Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments.
Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments.
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DOI：
10.1371/journal.pcbi.1004167
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发表时间：
2015-03
影响因子：
4.3
通讯作者：
Stevenson IH
中科院分区：
文献类型：
--
作者：
Volgushev M;Ilin V;Stevenson IH
准确描述神经元之间的突触相互作用以及相互作用如何随时间变化是系统神经科学的关键挑战。虽然细胞内电生理学是研究突触整合和可塑性的有力工具，但它受到体外可同时记录的神经元数量少和体内细胞内记录技术难度的限制。解决这些困难的一种方法可能是使用大规模的细胞外记录的尖峰列车，并应用统计方法来建模和推断神经元之间的功能连接。这些技术有可能揭示大规模的连接结构的基础上的尖峰时间。然而，功能连接的解释往往是近似的，因为只有一小部分的突触前输入通常被观察到。在这里，我们使用体外电流注入层2/3锥体神经元，以验证方法推断功能连接的设置中，输入到神经元的控制。在部分定义的输入实验中，我们注入一个单一的模拟输入与已知的振幅波动噪声的背景。在一个完全定义的输入范例中，我们控制许多模拟突触前神经元的突触权重和时序。通过分析神经元对这些人工输入的反应，我们提出了以下问题：1）从尖峰信号推断出的功能连接性与模拟的突触输入有何关系？2）连通性推理的局限性是什么？我们发现，个人电流为基础的突触输入是可检测的幅度和条件的范围很广。可检测性取决于输入幅度和输出放电率，兴奋性输入比抑制性输入更容易被检测到。此外，随着我们对越来越多的突触前输入进行建模，我们能够更准确地估计连接强度，并更快地检测连接的存在。这些结果说明的可能性，并概述了从尖峰推断突触输入的限制。突触在神经信息处理中起着核心作用-以不同的方式对单个输入进行加权，使神经元能够执行一系列计算，而突触权重随时间的变化可以帮助神经元进行学习和从损伤中恢复。细胞内记录提供了单个突触的特性和动力学的最详细的视图，但是在自然行为期间同时研究许多突触是不可行的。相比之下，细胞外记录允许同时观察许多神经元，但它们的突触相互作用的细节必须单独从尖峰信号推断。通过对一个神经元的尖峰如何在统计上影响另一个神经元的尖峰进行建模，统计推断方法可以揭示神经元之间的“功能”连接。在这里，我们研究这些方法使用神经元尖峰诱发细胞内注射的一个定义的人工电流，模拟输入从一个单一的突触前神经元或一个大的突触前神经元的人口。我们研究如何以及功能连接方法能够重建模拟输入，并评估功能连接推理的有效性和局限性。我们发现，有了足够的数据，准确的推断往往是可能的，并可以变得更准确，因为更多的突触前输入被观察到。
Accurately describing synaptic interactions between neurons and how interactions change over time are key challenges for systems neuroscience. Although intracellular electrophysiology is a powerful tool for studying synaptic integration and plasticity, it is limited by the small number of neurons that can be recorded simultaneously in vitro and by the technical difficulty of intracellular recording in vivo. One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons. These techniques have the potential to reveal large-scale connectivity structure based on the spike timing alone. However, the interpretation of functional connectivity is often approximate, since only a small fraction of presynaptic inputs are typically observed. Here we use in vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled. In experiments with partially-defined input, we inject a single simulated input with known amplitude on a background of fluctuating noise. In a fully-defined input paradigm, we then control the synaptic weights and timing of many simulated presynaptic neurons. By analyzing the firing of neurons in response to these artificial inputs, we ask 1) How does functional connectivity inferred from spikes relate to simulated synaptic input? and 2) What are the limitations of connectivity inference? We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions. Detectability depends on input amplitude and output firing rate, and excitatory inputs are detected more readily than inhibitory. Moreover, as we model increasing numbers of presynaptic inputs, we are able to estimate connection strengths more accurately and detect the presence of connections more quickly. These results illustrate the possibilities and outline the limits of inferring synaptic input from spikes. Synapses play a central role in neural information processing – weighting individual inputs in different ways allows neurons to perform a range of computations, and the changing of synaptic weights over time allows learning and recovery from injury. Intracellular recordings provide the most detailed view of the properties and dynamics of individual synapses, but studying many synapses simultaneously during natural behavior is not feasible with current methods. In contrast, extracellular recordings allow many neurons to be observed simultaneously, but the details of their synaptic interactions have to be inferred from spiking alone. By modeling how spikes from one neuron, statistically, affect the spiking of another neuron, statistical inference methods can reveal “functional” connections between neurons. Here we examine these methods using neuronal spiking evoked by intracellular injection of a defined artificial current that simulates input from a single presynaptic neuron or a large population of presynaptic neurons. We study how well functional connectivity methods are able to reconstruct the simulated inputs, and assess the validity and limitations of functional connectivity inference. We find that, with a sufficient amount of data, accurate inference is often possible, and can become more accurate as more of the presynaptic inputs are observed.
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影响因子：
7.8
作者：
Ahrens, Misha B.;Paninski, Liam;Sahani, Maneesh
通讯作者：
Sahani, Maneesh
DOI：
10.1073/pnas.1211467109
发表时间：
2012-12-18
影响因子：
11.1
作者：
Chapeton, Julio;Fares, Tarec;Stepanyants, Armen
通讯作者：
Stepanyants, Armen
影响因子：
2.9
作者：
Brody, CD
通讯作者：
Brody, CD
影响因子：
2.9
作者：
Escola, Sean;Fontanini, Alfredo;Paninski, Liam
通讯作者：
Paninski, Liam
影响因子：
2.9
作者：
Eden, UT;Frank, LM;Brown, EN
通讯作者：
Brown, EN