AUTHOR=Costa Rui P., Sjostrom P. Jesper , van Rossum Mark C.

TITLE=Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 7 - 2013

YEAR=2013

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2013.00075

DOI=10.3389/fncom.2013.00075

ISSN=1662-5188

ABSTRACT=Short-term synaptic plasticity is highly diverse across brain area,
cortical layer, cell type, and developmental stage. Since short-term
plasticity 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 short-term plasticity 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.