BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Statistical models of effective connectivity in neural microcircui
 ts
DTSTART:20140221T093000
DTSTAMP:20260414T213322Z
UID:4b44427db3d2f667171db270a4c04ff59ec53d7376831060aa5bf20a
CATEGORIES:Thesis defenses
DESCRIPTION:Joao Emanuel Felipe GERHARD\nThesis director : Prof. W. Gerstn
 er\nNeuroscience doctoral programAbstract\nTo appreciate how neural circui
 ts in the brain control behaviors\, we must identify how the neurons compr
 ising the circuit are connected. Neuronal connectivity is difficult to det
 ermine experimentally\, whereas neuronal activity can often be readily mea
 sured. I describe a statistical framework to estimate circuit connectivity
  directly from measured activity patterns. Because we usually only have ac
 cess to a small subset of neurons of a circuit\, the estimated connectivit
 y reflects an effective\ncoupling\, that is\, how spiking activity in one 
 neuron effectively modulates the activity of other neurons.\nFor small cir
 cuits\, like the nervous system of the crab that controls gut muscle activ
 ity\, we could show that it is possible to derive the actual physiological
  connectivity from observing neural activity alone. This was achieved with
  a regression model adapted to the spike train structure of the data (Gene
 ralized Linear Model\, GLM). This is the first successful demonstration of
  a network inference algorithm on a physiological circuit for which the co
 nnections are known.\nFor larger networks\, like cortical networks\, the c
 oncept of effective connectivity - though not equivalent to structural con
 nectivity - is useful to characterize the functional properties of the net
 work. For example\, we may assess whether networks have small-world or sca
 le-free properties that are important for information processing. We find 
 that cortical networks show a small\, but significant small-world structur
 e by applying our estimation framework on multi-electrode recordings from 
 the\nvisual system of the awake monkey.\nFinally\, we study how well spike
  dynamics and network topology can be inferred from noisy calcium imaging 
 data. We applied our framework on simulated data to explore how uncertaint
 ies in spike inference due to experimental parameters affect estimates of 
 network connectivity and their topological features. We find that consider
 able information about the connectivity can be extracted from the neural a
 ctivity\, but only if spikes are reconstructed with high temporal precisio
 n. We then study how errors in the network reconstruction affect the estim
 ation of a number of graph-theoretic measures. Our findings provide a benc
 hmark for future\nexperiments that aim to reliably infer neuronal network 
 properties.
LOCATION:Auditoire ELE 111 http://plan.epfl.ch/?lang=en&room=ELE+111
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR