarrollamos Evolutionary Nonlinear Optimization with Mesh Adaptive Direct search (ENOMAD), un marco híbrido que combina la exploración evolutiva y la explotación de búsqueda directa.
  • Implementamos principios de aprendizaje por refuerzo dentro del proceso de optimización sin gradientes.
  • Utilizamos el conectoma neural de Caenorhabditis elegans y tareas de búsqueda de alimento para la evaluación comparativa.

Principales resultados:

  • ENOMAD entrenó con éxito redes recurrentes, superando significativamente a los circuitos nativos no entrenados.
  • El método demostró la especialización eficiente de redes recurrentes naturales para tareas específicas.
  • Los priors de peso derivados biológicamente permitieron el refinamiento en lugar de la reconstrucción completa del circuito neuronal, mostrando aprendizaje por transferencia.

Conclusiones:

  • La integración de la búsqueda evolutiva con la optimización no lineal ofrece una estrategia eficiente y biológicamente fundamentada para la especialización de RNN.
  • ENOMAD proporciona una herramienta poderosa para avanzar en la investigación en neurociencia computacional e IA.
  • El marco facilita el estudio del aprendizaje y la adaptación en sistemas neuronales biológicos y artificiales.