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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Cálculo dependiente del contexto por dinámicas recurrentes en la corteza prefrontal.

Valerio Mante1, David Sussillo, Krishna V Shenoy

  • 11] Howard Hughes Medical Institute and Department of Neurobiology, Stanford University, Stanford, California 94305, USA [2] Institute of Neuroinformatics, University of Zurich/ETH Zurich, CH-8057 Zurich, Switzerland. [3].

Nature
|November 9, 2013
PubMed
Resumen
Este resumen es generado por máquina.

Los investigadores estudiaron la actividad de la corteza prefrontal en monos para comprender el comportamiento flexible. Descubrieron que la actividad neuronal compleja refleja un proceso dinámico unificado para seleccionar e integrar la información sensorial.

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Área de la Ciencia:

  • La neurociencia es la neurociencia.
  • Ciencias Cognitivas Ciencias Cognitivas.
  • La neurociencia computacional es la neurociencia computacional.

Sus antecedentes:

  • La corteza prefrontal (CFP) es crucial para los comportamientos flexibles y dependientes del contexto.
  • Los cálculos precisos realizados por las neuronas PFC siguen siendo poco conocidos, especialmente sus complejos patrones de respuesta.

Objetivo del estudio:

  • Investigar los mecanismos neuronales que subyacen a la selección flexible y la integración de la información sensorial en el PFC.
  • Comprender cómo la actividad compleja de una sola neurona contribuye al comportamiento.

Principales métodos:

  • Grabación de la actividad de la corteza prefrontal en monos macacos que realizan una tarea que requiere la selección flexible y la integración de entradas sensoriales ruidosas.
  • Se analizó la dinámica neuronal a nivel de población.
  • Utilizó una red neuronal recurrente entrenada (RNN) para modelar la actividad del PFC.

Principales resultados:

  • La complejidad de una sola neurona se explica por procesos dinámicos a nivel de población.
  • La dinámica de la población observada fue reproducida con éxito por un RNN entrenado.
  • Identificó un nuevo mecanismo para la selección e integración sensorial.

Conclusiones:

  • La selección e integración de entradas relevantes para la tarea son aspectos unificados de un solo proceso dinámico dentro de los circuitos PFC.
  • Este marco dinámico ofrece una nueva perspectiva sobre los cálculos dependientes del contexto en el cerebro.
  • Los hallazgos sugieren que las RNN pueden modelar funciones PFC complejas.