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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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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.
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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Introducimos un modelo de red neuronal para inferir estados epidémicos, incorporando covariables de nodos para condiciones iniciales más realistas. Este enfoque mejora la recuperación del estado, aunque las transiciones de fase pueden crear una brecha estadística a computacional.

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Área de la Ciencia:

  • Sistemas complejos de sistemas complejos.
  • La inferencia estadística es la inferencia estadística.
  • Aprendizaje automático Aprendizaje automático.

Sus antecedentes:

  • Los procesos estocásticos en los gráficos modelan epidemias, pero a menudo asumen estados iniciales aleatorios.
  • Los sistemas del mundo real tienen covariables de nodos que influyen en los estados iniciales, un factor a menudo ignorado en la inferencia.

Objetivo del estudio:

  • Para modelar el estado inicial de los procesos estocásticos en gráficos como una función de la red neuronal de los nodos covariables.
  • Desarrollar un marco de inferencia bayesiana que aproveche tanto la dinámica de los procesos como la información covariada.
  • Para analizar el impacto de los priores de la red neuronal en el estado y la recuperación de la trayectoria.

Principales métodos:

  • Se derivó un algoritmo híbrido de propagación de creencias y transmisión aproximada de mensajes (BP-AMP).
  • El algoritmo integra la dinámica de difusión con la información de las covariables de los nodos.
  • El rendimiento se comparó con los métodos que utilizan solo información de propagación o solo covariante.

Principales resultados:

  • El modelo propuesto mejora la recuperación de los estados iniciales y las trayectorias de propagación mediante la incorporación de información covariada.
  • Se observaron transiciones de fase de primer orden en algunos regímenes, en particular con pesos de redes neuronales distribuidas de Rademacher.
  • Surgió una brecha estadística-computacional donde la recuperación perfecta es teóricamente posible pero computacionalmente inalcanzable.

Conclusiones:

  • La integración de priores de redes neuronales basados en covariables de nodos mejora la inferencia para procesos estocásticos en gráficos.
  • Las transiciones de fase y la brecha resultante de estadística a computacional presentan desafíos para una estimación precisa del estado.
  • El algoritmo BP-AMP ofrece un enfoque robusto para manejar problemas complejos de inferencia con información de covariante integrada.