A Phenomenological Synapse Model for Asynchronous Neurotransmitter Release

Functional Autapses Form in Striatal Parvalbumin Interneurons but not Medium Spiny Projection Neurons

Autapses selectively form in specific cell types in many brain regions. Previous studies have also found putative autapses in principal spiny projection neurons (SPNs) in the striatum. However, it remains unclear whether these neurons indeed form physiologically functional autapses. We applied whole-cell recording in striatal slices and identified autaptic cells by the occurrence of prolonged asynchronous release (AR) of neurotransmitters after bursts of high-frequency action potentials (APs). Surprisingly, we found no autaptic AR in SPNs, even in the presence of Sr2+. However, robust autaptic AR was recorded in parvalbumin (PV)-expressing neurons. The autaptic responses were mediated by GABAA receptors and their strength was dependent on AP frequency and number. Further computer simulations suggest that autapses regulate spiking activity in PV cells by providing self-inhibition and thus shape network oscillations. Together, our results indicate that PV neurons, but not SPNs, form functional autapses, which may play important roles in striatal functions.

Computational modeling of trans-synaptic nanocolumns, a modulator of synaptic transmission

Understanding synaptic transmission is of crucial importance in neuroscience. The spatial organization of receptors, vesicle release properties and neurotransmitter molecule diffusion can strongly influence features of synaptic currents. Newly discovered structures coined trans-synaptic nanocolumns were shown to align presynaptic vesicles release sites and postsynaptic receptors. However, how these structures, spanning a few tens of nanometers, shape synaptic signaling remains little understood. Given the difficulty to probe submicroscopic structures experimentally, computer modeling is a useful approach to investigate the possible functional impacts and role of nanocolumns. In our in silico model, as has been experimentally observed, a nanocolumn is characterized by a tight distribution of postsynaptic receptors aligned with the presynaptic vesicle release site and by the presence of trans-synaptic molecules which can modulate neurotransmitter molecule diffusion. In this work, we found that nanocolumns can play an important role in reinforcing synaptic current mostly when the presynaptic vesicle contains a small number of neurotransmitter molecules. Our work proposes a new methodology to investigate in silico how the existence of trans-synaptic nanocolumns, the nanometric organization of the synapse and the lateral diffusion of receptors shape the features of the synaptic current such as its amplitude and kinetics.

Identifiability of a Binomial Synapse

Synapses are highly stochastic transmission units. A classical model describing this stochastic transmission is called the binomial model, and its underlying parameters can be estimated from postsynaptic responses to evoked stimuli. The accuracy of parameter estimates obtained via such a model-based approach depends on the identifiability of the model. A model is said to be structurally identifiable if its parameters can be uniquely inferred from the distribution of its outputs. However, this theoretical property does not necessarily imply practical identifiability. For instance, if the number of observations is low or if the recording noise is high, the model's parameters can only be loosely estimated. Structural identifiability, which is an intrinsic property of a model, has been widely characterized; but practical identifiability, which is a property of both the model and the experimental protocol, is usually only qualitatively assessed. Here, we propose a formal definition for the practical identifiability domain of a statistical model. For a given experimental protocol, this domain corresponds to the set of parameters for which the model is correctly identified as the ground truth compared to a simpler alternative model. Considering a model selection problem instead of a parameter inference problem allows to derive a non-arbitrary criterion for practical identifiability. We apply our definition to the study of neurotransmitter release at a chemical synapse. Our contribution to the analysis of synaptic stochasticity is three-fold: firstly, we propose a quantitative criterion for the practical identifiability of a statistical model, and compute the identifiability domains of different variants of the binomial release model (uni or multi-quantal, with or without short-term plasticity); secondly, we extend the Bayesian Information Criterion (BIC), a classically used tool for model selection, to models with correlated data (which is the case for most models of chemical synapses); finally, we show that our approach allows to perform data free model selection, i.e., to verify if a model used to fit data was indeed identifiable even without access to the data, but having only access to the fitted parameters.