Neurons with Multiplicative Interactions of Nonlinear Synapses
Neurons with Multiplicative Interactions of Nonlinear Synapses
复制标题
DOI：
10.1142/s0129065719500126
复制
发表时间：
2019-10-01
影响因子：
8
通讯作者：
Yamashita, Kazuya
中科院分区：
文献类型：
--
作者：
Todo, Yuki;Tang, Zheng;Yamashita, Kazuya
神经元是大脑和神经系统的基本单位。建立一个良好的人类神经元模型不仅对神经生物学，而且对计算机科学和许多其他领域都非常重要。McCulloch和Pitts神经元模型是最广泛使用的神经元模型，但长期以来一直被批评为鉴于真实的神经元的性质及其执行的计算而被过度简化。另一方面，树突在神经元执行的整体计算中起着关键作用，这一点已被广泛接受。然而，树突计算的建模和右突触到右树突的分配仍然是该领域的开放问题。在这里，我们提出了一种新的树突神经模型（DNM），模仿的本质，已知的非线性相互作用的树突的输入。在该模型中，每个输入通过距离依赖的非线性突触连接到分支，并且每个分支对输入执行简单的乘法。然后，索马将所有分支的加权积相加，产生神经元的输出信号。我们表明，丰富的非线性树突反应和强大的非线性神经计算能力，以及许多已知的神经元和树突的神经生物学现象，可以理解和解释的DNM。此外，我们表明，该模型是能够学习和发展的内部结构，如突触的位置在树突状分支和突触的类型，这是适合于特定的任务-例如，线性不可分离的问题，一个现实世界的基准问题-玻璃分类和方向选择性问题。
Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron's output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task - for example, the linearly nonseparable problem, a real-world benchmark problem - Glass classification and the directional selectivity problem.