吸引器神经网络中的矢量符号有限状态机

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Madison Cotteret;Hugh Greatorex;Martin Ziegler;Elisabetta Chicca
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引用次数: 0

摘要

Hopfield 吸引子网络是人类记忆的稳健分布式模型,但它们缺乏一种通用机制来实现与输入相关的吸引子转换。我们提出了构建规则,使吸引子网络可以实现任意的有限状态机(FSM),其中状态和刺激由高维随机向量表示,所有状态转换都由吸引子网络的动力学来实现。数值模拟显示,就可实现的 FSM 的最大规模而言,该模型的容量与密集双极状态向量的吸引子网络规模呈线性关系,而对于稀疏二进制状态向量则近似二次方关系。我们的研究表明,该模型对不精确和有噪声的权重具有很强的鲁棒性,因此是使用高密度但不可靠的设备实现的最佳候选模型。通过赋予吸引子网络模拟任意 FSM 的能力,我们提出了一条可行的途径,使 FSM 可以作为分布式计算基元存在于生物神经网络中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vector Symbolic Finite State Machines in Attractor Neural Networks
Hopfield attractor networks are robust distributed models of human memory, but they lack a general mechanism for effecting state-dependent attractor transitions in response to input. We propose construction rules such that an attractor network may implement an arbitrary finite state machine (FSM), where states and stimuli are represented by high-dimensional random vectors and all state transitions are enacted by the attractor network's dynamics. Numerical simulations show the capacity of the model, in terms of the maximum size of implementable FSM, to be linear in the size of the attractor network for dense bipolar state vectors and approximately quadratic for sparse binary state vectors. We show that the model is robust to imprecise and noisy weights, and so a prime candidate for implementation with high-density but unreliable devices. By endowing attractor networks with the ability to emulate arbitrary FSMs, we propose a plausible path by which FSMs could exist as a distributed computational primitive in biological neural networks.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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