Short-Term Synaptic Plasticity: Microscopic Modelling and (Some) Computational Implications.

Synaptic transmission is transiently adjusted on a spike-by-spike basis, with the adjustments persisting from hundreds of milliseconds up to seconds. Such a short-term plasticity has been suggested to significantly augment the computational capabilities of neuronal networks by enhancing their dynamical repertoire. In this chapter, after reviewing the basic physiology of chemical synaptic transmission, we present a general framework-inspired by the quantal model-to build simple, yet quantitatively accurate models of repetitive synaptic transmission. We also discuss different methods to obtain estimates of the model's parameters from experimental recordings. Next, we show that, indeed, new dynamical regimes appear in the presence of short-term synaptic plasticity. In particular, model neuronal networks exhibit the co-existence of a stable fixed point and a stable limit cycle in the presence of short-term synaptic facilitation. It has been suggested that this dynamical regime is especially relevant in working memory processes. We provide, then, a short summary of the synaptic theory of working memory and discuss some of its specific predictions in the context of experiments. We conclude the chapter with a short outlook.

Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach.

The dependence of the synaptic responses on the history of activation and their large variability are both distinctive features of repetitive transmission at chemical synapses. Quantitative investigations have mostly focused on trial-averaged responses to characterize dynamic aspects of the transmission--thus disregarding variability--or on the fluctuations of the responses in steady conditions to characterize variability--thus disregarding dynamics. We present a statistically principled framework to quantify the dynamics of the probability distribution of synaptic responses under arbitrary patterns of activation. This is achieved by constructing a generative model of repetitive transmission, which includes an explicit description of the sources of stochasticity present in the process. The underlying parameters are then selected via an expectation-maximization algorithm that is exact for a large class of models of synaptic transmission, so as to maximize the likelihood of the observed responses. The method exploits the information contained in the correlation between responses to produce highly accurate estimates of both quantal and dynamic parameters from the same recordings. The method also provides important conceptual and technical advances over existing state-of-the-art techniques. In particular, the repetition of the same stimulation in identical conditions becomes unnecessary. This paves the way to the design of optimal protocols to estimate synaptic parameters, to the quantitative comparison of synaptic models over benchmark datasets, and, most importantly, to the study of repetitive transmission under physiologically relevant patterns of synaptic activation.