Probabilistic Inference of Short-Term Synaptic Plasticity in Neocortical Microcircuits

Abstract

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Short-term synaptic plasticity is highly diverse across brain area,cortical layer, cell type, and developmental stage. Since short-termplasticity shapes neural dynamics, this diversity suggests a specificand essential role in neural information processing. Therefore, acorrect characterization of short-term synaptic plasticity is an importantstep towards understanding and modeling neural systems. Phenomenologicalmodels have been developed, but they are usually fitted to experimentaldata using least-mean-square methods. We demonstrate that, for typicalsynaptic dynamics, such fitting may give unreliable results. As asolution, we introduce a Bayesian formulation, which yields the posteriordistribution over the model parameters given the data. First, we showthat common short-term plasticity protocols yield broad distributionsover some model parameters. Using our result we propose a experimentalprotocol to more accurately determine synaptic dynamics parameters.Next, we infer the model parameters using experimental data from threedifferent neocortical excitatory connection types. This reveals connection-specificdistributions, which we use to classify synaptic dynamics. Our approachto demarcate connection-specific synaptic dynamics is an importantimprovement on the state of the art and reveals novel features fromexisting data.

Keywords

• experimental design
• neocortical circuits
• parameter estimation
• Probabilistic inference
• short-term synaptic plasticity