Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, USA.
Volen Center for Complex Systems, Brandeis University, Waltham, MA, USA.
J Comput Neurosci. 2022 Nov;50(4):395-429. doi: 10.1007/s10827-022-00822-y. Epub 2022 Jul 23.
Temporal filters, the ability of postsynaptic neurons to preferentially select certain presynaptic input patterns over others, have been shown to be associated with the notion of information filtering and coding of sensory inputs. Short-term plasticity (depression and facilitation; STP) has been proposed to be an important player in the generation of temporal filters. We carry out a systematic modeling, analysis and computational study to understand how characteristic postsynaptic (low-, high- and band-pass) temporal filters are generated in response to periodic presynaptic spike trains in the presence STP. We investigate how the dynamic properties of these filters depend on the interplay of a hierarchy of processes, including the arrival of the presynaptic spikes, the activation of STP, its effect on the excitatory synaptic connection efficacy, and the response of the postsynaptic cell. These mechanisms involve the interplay of a collection of time scales that operate at the single-event level (roughly, during each presynaptic interspike-interval) and control the long-term development of the temporal filters over multiple presynaptic events. These time scales are generated at the levels of the presynaptic cell (captured by the presynaptic interspike-intervals), short-term depression and facilitation, synaptic dynamics and the post-synaptic cellular currents. We develop mathematical tools to link the single-event time scales with the time scales governing the long-term dynamics of the resulting temporal filters for a relatively simple model where depression and facilitation interact at the level of the synaptic efficacy change. We extend our results and tools to account for more complex models. These include multiple STP time scales and non-periodic presynaptic inputs. The results and ideas we develop have implications for the understanding of the generation of temporal filters in complex networks for which the simple feedforward network we investigate here is a building block.
时间滤波器，即突触后神经元优先选择某些突触前输入模式而不是其他模式的能力，与信息过滤和感觉输入编码的概念有关。短期可塑性（压抑和易化；STP）被认为是产生时间滤波器的重要因素。我们进行了系统的建模、分析和计算研究，以了解在存在短期可塑性的情况下，周期性突触前脉冲序列如何产生特征性的突触后（低通、高通和带通）时间滤波器。我们研究了这些滤波器的动态特性如何取决于一系列过程的相互作用，包括突触前脉冲的到达、短期可塑性的激活、其对兴奋性突触连接效能的影响以及突触后细胞的反应。这些机制涉及到一系列时间尺度的相互作用，这些时间尺度在单个事件水平上（大致在每个突触前脉冲间隔期间）运行，并控制多个突触前事件中时间滤波器的长期发展。这些时间尺度是在突触前细胞水平（由突触前脉冲间隔捕获）、短期压抑和易化、突触动力学和突触后细胞电流中产生的。我们开发了数学工具，将单个事件时间尺度与控制时间滤波器长期动力学的时间尺度联系起来，用于一个相对简单的模型，其中压抑和易化在突触效能变化的水平上相互作用。我们将我们的结果和工具扩展到更复杂的模型，包括多个短期可塑性时间尺度和非周期性的突触前输入。我们提出的结果和思路对于理解复杂网络中时间滤波器的产生具有重要意义，因为我们在这里研究的简单前馈网络是构建块。
