The Hopfield network is an example of an artificial neural network used to implement associative memories. A binary digit represents the neuron’s state of a traditional Hopfield neural network. Inspired by the human brain’s ability to cope simultaneously with multiple sensorial inputs, this paper presents three multi-modal Hopfield-type neural networks that treat multi-dimensional data as a single entity. In the first model, called the vector-valued Hopfield neural network, the neuron’s state is a vector of binary digits. Synaptic weights are modeled as finite impulse response (FIR) filters in the second model, yielding the so-called convolutional associative memory. Finally, the synaptic weights are modeled by linear time-varying (LTV) filters in the third model. Besides their potential applications for multi-modal intelligence, the new associative memories may also be used for signal and image processing and solve optimization and classification tasks.

Hopfield 网络是用于实现联想记忆的人工神经网络的一个例子。二进制数字表示传统 Hopfield 神经网络的神经元状态。受人脑同时处理多个感官输入的能力的启发，本文提出了三个多模态 Hopfield 型神经网络，它们将多维数据视为单个实体。在第一个模型中，称为向量值 Hopfield 神经网络，神经元的状态是二进制数字的向量。在第二个模型中，突触权重被建模为有限脉冲响应 (FIR) 滤波器，从而产生所谓的卷积联想记忆。最后，突触权重由第三个模型中的线性时变 (LTV) 滤波器建模。除了它们在多模态智能方面的潜在应用外，