Postsynaptic frequency filters shaped by the interplay of synaptic short-term plasticity and cellular time scales

Abstract

AbstractNeuronal filters can be thought of as constituent building blocks underlying the ability of neuronal systems to process information, generate rhythms and perform computations. How neuronal filters are generated by the concerted activity of a multiplicity of process and interacting time scales within and across levels of neuronal organization is poorly understood. In this paper we address these issues in a feedforward network in the presence of synaptic short-term plasticity (STP, depression and facilitation). The network consists of a presynaptic spike-train, a postsynaptic passive cell, and an excitatory (AMPA) chemical synapse. The dynamics of each network components is controlled by one or more time scales. We use mathematical modeling, numerical simulations and analytical approximations of the network response to presynaptic spike trains. We explain the mechanisms by which the participating time scales shape the neuronal filters at the (i) synaptic update level (the target of the synaptic variable in response to presynaptic spikes), which is shaped by STP, (ii) the synaptic variable, and (iii) the postsynaptic membrane potential. We focus on two metrics giving rise to two types of profiles (curves of the corresponding metrics as a function of the spike-train input frequency or firing rate): (i) peak profiles and (ii) peak-to-trough amplitude profiles. The effects of STP are present at the synaptic update level and are communicated to the synaptic level where they interact with the synaptic decay time. STP band-pass filters (BPFs) are reflected in the synaptic BPFs with some modifications due primarily to the synaptic decay time. The postsynaptic filters result from the interaction between the synaptic variable and the bio-physical properties of the postsynaptic cell. Postsynaptic BPFs can be inherited from the synaptic level or generated across levels of organization due to the interaction between (i) a synaptic low-pass filter and the postsynaptic summation filter (voltage peak BPF), and (ii) a synaptic high-pass filter and the postsynaptic summation filter (peak-to-trough amplitude BPF). These type of BPFs persist in response to jitter periodic spike trains and Poisson-distributed spike trains. The response variability depends on a number of factors including the spike train input frequency and are controlled by STP in a non-monotonic frequency manner. The lessons learned from the investigation of this relatively simple feedforward network will serve to construct a framework to analyze the mechanisms of generation of neuronal filters in networks with more complex architectures and a variety of interacting cellular, synaptic and plasticity time scales.