Department of Physics, New York Institute of Technology, New York, New York 10023, USA and Department of Astrophysics, American Museum of Natural History, New York, New York 10024, USA.
Phys Rev E. 2020 Jan;101(1-1):012415. doi: 10.1103/PhysRevE.101.012415.
A method of data assimilation (DA) is employed to estimate electrophysiological parameters of neurons simultaneously with their synaptic connectivity in a small model biological network. The DA procedure is cast as an optimization, with a cost function consisting of both a measurement error and a model error term. An iterative reweighting of these terms permits a systematic method to identify the lowest minimum, within a local region of state space, on the surface of a nonconvex cost function. In the model, two sets of parameter values are associated with two particular functional modes of network activity: simultaneous firing of all neurons and a pattern-generating mode wherein the neurons burst in sequence. The DA procedure is able to recover these modes if: (i) the stimulating electrical currents have chaotic waveforms and (ii) the measurements consist of the membrane voltages of all neurons in the circuit. Further, this method is able to prune a model of unnecessarily high dimensionality to a representation that contains the maximum dimensionality required to reproduce the provided measurements. This paper offers a proof-of-concept that DA has the potential to inform laboratory designs for estimating properties in small and isolatable functional circuits.
一种数据同化 (DA) 方法被用于同时估计小型生物网络中神经元的电生理参数及其突触连接。DA 过程被表示为一个优化问题，其代价函数由测量误差和模型误差项组成。通过迭代重新加权这些项，可以在非凸代价函数的表面上，在状态空间的局部区域内系统地找到最低的最小值。在模型中，两组参数值与网络活动的两种特定功能模式相关联：所有神经元的同时发射和神经元按顺序爆发的模式生成。如果满足以下条件，DA 过程能够恢复这些模式：（i）刺激电流具有混沌波形，（ii）测量结果由电路中所有神经元的膜电压组成。此外，该方法能够将维度过高的模型修剪到包含再现所提供测量结果所需的最大维度的表示形式。本文提供了一个概念验证，证明 DA 有可能为估计小型和可隔离功能电路中的特性提供实验室设计信息。
