Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.
Front Syst Neurosci. 2011 Oct 11;5:80. doi: 10.3389/fnsys.2011.00080. eCollection 2011.
In this paper, we pursue recent observations that, through selective dendritic filtering, single neurons respond to specific sequences of presynaptic inputs. We try to provide a principled and mechanistic account of this selectivity by applying a recent free-energy principle to a dendrite that is immersed in its neuropil or environment. We assume that neurons self-organize to minimize a variational free-energy bound on the self-information or surprise of presynaptic inputs that are sampled. We model this as a selective pruning of dendritic spines that are expressed on a dendritic branch. This pruning occurs when postsynaptic gain falls below a threshold. Crucially, postsynaptic gain is itself optimized with respect to free energy. Pruning suppresses free energy as the dendrite selects presynaptic signals that conform to its expectations, specified by a generative model implicit in its intracellular kinetics. Not only does this provide a principled account of how neurons organize and selectively sample the myriad of potential presynaptic inputs they are exposed to, but it also connects the optimization of elemental neuronal (dendritic) processing to generic (surprise or evidence-based) schemes in statistics and machine learning, such as Bayesian model selection and automatic relevance determination.
在本文中，我们关注最近的观察结果，即通过选择性树突过滤，单个神经元对特定的突触前输入序列做出反应。我们尝试通过将最近的自由能原理应用于沉浸在神经突或环境中的树突，为这种选择性提供一个有原则和机械的解释。我们假设神经元自我组织，以最小化采样的突触前输入的自信息或惊讶的变分自由能约束。我们将其建模为表达在树突分支上的树突棘的选择性修剪。当突触后增益低于阈值时，就会发生这种修剪。至关重要的是，突触后增益本身是针对自由能进行优化的。当树突选择符合其预期的突触前信号时，修剪会抑制自由能，其预期由其细胞内动力学中隐含的生成模型指定。这不仅为神经元如何组织和选择性地采样它们所暴露的无数潜在突触前输入提供了一个有原则的解释，而且还将基本神经元（树突）处理的优化与统计学和机器学习中的通用（惊讶或基于证据的）方案联系起来，例如贝叶斯模型选择和自动相关性确定。
