Extraction of Synaptic Input Properties in Vivo

Abstract

Knowledge of synaptic input is crucial to understand synaptic integration and ultimately neural function. However, in vivo the rates at which synaptic inputs arrive are high, that it is typically impossible to detect single events. We show here that it is nevertheless possible to extract the properties of the events, and particular to extract the event rate, the synaptic time-constants, and the properties of the event size distribution from in vivo voltage-clamp recordings. Applied to cerebellar interneurons our method reveals that the synaptic input rate increases from 600Hz during rest to 1000Hz during locomotion, while the amplitude and shape of the synaptic events are unaffected by this state change. This method thus complements existing methods to measure neural function in vivo. Significance StatementNeurons in vivo typically receive thousands of synaptic events per second. While methods have been developed to measure the total synaptic current that results from these events, extraction of the constituent events has proven very difficult given their high degree of overlap. To resolve this, we introduce a probabilistic method that extracts the statistics of synaptic event amplitudes and their frequency from voltage clamp recordings, which is then applied to recordings from cerebellar interneurons. With this method it becomes possible to better understand synaptic input and how it changes with behavioral state.

Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset

“…There exist several useful algorithms for estimating properties of synaptic activity from membrane potential recordings (Bédard et al 2012;Berg and Ditlevsen 2013;Borg-Graham et al 1998;Lankarany et al 2013;Paninski et al 2012;Puggioni et al 2017;Rudolph and Destexhe 2003;Wehr and Zador 2003). The analysis performed here requires knowledge of the synaptic conductance time series on individual trials, which a few cleverly crafted algorithms can provide (Bédard et al 2012;Berg and Ditlevsen 2013;Lankarany et al 2013;Paninski et al 2012).…”

Model-Based Inference of Synaptic Transmission

“…For example, Costa et al (2015) applied binomial-based estimation methods typically used in slices to in vivo data, and obtained results consistent with both modeling predictions and slice data. Puggioni et al (2017) and Latimer et al (2018) introduced new statistical methods with some success in inferring synaptic conductances from in vivo intracellular recordings and spike trains, respectively. However, these methods were not developed to estimate quantal or synaptic dynamics properties.…”