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Workshop: Guido Sanguinetti and Rui P. Costa, Chair: David Sterratt

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What
  • ANC Workshop Talk
When May 28, 2013
from 11:00 AM to 12:00 PM
Where IF 4.31/4.33
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Guido Sanguinetti

Learning stochastic processes from logical constraints

We consider the problem of calibrating a stochastic process (i.e. learning its unknown parameters) from qualitative observations of the system. More specifically, we suppose that we have access to data detailing the satisfaction of specific properties (formulated as logical formulae on the state of the system) on a number of independent runs of the model. This situation could arise e.g. in computer systems, where error messages are supplied, or in biology, where qualitative data about the presence/ absence of a phenotype is available. We estimate the likelihood for a number of values of the parameters using Monte Carlo techniques, and we then use the GP-UCB algorithm to optimize the intractable likelihood. Joint work with Luca Bortolussi, University of Trieste.


Rui P. Costa

Probabilistic Inference of Short-Term Synaptic Plasticity in Neocortical Microcircuits

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.