The realization of bio-compatible nanomachines would pave the way for developing novel diagnosis and treatment techniques for the dysfunctions of intra-body nanonetworks and revolutionize the traditional healthcare methodologies making them less invasive and more efficient. The network of these nanomachines is aimed to be used for treating neuronal diseases such as developing an implant that bridges over the injured spinal cord to regain its normal functionality. Thus, nanoscale communication paradigms are needed to be investigated to facilitate communication between nanomachines. Communication among neurons is one of the most promising nanoscale communication paradigm, which necessitates the thorough communication theoretical analysis of information transmission among neurons. The information flow in neuro-spike communication channel is regulated by the ability of neurons to change synaptic strengths over time, i.e. synaptic plasticity. Thus, the performance evaluation of the nervous nanonetwork is incomplete without considering the influence of synaptic plasticity. In this paper, we focus on information transmission among hippocampal pyramidal neurons and provide a comprehensive channel model for MISO neuro-spike communication, which includes axonal transmission, vesicle release process, synaptic communication and spike generation. In this channel, the spike timing dependent plasticity (STDP) model is used to cover both synaptic depressiofan and potentiation depending on the temporal correlation between spikes generated by input and output neurons. Since synaptic strength changes depending on different physiological factors such as spiking rate of presynaptic neurons, number of correlated presynaptic neurons and the correlation factor among them, we simulate this model with correlated inputs and analyze the evolution of synaptic weights over time. Moreover, we calculate average mutual information between input and output of the channel and find the impact of plasticity and correlation among inputs on the information transmission. The simulation results reveal the impact of different physiological factors related to either presynaptic or postsynaptic neurons on the performance of MISO neuro-spike communication. Moreover, they provide guidelines for selecting the system parameters in a bio-inspired neuronal network according to the requirements of different applications.

中文翻译：

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长期可塑性对神经网络信息传输的影响。

生物相容性纳米机器的实现将为开发针对体内纳米网络功能障碍的新型诊断和治疗技术铺平道路，并革新传统的医疗保健方法，使其侵入性更小，效率更高。这些纳米机器的网络旨在用于治疗神经元疾病，例如开发桥接受伤脊髓以恢复其正常功能的植入物。因此，需要研究纳米级通信范例以促进纳米机器之间的通信。神经元之间的交流是最有前途的纳米级交流范式之一，它需要对神经元之间的信息传递进行彻底的交流理论分析。神经突突通讯通道中的信息流受神经元随时间改变突触强度（即突触可塑性）的能力调节。因此，不考虑突触可塑性的影响，神经纳米网络的性能评估是不完整的。在本文中，我们着重于海马锥体神经元之间的信息传递，并为MISO神经-尖峰交流提供了一个全面的通道模型，包括轴突传递，囊泡释放过程，突触传递和尖峰产生。在此通道中，取决于输入和输出神经元所产生的尖峰之间的时间相关性，使用了尖峰时序相关可塑性（STDP）模型来覆盖突触降压和增强。由于突触强度的变化取决于不同的生理因素，例如突触前神经元的突增率，相关的突触前神经元的数量以及它们之间的相关因子，因此我们用相关的输入来模拟该模型，并分析突触权重随时间的变化。此外，我们计算通道的输入和输出之间的平均相互信息，并发现输入之间的可塑性和相关性对信息传输的影响。模拟结果揭示了与突触前或突触后神经元相关的不同生理因素对MISO神经突峰通讯性能的影响。此外，它们提供了根据不同应用程序的需求在生物启发式神经元网络中选择系统参数的指南。