Semantic communication is poised to play a pivotal role in shaping the
landscape of future AI-driven communication systems. Its challenge of
extracting semantic information from the original complex content and
regenerating semantically consistent data at the receiver, possibly being
robust to channel corruptions, can be addressed with deep generative models.
This ICASSP special session overview paper discloses the semantic communication
challenges from the machine learning perspective and unveils how deep
generative models will significantly enhance semantic communication frameworks
in dealing with real-world complex data, extracting and exploiting semantic
information, and being robust to channel corruptions. Alongside establishing
this emerging field, this paper charts novel research pathways for the next
generative semantic communication frameworks.

语义通信在未来的人工智能驱动通信系统中扮演关键角色，通过深度生成模型来从原始复杂内容中提取语义信息并在接收端重新生成语义一致的数据，使得其对信道干扰具有鲁棒性。本文概述了语义通信面临的挑战，并揭示了深度生成模型如何显著增强语义通信框架，处理现实世界中的复杂数据、提取和利用语义信息以及对信道干扰具有鲁棒性。此外，本文还为下一代生成式语义通信框架提供了新的研究路径。

深度生成模型在语义交流中的增强 ——ICASSP 特别专题综述

Enhancing Semantic Communication with Deep Generative Models -- An  ICASSP Special Session Overview

We introduce a weight update formula that is expressed only in terms of
firing rates and their derivatives and that results in changes consistent with
those associated with spike-timing dependent plasticity (STDP) rules and
biological observations, even though the explicit timing of spikes is not
needed. The new rule changes a synaptic weight in proportion to the product of
the presynaptic firing rate and the temporal rate of change of activity on the
postsynaptic side. These quantities are interesting for studying theoretical
explanation for synaptic changes from a machine learning perspective. In
particular, if neural dynamics moved neural activity towards reducing some
objective function, then this STDP rule would correspond to stochastic gradient
descent on that objective function.

通过权重更新公式来模拟突触可塑性变化，该公式仅利用神经元的发射率和其导数做出权重更新的改变，该公式可以视为随机梯度下降，用于神经动力学寻求降低目标函数。