Dorval Alan D, White John A
Department of Biomedical Engineering, Center for BioDynamics, Center for Memory and Brain, Boston University, Boston, Massachusetts 02215, USA.
Chaos. 2006 Jun;16(2):026105. doi: 10.1063/1.2209427.
Synaptic waveforms, constructed from excitatory and inhibitory presynaptic Poisson trains, are presented to living and computational neurons. We review how the average output of a neuron (e.g., the firing rate) is set by the difference between excitatory and inhibitory event rates while neuronal variability is set by their sum. We distinguish neuronal variability from reproducibility. Variability quantifies how much an output measure is expected to vary; for example, the interspike interval coefficient of variation quantifies the typical range of interspike intervals. Reproducibility quantifies the similarity of neuronal outputs in response to repeated presentations of identical stimuli. Although variability and reproducibility are conceptually distinct, we show that, for ideal current source synapses, reproducibility is defined entirely by variability. For physiologically realistic conductance-based synapses, however, reproducibility is distinct from variability and average output, set by the Poisson rate and the degree of synchrony within the synaptic waveform.
由兴奋性和抑制性突触前泊松序列构建的突触波形被呈现给活体神经元和计算神经元。我们回顾了神经元的平均输出（例如放电率）是如何由兴奋性和抑制性事件发生率之间的差异设定的，而神经元变异性则由它们的总和设定。我们区分了神经元变异性和可重复性。变异性量化了输出测量预期变化的程度；例如，峰间间隔变异系数量化了峰间间隔的典型范围。可重复性量化了神经元输出在相同刺激重复呈现时的相似性。虽然变异性和可重复性在概念上是不同的，但我们表明，对于理想的电流源突触，可重复性完全由变异性定义。然而，对于基于生理现实的电导突触，可重复性与变异性和平均输出不同，它由泊松率和突触波形内的同步程度设定。
