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树突棘早期长时程增强过程中的协同性、信息增益和能量成本。

Cooperativity, Information Gain, and Energy Cost During Early LTP in Dendritic Spines.

机构信息

Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw 02-097, Poland

College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences and Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland.

出版信息

Neural Comput. 2024 Jan 18;36(2):271-311. doi: 10.1162/neco_a_01632.

Abstract

We investigate a mutual relationship between information and energy during the early phase of LTP induction and maintenance in a large-scale system of mutually coupled dendritic spines, with discrete internal states and probabilistic dynamics, within the framework of nonequilibrium stochastic thermodynamics. In order to analyze this computationally intractable stochastic multidimensional system, we introduce a pair approximation, which allows us to reduce the spine dynamics into a lower-dimensional manageable system of closed equations. We found that the rates of information gain and energy attain their maximal values during an initial period of LTP (i.e., during stimulation), and after that, they recover to their baseline low values, as opposed to a memory trace that lasts much longer. This suggests that the learning phase is much more energy demanding than the memory phase. We show that positive correlations between neighboring spines increase both a duration of memory trace and energy cost during LTP, but the memory time per invested energy increases dramatically for very strong, positive synaptic cooperativity, suggesting a beneficial role of synaptic clustering on memory duration. In contrast, information gain after LTP is the largest for negative correlations, and energy efficiency of that information generally declines with increasing synaptic cooperativity. We also find that dendritic spines can use sparse representations for encoding long-term information, as both energetic and structural efficiencies of retained information and its lifetime exhibit maxima for low fractions of stimulated synapses during LTP. Moreover, we find that such efficiencies drop significantly with increasing the number of spines. In general, our stochastic thermodynamics approach provides a unifying framework for studying, from first principles, information encoding, and its energy cost during learning and memory in stochastic systems of interacting synapses.

摘要

我们在一个大规模的、相互耦合的树突棘的系统中,研究了信息和能量在长时程增强(LTP)诱导和维持的早期阶段之间的相互关系。这个系统具有离散的内部状态和概率动力学,我们在非平衡随机热力学的框架内研究了这个问题。为了分析这个计算上难以处理的随机多维系统,我们引入了一种配对近似,它可以将树突棘动力学简化为一个低维的、可管理的封闭方程组系统。我们发现,在 LTP 的初始阶段(即刺激期间),信息增益和能量获取的速率达到最大值,之后它们恢复到基线的低水平,而不像记忆痕迹那样持续更长时间。这表明学习阶段比记忆阶段需要更多的能量。我们表明,相邻树突棘之间的正相关性会增加记忆痕迹的持续时间和 LTP 期间的能量消耗,但对于非常强的正突触协同作用,记忆时间与投入的能量之比会急剧增加,这表明突触聚类对记忆持续时间有有益的作用。相比之下,LTP 后的信息增益在负相关时最大,并且随着突触协同作用的增加,信息的能量效率通常会下降。我们还发现,树突棘可以使用稀疏表示来编码长期信息,因为在 LTP 期间,保留信息及其寿命的能量和结构效率都在刺激突触的低分数时达到最大值。此外,我们发现,随着树突棘数量的增加,这些效率会显著下降。总的来说,我们的随机热力学方法提供了一个统一的框架,从第一性原理出发,研究了在随机的突触相互作用系统中,信息编码及其在学习和记忆过程中的能量成本。

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