[Statlist] Research Webinar in Statistics *FRIDAY 1ST APRIL 2022* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

We are pleased to invite you to our next Research Webinar.

Looking forward to seeing you


Organized by Prof. Sebastian Engelke on behalf of the Research Center for Statistics (https://www.unige.ch/gsem/en/research/institutes/rcs/)


FRIDAY 1ST APRIL 2022 at 11:15am
ONLINE
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192


>From Single Neuron to Networks: the Integrate and Fire Paradigm
Laura Lea Sacerdote, Università degli studi di Torino, Italy
https://www.dipmatematica.unito.it/do/docenti.pl/Alias?laura.sacerdote#tab-profilo

ABSTRACT:
Single neuron models aim to describe the information transmission within a neural network. In some instances, the introduction of strong simplifications allows to attain mathematical tractability, ignoring many involved biophysical features of the single units. In other cases, models are very faithful to reality at the price of very complex mathematical descriptions. Integrate and fire models are often recognized as a good compromise between biological meaningfulness and mathematical tractability. Furthermore, their paradigm is often used in different contexts.

Up to recent years models focused mainly on single neuron dynamics.  Nowadays, the improvement of experimental techniques allows the collection of simultaneous recording from large groups of neurons and the attention of scientists is directed toward networks features. Simulations show the appearance of particular patterns in the trains of spikes. In this framework, it is essential to guarantee the reliability of the output of the model and often scientists compare the output of the models with real data.

However, in a network the output of groups of neurons becomes the input of a successive layer of neurons. This fact opens a problem of consistency between input and output of layers of neurons. To the best of our knowledge, this problem has not yet been deeply investigated. Here, we consider the simplest Stochastic Integrate and Fire network and we characterize the features of its input in such a way to re-obtain the same features in the output.

In particular, we focus on the tail properties of ISIs distribution. Observed data suggest the presence of heavy tails for this distribution. Using the Stochastic Integrate and Fire paradigm for the neurons of the network we study how such feature can be transmitted across the network.


Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/