Abstract Biological studies have shown that the interaction between neurons are based on neurotransmitters, which transmit signals between neurons, and that one neuron sends information to another neuron by releasing a number of different neurotransmitters, which play different roles. Motivated by this biological discovery, a novel neural networks model is proposed by extending the dimension of connection weights from one to multiple, i.e. there are multiple not only one connections between each two units. The number of dimensions of connection weight represents the number of categories of neurotransmitters and different components of the weight correspond to different neurotransmitters. In order to make these neurotransmitters collaborate and compete appropriately, the input and output for each unit in our proposed model have been heuristically defined. From the biological perspective, the proposed neural network is much closer to biological neural network. From the viewpoint of new model structure, the characteristic that the activation of each hidden unit is based on several filters, can improve the interpretability of features learned by the proposed neural network. Experimental results on MNIST, NORB and several other data sets have demonstrated that the performances of traditional neural networks can be improved by extending the dimension of connection weight between units, and the idea of multiple connection weights provides a new paradigm for the design of neural networks.

中文翻译：

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具有多个连接权重的神经网络

摘要 生物学研究表明，神经元之间的相互作用是基于神经递质，神经递质在神经元之间传递信号，一个神经元通过释放多种不同的神经递质将信息传递给另一个神经元，这些递质起不同的作用。受这一生物学发现的启发，提出了一种新的神经网络模型，将连接权重的维度从一个扩展到多个，即每两个单元之间有多个连接，而不仅仅是一个连接。连接权重的维数代表神经递质的类别数，权重的不同分量对应不同的神经递质。为了让这些神经递质适当地协作和竞争，我们提出的模型中每个单元的输入和输出都是启发式定义的。从生物学的角度来看，所提出的神经网络更接近于生物神经网络。从新的模型结构来看，每个隐藏单元的激活基于多个过滤器的特点，可以提高所提出的神经网络学习到的特征的可解释性。在MNIST、NORB等几个数据集上的实验结果表明，传统神经网络的性能可以通过扩展单元之间连接权的维度来提高，多重连接权的思想为神经网络的设计提供了新的范式. 从新的模型结构来看，每个隐藏单元的激活基于多个过滤器的特点，可以提高所提出的神经网络学习到的特征的可解释性。在MNIST、NORB等几个数据集上的实验结果表明，传统神经网络的性能可以通过扩展单元之间连接权的维度来提高，多重连接权的思想为神经网络的设计提供了新的范式. 从新的模型结构来看，每个隐藏单元的激活基于多个过滤器的特点，可以提高所提出的神经网络学习到的特征的可解释性。在MNIST、NORB等几个数据集上的实验结果表明，传统神经网络的性能可以通过扩展单元之间连接权的维度来提高，多重连接权的思想为神经网络的设计提供了新的范式.