灵活的变送器网络

摘要：

当前的神经网络大多建立在MP模型上，MP模型通常将神经元公式化为对从其他神经元接收的信号的实值加权聚合执行激活函数。在本文中，我们提出了灵活的发射机（FT）模型，一种新的生物合理的神经元模型与灵活的突触可塑性。FT模型采用一对参数来模拟神经元之间的递质，并提出一个神经元排斥变量来记录调节的神经营养因子浓度，这导致FT模型的制定为一个两变量的两值函数，作为其特殊情况下，常用的MP神经元模型。这种建模方式使FT模型不仅具有更好的生物学真实性，而且能够处理复杂的数据，甚至是时间序列。为了展示其功能和潜力，我们提出了灵活的发射机网络（FTNet），它是建立在最常见的全连接前馈架构，以FT模型为基本构建块。FTNet允许梯度计算，可以通过复值域中的改进的反向传播算法来实现。在一系列任务上的实验表明了所提出的FTNet的优越性。这项研究提供了一个替代的基本构建块在神经网络和展示的可行性，开发人工神经网络与神经元的可塑性。

英文摘要：

Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. In this paper, we propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. The FT model employs a pair of parameters to model the transmitters between neurons and puts up a neuron-exclusive variable to record the regulated neurotrophin density, which leads to the formulation of the FT model as a two-variable two-valued function, taking the commonly-used MP neuron model as its special case. This modeling manner makes the FT model not only biologically more realistic, but also capable of handling complicated data, even time series. To exhibit its power and potential, we present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture taking the FT model as the basic building block. FTNet allows gradient calculation and can be implemented by an improved back-propagation algorithm in the complex-valued domain. Experiments on a board range of tasks show the superiority of the proposed FTNet. This study provides an alternative basic building block in neural networks and exhibits the feasibility of developing artificial neural networks with neuronal plasticity.

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