Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Poland.
Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.
J Neurophysiol. 2019 Oct 1;122(4):1473-1490. doi: 10.1152/jn.00092.2019. Epub 2019 Jul 31.
Dendritic spines, the carriers of long-term memory, occupy a small fraction of cortical space, and yet they are the major consumers of brain metabolic energy. What fraction of this energy goes for synaptic plasticity, correlated with learning and memory? It is estimated here based on neurophysiological and proteomic data for rat brain that, depending on the level of protein phosphorylation, the energy cost of synaptic plasticity constitutes a small fraction of the energy used for fast excitatory synaptic transmission, typically 4.0-11.2%. Next, this study analyzes a metabolic cost of new learning and its memory trace in relation to the cost of prior memories, using a class of cascade models of synaptic plasticity. It is argued that these models must contain bidirectional cyclic motifs, related to protein phosphorylation, to be compatible with basic thermodynamic principles. For most investigated parameters longer memories generally require proportionally more energy to store. The exceptions are the parameters controlling the speed of molecular transitions (e.g., ATP-driven phosphorylation rate), for which memory lifetime per invested energy can increase progressively for longer memories. Furthermore, in general, a memory trace decouples dynamically from a corresponding synaptic metabolic rate such that the energy expended on new learning and its memory trace constitutes in most cases only a small fraction of the baseline energy associated with prior memories. Taken together, these empirical and theoretical results suggest a metabolic efficiency of synaptically stored information. Learning and memory involve a sequence of molecular events in dendritic spines called synaptic plasticity. These events are physical in nature and require energy, which has to be supplied by ATP molecules. However, our knowledge of the energetics of these processes is very poor. This study estimates the empirical energy cost of synaptic plasticity and considers theoretically a metabolic rate of learning and its memory trace in a class of cascade models of synaptic plasticity.
树突棘作为长期记忆的载体，仅占据大脑皮质很小的一部分空间，但它们却是大脑代谢能量的主要消耗者。这些能量中有多少用于与学习和记忆相关的突触可塑性呢？本研究基于大鼠脑的神经生理学和蛋白质组学数据进行了估计，结果表明，根据蛋白质磷酸化的程度，突触可塑性的能量成本占快速兴奋性突触传递所消耗能量的一小部分，通常为 4.0-11.2%。接下来，本研究分析了新学习及其记忆痕迹的代谢成本与先前记忆的成本之间的关系，使用了一类突触可塑性级联模型。研究认为，这些模型必须包含与蛋白质磷酸化相关的双向循环模式，才能符合基本热力学原理。对于大多数研究参数，较长的记忆通常需要更多的能量来存储。例外的是控制分子跃迁速度的参数（例如，ATP 驱动的磷酸化速率），对于这些参数，每单位能量的记忆寿命可以随着记忆的延长而逐渐增加。此外，一般来说，记忆痕迹与相应的突触代谢率动态解耦，以至于新学习及其记忆痕迹所消耗的能量在大多数情况下仅占与先前记忆相关的基线能量的一小部分。总的来说，这些经验和理论结果表明突触存储信息具有代谢效率。学习和记忆涉及到树突棘中的一系列称为突触可塑性的分子事件。这些事件本质上是物理性质的，需要能量，而能量必须由 ATP 分子提供。然而，我们对这些过程的能量学知之甚少。本研究估计了突触可塑性的经验能量成本，并在一类突触可塑性级联模型中从理论上考虑了学习及其记忆痕迹的代谢率。
