State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P.R. China.
Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, NY, USA.
PLoS Comput Biol. 2020 Oct 2;16(10):e1008209. doi: 10.1371/journal.pcbi.1008209. eCollection 2020 Oct.
Uncovering the underlying biophysical principles of emergent collective computational abilities, such as working memory, in neural circuits is one of the most essential concerns in modern neuroscience. Working memory system is often desired to be robust against noises. Such systems can be highly flexible for adapting environmental demands. How neural circuits reconfigure themselves according to the cognitive task requirement remains unclear. Previous studies explored the robustness and the flexibility in working memory by tracing individual dynamical trajectories in a limited time scale, where the accuracy of the results depends on the volume of the collected statistical data. Inspired by thermodynamics and statistical mechanics in physical systems, we developed a non-equilibrium landscape and flux framework for studying the neural network dynamics. Applying this approach to a biophysically based working memory model, we investigated how changes in the recurrent excitation mediated by slow NMDA receptors within a selective population and mutual inhibition mediated by GABAergic interneurons between populations affect the robustness against noises. This is realized through quantifying the underlying non-equilibrium potential landscape topography and the kinetics of state switching. We found that an optimal compromise for a working memory circuit between the robustness and the flexibility can be achieved through the emergence of an intermediate state between the working memory states. An optimal combination of both increased self-excitation and inhibition can enhance the flexibility to external signals without significantly reducing the robustness to the random fluctuations. Furthermore, we found that the enhanced performance in working memory is supported by larger energy consumption. Our approach can facilitate the design of new network structure for cognitive functions with the optimal balance between performance and cost. Our work also provides a new paradigm for exploring the underlying mechanisms of many cognitive functions based on non-equilibrium physics.
揭示新兴集体计算能力（如工作记忆）背后的基本生物物理原理是现代神经科学最关注的问题之一。工作记忆系统通常需要具有抗噪能力。这些系统可以高度灵活地适应环境需求。根据认知任务的要求，神经回路如何重新配置自己仍然不清楚。以前的研究通过在有限的时间尺度内追踪单个动力学轨迹来探索工作记忆的鲁棒性和灵活性，结果的准确性取决于收集的统计数据量。受物理系统热力学和统计力学的启发，我们为研究神经网络动力学开发了一个非平衡景观和通量框架。将这种方法应用于基于生物物理的工作记忆模型，我们研究了通过慢 NMDA 受体在选择性群体中介导的循环兴奋变化以及通过 GABA 能中间神经元在群体之间介导的相互抑制如何影响抗噪能力。这是通过量化潜在的非平衡势能景观地形和状态转换的动力学来实现的。我们发现，工作记忆电路在鲁棒性和灵活性之间可以通过工作记忆状态之间的中间状态实现最优折衷。自我兴奋和抑制的最佳组合可以增强对外部信号的灵活性，而不会显著降低对随机波动的鲁棒性。此外，我们发现工作记忆性能的提高得到了更大能量消耗的支持。我们的方法可以促进具有性能和成本最佳平衡的新网络结构的设计，用于认知功能。我们的工作还为探索许多基于非平衡物理的认知功能的基本机制提供了一个新范例。
