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Seminario del 2017
2017
17 febbraio
Along the last years the technological advancements have been fundamental to improve the
recording capability from brain areas and neural populations. For example multi-site
recordings can be achieved from thousands of channels (sites) with a good spatial and
temporal resolution yielding a good description of the underlying network dynamics. Given
that, the brain operates on a single trial basis such recordings are becoming important to
understand the neural code. As a first step, multi-site recordings allow to quantify the
information flow in the network. The anatomical wiring (i.e. Structural Connectivity, SC)
clearly plays a fundamental role to understand how cells communicate among them but it is
often not well known neither it can by itself explain the overall network activity. Multi-site
recordings can be used to infer statistical dependencies (i.e. Functional Connections, FC)
among the recorded units and to track the information flow in the network. On the other
hand the Effective Connectivity (EC) denotes the directed causal relationship between the
recorded sites. Experimentally, the EC is typically estimated by stimulating one cell and
studying the effects on the connected elements. Alternatively the EC can also be studied by
using a causal mathematical model between the recorded units data. Importantly, multi-site
recordings raise some limitations that need to be evaluated carefully before any further
analysis. First, the experimental sessions are often limited in time. Second, the high
dimensional data sets involve a set of numerical and mathematical problems that would be
hard to face even with long enough recording sessions. These issues are common to
different fields and have been coined as “curse of dimensionality”. In order to capture
nonlinear interactions between even short and noisy time series, we consider an event-
based model. Then, we involve the physiological basis of the signal, which is likely to be
mainly nonlinear. Specifically, we suppose that we are able to observe the dynamical
behaviours of individual components of a neuronal networks and that few of the components
may be causally influencing each other. The variables could be time series from different
parts of the brain. In order to introduce our method we have considered a simulated
cerebellar granule cell network capturing nonlinear interactions between even short and
noisy time series. Although the proposed EC algorithm cannot be applied straightforwardly
to the experimental data, our preliminary results are quite promising.
This is a joint work with G. Aletti, T. Nieus, and M. Moroni.