Monosynaptic inference via finely-timed spikes.
Monosynaptic inference via finely-timed spikes.
复制标题
通过精细定时的尖峰进行单突触推理。
DOI：
10.1007/s10827-020-00770-5
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发表时间：
2021
影响因子：
1.2
通讯作者：
Amarasingham,Asohan
中科院分区：
文献类型：
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作者：
Platkiewicz,Jonathan;Saccomano,Zachary;McKenzie,Sam;English,Daniel;Amarasingham,Asohan
在人口记录精细定时尖峰关系的观察已被用来支持部分重建的神经微电路图。在这种方法中，成对的尖峰串相互作用的精细时标组件被隔离，随后归因于突触参数。最近的扰动研究加强了这种推断的情况下，但校准统计模型所需的一套完整的测量是不可用的。为了解决这一差距，我们研究了大规模活体数据集中成对尖峰的特征，其中突触前神经元通过神经元刺激与网络活动明确解耦。然后，我们构建生物物理模型的配对穗列车重现观察到的现象ofin vivomonosynaptic相互作用，包括细时标穗穗相关性和发射不规则性。这些模型的一个关键特征是成对的神经元通过快速波动的背景输入耦合。当单突触被移除时，我们通过比较突触后序列与其反事实序列来量化单突触的因果效应。随后，我们开发了统计技术来估计这种因果关系的影响，从突触前和突触后的尖峰列车。一个特别的焦点是时间尺度原则的非参数分离的理由和应用，以实现突触推理。使用从生物物理模型生成的模拟数据，我们表征的制度，其中的估计准确地识别单突触效应。第二个目标是在生物物理机制方面对神经统计学假设进行批判性探索，特别是关于背景动力学中快速，不可观测的非平稳性这一具有挑战性但可以说是根本性的问题。
Observations of finely-timed spike relationships in population recordings have been used to support partial reconstruction of neural microcircuit diagrams. In this approach, fine-timescale components of paired spike train interactions are isolated and subsequently attributed to synaptic parameters. Recent perturbation studies strengthen the case for such an inference, yet the complete set of measurements needed to calibrate statistical models is unavailable. To address this gap, we study features of pairwise spiking in a large-scalein vivodataset where presynaptic neurons were explicitly decoupled from network activity by juxtacellular stimulation. We then construct biophysical models of paired spike trains to reproduce the observed phenomenology ofin vivomonosynaptic interactions, including both fine-timescale spike-spike correlations and firing irregularity. A key characteristic of these models is that the paired neurons are coupled by rapidly-fluctuating background inputs. We quantify a monosynapse’s causal effect by comparing the postsynaptic train with its counterfactual, when the monosynapse is removed. Subsequently, we develop statistical techniques for estimating this causal effect from the pre- and post-synaptic spike trains. A particular focus is the justification and application of a nonparametric separation of timescale principle to implement synaptic inference. Using simulated data generated from the biophysical models, we characterize the regimes in which the estimators accurately identify the monosynaptic effect. A secondary goal is to initiate a critical exploration of neurostatistical assumptions in terms of biophysical mechanisms, particularly with regards to the challenging but arguably fundamental issue of fast, unobservable nonstationarities in background dynamics.