A tutorial on the free-energy framework for modelling perception and learning.
A tutorial on the free-energy framework for modelling perception and learning.
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DOI：
10.1016/j.jmp.2015.11.003
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发表时间：
2017-02
影响因子：
1.8
通讯作者：
Bogacz R
中科院分区：
文献类型：
--
作者：
Bogacz R
本文提供了一个易于理解的教程，介绍了由Friston开发的用于建模感知的自由能框架，该框架扩展了Rao和Ballard的预测编码模型。这些模型假设感觉皮层从编码刺激的噪声输入中推断出最可能的感官刺激属性或特征值。值得注意的是，这些模型描述了这种推理是如何在一个非常简单的计算元素的网络中实现的，这表明这种推理可以由神经元的生物网络来执行。此外，这些模型通过基于Hebbian学习的突触可塑性的简单规则来实现描述特征及其不确定性的参数学习。本教程使用非常简单的示例介绍了自由能框架，并提供了该模型的逐步推导。它还详细讨论了如何在生物神经回路中实现该模型。特别是，它提出了一个扩展版本的模型，其中神经元仅求和其输入，突触可塑性仅取决于突触前和突触后神经元的活动。关于刺激性质的贝叶斯推理可以通过神经元网络来完成。学习刺激的统计数据可以通过Hebbian突触可塑性实现。该模型的结构类似于新皮层的分层组织。
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. Bayesian inference about stimulus properties can be performed by networks of neurons. Learning about statistics of stimuli can be achieved by Hebbian synaptic plasticity. Structure of the model resembles the hierarchical organization of the neocortex.