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Cuantificación de la similitud de la representación relevante para la tarea mediante la correlación de la variable de

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    PubMed
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    Este resumen es generado por máquina.

    Introducimos la correlación de la variable de decisión (DVC) para comparar cómo los cerebros y los modelos de IA toman decisiones. Los modelos de IA muestran una menor similitud en la estrategia de decisión con los cerebros de los monos, lo que sugiere una divergencia en la representación relevante para la tarea.

    Palabras clave:
    correlación de la variable de decisiónrepresentaciones neuronalesredes neuronales profundasvisión artificialcorteza visualestrategias de decisión

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    Área de la Ciencia:

    • Neurociencia
    • Inteligencia Artificial
    • Visión por Computadora

    Sus antecedentes:

    • La comparación de las representaciones neuronales en la corteza visual con las redes neuronales profundas (DNN) es crucial para comprender la visión biológica y artificial.
    • Estudios anteriores muestran resultados mixtos con respecto a la similitud entre las actividades neuronales y las representaciones de las DNN.
    • Se necesita un nuevo método para evaluar específicamente las estrategias de decisión relevantes para la tarea, no solo la alineación general de la representación.

    Objetivo del estudio:

    • Proponer y evaluar la correlación de la variable de decisión (DVC) como un enfoque novedoso para cuantificar la similitud de las estrategias de decisión entre observadores (cerebros o modelos).
    • Comparar las representaciones relevantes para la tarea de la corteza visual del mono (V4/IT) con las de las DNN entrenadas en la clasificación de imágenes.

    Principales métodos:

    • Desarrollo de la correlación de la variable de decisión (DVC) para medir la correlación imagen por imagen de las decisiones decodificadas a partir de representaciones internas.
    • Recopilación de registros neuronales del V4/IT del mono durante una tarea de clasificación.
    • Utilización de varias DNN entrenadas en tareas de clasificación de imágenes, incluidas aquellas con entrenamiento adversario y preentrenamiento en grandes conjuntos de datos.

    Principales resultados:

    • La similitud entre modelos y entre monos fue comparable, pero la similitud entre modelos y monos fue consistentemente menor.
    • La correlación de la variable de decisión (DVC) disminuyó a medida que mejoró el rendimiento del modelo en ImageNet-1k.
    • El entrenamiento adversario y el preentrenamiento en grandes conjuntos de datos no mejoraron la similitud entre modelos y monos en las dimensiones relevantes para la tarea.

    Conclusiones:

    • La correlación de la variable de decisión (DVC) captura eficazmente la información relevante para la tarea, revelando diferencias en las estrategias de decisión.
    • Las representaciones relevantes para la tarea en el V4/IT del mono divergen de las aprendidas por las DNN estándar de clasificación de imágenes.
    • Los métodos actuales de entrenamiento de DNN no cierran completamente la brecha en las estrategias de toma de decisiones en comparación con la visión biológica.