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一种用于非空间前语言推理的赫布理论方法。

A Hebbian Approach to Non-Spatial Prelinguistic Reasoning.

作者信息

Aguilar-Canto Fernando, Calvo Hiram

机构信息

Computational Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, Mexico.

出版信息

Brain Sci. 2022 Feb 17;12(2):281. doi: 10.3390/brainsci12020281.

Abstract

This research integrates key concepts of Computational Neuroscience, including the Bienestock-CooperMunro (BCM) rule, Spike Timing-Dependent Plasticity Rules (STDP), and the Temporal Difference Learning algorithm, with an important structure of Deep Learning (Convolutional Networks) to create an architecture with the potential of replicating observations of some cognitive experiments (particularly, those that provided some basis for sequential reasoning) while sharing the advantages already achieved by the previous proposals. In particular, we present Ring Model B, which is capable of associating visual with auditory stimulus, performing sequential predictions, and predicting reward from experience. Despite its simplicity, we considered such abilities to be a first step towards the formulation of more general models of prelinguistic reasoning.

摘要

本研究将计算神经科学的关键概念,包括比恩斯托克-库珀-蒙罗(BCM)规则、尖峰时间依赖可塑性规则(STDP)和时间差分学习算法,与深度学习的一个重要结构(卷积网络)相结合,创建了一种架构,该架构有潜力复制一些认知实验的观察结果(特别是那些为序列推理提供了一些基础的实验),同时具备先前提议已取得的优势。具体而言,我们提出了环形模型B,它能够将视觉与听觉刺激相关联,进行序列预测,并从经验中预测奖励。尽管其简单性,但我们认为这些能力是朝着构建更通用的前语言推理模型迈出的第一步。

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