This paper provides an easy to follow tutorial on the free-energy framework for modelling perception developed by Friston, which extends the predictive coding model of Rao and Ballard. These models assume that the sensory cortex infers the most likely values of attributes or features of sensory stimuli from the noisy inputs encoding the stimuli. Remarkably, these models describe how this inference could be implemented in a network of very simple computational elements, suggesting that this inference could be performed by biological networks of neurons. Furthermore, learning about the parameters describing the features and their uncertainty is implemented in these models by simple rules of synaptic plasticity based on Hebbian learning. This tutorial introduces the free-energy framework using very simple examples, and provides step-by-step derivations of the model. It also discusses in more detail how the model could be implemented in biological neural circuits. In particular, it presents an extended version of the model in which the neurons only sum their inputs, and synaptic plasticity only depends on activity of pre-synaptic and post-synaptic neurons.

中文翻译：

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用于建模感知和学习的自由能框架教程

本文提供了一个易于遵循的教程，介绍了由 Friston 开发的用于建模感知的自由能框架，它扩展了 Rao 和 Ballard 的预测编码模型。这些模型假设感觉皮层从对刺激进行编码的嘈杂输入中推断出感觉刺激的属性或特征的最可能值。值得注意的是，这些模型描述了如何在非常简单的计算元素的网络中实现这种推理，表明这种推理可以由神经元的生物网络执行。此外，通过基于 Hebbian 学习的简单突触可塑性规则，在这些模型中实现了对描述特征的参数及其不确定性的学习。本教程使用非常简单的示例介绍了自由能框架，并提供模型的逐步推导。它还更详细地讨论了如何在生物神经回路中实现该模型。特别是，它提出了模型的扩展版本，其中神经元仅对它们的输入求和，突触可塑性仅取决于突触前和突触后神经元的活动。