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SUMMARY:Biologically realistic mean-field models of neuronal populations -
  Alain Destexhe\, Paris-Saclay Institute of Neuroscience (NeuroPSI)\, CNRS
 \, Gif sur Yvette\, France.
DTSTART:20191128T160000Z
DTEND:20191128T170000Z
UID:TALK135184@talks.cam.ac.uk
CONTACT:Alberto Padoan
DESCRIPTION:Brain activity can be measured at different scales\, from sing
 le-neuron activity (micro-electrode recordings)\, mesoscopic scales (local
  field potentials\, calcium imaging\, voltage-sensitive dyes)\, up to larg
 e scales (EEG\, MEG\, …)  We investigate how to link those scales using 
 mean-field models.  Networks of spiking neurons can address the level of s
 ingle-neuron activity as seen from microelectrode arrays. To link to large
 r scales\, we use mean-field techniques to derive population models from n
 etworks of spiking neurons.  We show that mean-field techniques can be app
 lied to complex neuron models\, such as non-linear integrate-and-fire mode
 ls\, Hodgkin-Huxley type models or even real neurons recorded in slices.  
 Such mean-field models capture the level of spontaneous activity exhibited
  by the network\, as well as the response to external stimuli. Mean-field 
 models can also capture the state-dependent responsiveness of networks\, d
 epending on their level of spontaneous activity\, as well as slow oscillat
 ory phenomena such as Up/Down state oscillations. Finally\, we show that t
 hese models can be adjusted to mesoscopic measurements\, such as the trave
 ling waves measured by voltage-sensitive dyes in awake monkey visual corte
 x.  We conclude that\, for the first time\, we can obtain biologically-rea
 listic population models that are derived from the activity of single neur
 ons\, and that can be adjusted to experimental measurements.  We discuss t
 he perspective of using such models to study large-scale interactions at t
 he level of the whole brain. \n\nSupported by the CNRS and the EU (Human B
 rain Project).
LOCATION: Cambridge University Engineering Department\,  Lecture Room 6
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