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PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Three approaches for data processing in neuroscience
DTSTART:20220301T143000
DTEND:20220301T163000
DTSTAMP:20260406T222306Z
UID:ed9e9fe33b4077cc1588dbf32316cfa8b354651f6b20150e8dbbcecc
CATEGORIES:Conferences - Seminars
DESCRIPTION:Andrea Giovannucci\, Daniel Sage\, Carsen Stringer\n \nKey co
 ncepts explored in FNIP-T3 event:\n\n- How to perform neuroimaging big-dat
 a analysis.\n- How to benchmark functional imaging methods.\n- Image segme
 ntation: which are the open challenges/solutions?\n- Which are the current
  bottlenecks in real-time data processing for closed-loop experimental app
 roaches?\n\nImage processing methods are a family of algorithms developed 
 to extract and analyze features of interest from digital images. Thanks to
  recent technological developments in the field of neuro-imaging\, we can 
 now record from tens of thousand neurons with subcellular precision at hig
 h speed for prolonged periods while the animal is engaged in some behavior
 al task.\nThe traditional manual data curation is therefore no longer affo
 rdable particularly in the context of closed-loop approaches in which the 
 neurophysiological readout must be available during the data acquisition s
 ession in order to guide for example optogenetic manipulations or behavior
 al interventions. Several excellent tools designed for automatic or semi-
 automatic data processing\, are available\, and in the context of this FNI
 P-T3 event we will discuss about 3 popular approaches with 3 experts in th
 e field.\n\nTogether we will learn about key features\, advantages and wea
 kness of each method\, with the aim to provide participants with useful in
 formation and start a discussion in the FNIP community\, promoting future 
 improvements on this critical topic.\n\nMore Information
LOCATION:https://epfl.zoom.us/webinar/register/WN_jaLD_KmlSLaeFjp16E8YtQ
STATUS:CONFIRMED
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