Neural kernels for recursive support vector regression as a model for episodic memory

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
C. Leibold
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引用次数: 2

Abstract

Retrieval of episodic memories requires intrinsic reactivation of neuronal activity patterns. The content of the memories is thereby assumed to be stored in synaptic connections. This paper proposes a theory in which these are the synaptic connections that specifically convey the temporal order information contained in the sequences of a neuronal reservoir to the sensory-motor cortical areas that give rise to the subjective impression of retrieval of sensory motor events. The theory is based on a novel recursive version of support vector regression that allows for efficient continuous learning that is only limited by the representational capacity of the reservoir. The paper argues that hippocampal theta sequences are a potential neural substrate underlying this reservoir. The theory is consistent with confabulations and post hoc alterations of existing memories.
递归支持向量回归作为情景记忆模型的神经核
情景记忆的恢复需要神经元活动模式的内在再激活。由此,假定存储器的内容存储在突触连接中。本文提出了一种理论,其中这些突触连接专门将神经元库序列中包含的时间顺序信息传递到感觉运动皮层区域,从而产生检索感觉运动事件的主观印象。该理论基于支持向量回归的一种新的递归版本,该版本允许有效的连续学习,而该学习仅受储层代表能力的限制。该论文认为,海马θ序列是潜在的神经基质,在这个储层的基础上。该理论与现有记忆的虚构和事后改变是一致的。
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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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