以突触为中心的自由能原理
A synapse-centric account of the free energy principle
摘要：
自由能原理（FEP）是一个数学框架，描述了生物系统如何在其环境中自我组织和生存。这一原理提供了多个尺度的见解，从高级行为和认知功能（如注意力或觅食）到专门的皮质微电路的动态，表明FEP表现在几个级别的大脑功能上。在这里，我们将FEP应用于大脑最小的功能单位之一：单个兴奋性突触连接。通过专注于实验上充分理解的生物系统，我们能够从第一原理中推导出学习规则，同时保持最小的假设。这种以突触为中心的FEP解释预测突触通过随机突触释放与突触后神经元的胞体相互作用，以探测它们的行为，并使用反向传播动作电位作为反馈来更新突触权重。涌现学习规则是受调节的三联STDP规则，其仅取决于突触前和突触后尖峰的时间以及突触的内部状态。学习规则的参数完全由突触后神经元模型的参数确定，这表明突触和体细胞室之间的密切相互作用，并对突触动力学进行精确预测。突触层面的不确定性会自动导致网络层面上的不确定性表示，这些不确定性表现在模糊的情况下。我们表明，FEP学习规则可以应用于尖峰神经网络的监督和无监督学习和一个闭环学习任务的行为代理与环境进行交互。
英文摘要：
The free energy principle (FEP) is a mathematical framework that describes how biological systems self-organize and survive in their environment. This principle provides insights on multiple scales, from high-level behavioral and cognitive functions such as attention or foraging, down to the dynamics of specialized cortical microcircuits, suggesting that the FEP manifests on several levels of brain function. Here, we apply the FEP to one of the smallest functional units of the brain: single excitatory synaptic connections. By focusing on an experimentally well understood biological system we are able to derive learning rules from first principles while keeping assumptions minimal. This synapse-centric account of the FEP predicts that synapses interact with the soma of the post-synaptic neuron through stochastic synaptic releases to probe their behavior and use back-propagating action potentials as feedback to update the synaptic weights. The emergent learning rules are regulated triplet STDP rules that depend only on the timing of the pre- and post-synaptic spikes and the internal states of the synapse. The parameters of the learning rules are fully determined by the parameters of the post-synaptic neuron model, suggesting a close interplay between the synaptic and somatic compartment and making precise predictions about the synaptic dynamics. The synapse-level uncertainties automatically lead to representations of uncertainty on the network level that manifest in ambiguous situations. We show that the FEP learning rules can be applied to spiking neural networks for supervised and unsupervised learning and for a closed loop learning task where a behaving agent interacts with an environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
