Abstract

This paper presents an original biomorphic neuron model, which differs from common IT models by a more complex synapse structure and from biological models by replacement of differential equations that describe the change in potential over time with explicit recurrence expressions by approximation of experimental data in the cortical neuron, and therefore, by transition from the spiking information coding to the coding using the average frequency of action potentials per a simulation step. This approach ensures sufficiently simple and efficient calculation of an ultra-large neural network in the stand-alone hardware with limited computing resources. The model consists of three separate functional parts: dendrites, soma, and axon, which allows implementing any connections between functional parts of different neurons, thus making the neural network architecture more flexible. To perform functional testing of the neuron model, the test neural network performing simple association and constructed as a consequent stack of functional blocks with primary connections organized using experimental neurophysiological data was simulated. It is shown that encoding of the information transmitted by the impulses, similar to biological ones, allows using memristors for calculating recurrence expressions that describe the change in the quantity of neurotransmitter receptors of the dendrite membrane. The elaborated biomorphic neuron model, defined conceptual principles of a neural network construction based on it, as well as replacement of synapses in the neural network with memristors will allow building an ultra-large biomorphic neural network that simulates the functioning of a separate brain cortical column in the stand-alone hardware—a biomorphic neuroprocessor.

中文翻译：

---

生物形态神经元模型和设计具有忆阻器突触的神经网络的生物形态神经处理器的原理

摘要

本文提出了一种原始的生物形态神经元模型，该模型不同于普通的IT模型，其突触结构更复杂，而生物学模型则通过替换微分方程来替代，该方程用显式递归表达式描述了随着时间的变化，通过近似皮层中的实验数据来描述电位随时间的变化。神经元，因此，通过从峰值信息编码过渡到使用每个模拟步骤的平均动作电位频率进行编码。这种方法可确保在具有有限计算资源的独立硬件中对超大型神经网络进行足够简单有效的计算。该模型包含三个独立的功能部分：树突，体细胞和轴突，可实现不同神经元功能部分之间的任何连接，从而使神经网络架构更加灵活。为了执行神经元模型的功能测试，模拟了执行简单关联并构造为功能块堆栈的测试神经网络，该功能块具有使用实验神经生理学数据组织的主要连接。结果表明，与生物信号相似，对脉冲所传递信息的编码允许使用忆阻器来计算描述树枝状膜神经递质受体数量变化的递归表达式。精细的生物形态神经元模型，定义了基于该模型的神经网络构造的原理，