Abstract
Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.
Highlights
Synaptic plasticity is thought to underlie learning and information processing in the brain
We tested whether the Gaussian noise model was a valid description of the data using the Kolmogorov– Smirnov (KS) normality test, and we found that the null hypothesis that samples were drawn from a normal distribution could not be rejected for 160 out of 170 Excitatory postsynaptic potentials (EPSPs) distributions, with no connection-specific bias
We estimated the performance of our classifier with K-cross validation (K = 7, i.e., ∼80% for pyramidal cells (PCs)–PC (n = 9) and PC–Martinotti cells (MCs) (n = 9), and ∼60% for PC–Basket cells (BCs) (n = 12)), where we sampled over K data points for each synapse-type to obtain our likelihood model and test the classifier with the remaining data points
Summary
Synaptic plasticity is thought to underlie learning and information processing in the brain. Short-term plasticity (STP) refers to transient changes in synaptic efficacy, in the range of tens of milliseconds to several seconds or even minutes (Zucker and Regehr, 2002) It is highly heterogeneous and is correlated with developmental stage (Reyes and Sakmann, 1999), cortical layer (Reyes and Sakmann, 1999), brain area (Wang et al, 2006; Cheetham and Fox, 2010), and postsynaptic cell-type (Markram et al, 1998; Reyes et al, 1998; Scanziani et al, 1998; Tóth and McBain, 2000; Rozov et al, 2001; Sun and Dobrunz, 2006). We performed model selection to determine which variant of the TM model best captures the synaptic dynamics of the connection type at hand
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
