动态预测编码与蓄水池计算实现了噪声稳健的多感官语音识别。

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-09-23 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1464603
Yoshihiro Yonemura, Yuichi Katori
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引用次数: 0

摘要

多感觉统合是一个感知过程,大脑通过整合多种感觉模式的输入来合成统一的感知。一个关键问题是了解大脑如何利用皮层中的共同神经基础进行多感官整合。有人提出了一个基于储库计算的大脑皮层模型,以阐明大脑皮层神经元之间的循环连接在这一过程中的作用。水库计算非常适合语音识别等时间序列处理。本研究的重点是扩展基于水库计算的皮层模型,以涵盖皮层内的多感官整合。这项研究引入了一个多感官语音识别动态模型,利用预测编码与水库计算相结合。预测编码为大脑皮层的层次结构提供了一个框架。该模型整合了从多感官整合计算理论中得出的可靠性加权,以适应多感官时间序列处理。该模型针对的是需要管理复杂时间序列的多感官语音识别任务。我们观察到,通过提取时间上下文信息并根据感官噪声对感官输入进行加权,水库能有效识别语音。这些研究结果表明,递归网络的动态特性适用于多感官时间序列处理,从而将水库计算定位为多感官整合的合适模型。
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Dynamical predictive coding with reservoir computing performs noise-robust multi-sensory speech recognition.

Multi-sensory integration is a perceptual process through which the brain synthesizes a unified perception by integrating inputs from multiple sensory modalities. A key issue is understanding how the brain performs multi-sensory integrations using a common neural basis in the cortex. A cortical model based on reservoir computing has been proposed to elucidate the role of recurrent connectivity among cortical neurons in this process. Reservoir computing is well-suited for time series processing, such as speech recognition. This inquiry focuses on extending a reservoir computing-based cortical model to encompass multi-sensory integration within the cortex. This research introduces a dynamical model of multi-sensory speech recognition, leveraging predictive coding combined with reservoir computing. Predictive coding offers a framework for the hierarchical structure of the cortex. The model integrates reliability weighting, derived from the computational theory of multi-sensory integration, to adapt to multi-sensory time series processing. The model addresses a multi-sensory speech recognition task, necessitating the management of complex time series. We observed that the reservoir effectively recognizes speech by extracting time-contextual information and weighting sensory inputs according to sensory noise. These findings indicate that the dynamic properties of recurrent networks are applicable to multi-sensory time series processing, positioning reservoir computing as a suitable model for multi-sensory integration.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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