Synaptic computation is believed to underlie many forms of animal behavior. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.

Depression is believed to hinder one’s ability to reason about oneself (metacognition). This impairment can arise from dysfunctional biases and/or learning processes. However, the relationship between depression, biases and learning in metacognition is not known. Here we combined multi-trial behavioural experiments with computa- tional modelling to explicitly test whether depression impacts biases and/or learning in a metacognitive task. First, using a perceptual estimation task with fixed feedback valence (N=131), we show that depressive symptoms predict negative metacognitive biases but do not impact learning. Next, we tested the generality of our results in a more realistic perceptual estimation task where we varied the valence of the feedback. Using a Rescorla-Wagner model of confidence fitted to behavioural data (N=74), we show that also in this task, depressive symptoms predict negative metacognitive biases but do not impair learning. Overall, our study suggests that depression impacts metacognitive states but not one’s ability to learn while offering a behavioural-computational framework for the study of metacog- nition in depression.