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SUMMARY:BMI Seminar // Learning and memory in hippocampus and cortex\, a t
 ale of two theories
DTSTART:20181010T121500
DTEND:20181010T131500
DTSTAMP:20260510T082028Z
UID:26fd4a2278374f0ad1498cd0e733f9ea3bfea63a4f06c8ef098eb2f8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Sandro Romani\, Romani Lab\, HHMI Janelia\, Ashburn\, Virginia
 \, USA\nLearning is thought to be mediated by changes of synaptic weights.
  Many physiology-based theories posit that synaptic modifications are indu
 ced by repeated and almost coincident spiking of pre- and post-synaptic ne
 urons. At the level of behavior\, learning can occur without repetitions a
 nd can link events that are separated in time by seconds. The hippocampus 
 has been implicated in these forms of learning.  Analysis and modeling of
  in-vivo recordings from region CA1 of rodents hippocampus\, validated wit
 h in-vitro manipulations\, reveal a novel learning rule: pre-synaptic spik
 ing and post-synaptic complex spiking can be separated by a time-scale of 
 seconds while still inducing potent (one-shot) changes in synaptic weights
 . This novel plasticity rule offers an immediate connection between behavi
 or and synaptic changes in the hippocampus. \nCortex on the other hand is
  thought to learn by slowly shaping circuit dynamics to subserve complex b
 ehaviors. For instance\, certain behaviors require short-term memory\, the
  ability to maintain information in memory\, in the absence of cues\, for 
 a time scale of seconds. The classic neural correlate of short-term memory
  is persistent selective activity\, elevated neuronal firing that persists
  during memory maintenance\, shows large and systematic ramping\, and depe
 nds on the particular item kept in memory. Several hypotheses have been pr
 oposed to explain how neural circuits can support short-term memory. We us
 e theory-driven optogenetic perturbations of pre-motor cortex in rodents p
 erforming a delayed binary response task. Circuit dynamics following recov
 ery from perturbations reveal the presence of discrete attractors. We furt
 her devise a task structure to eliminate ramping activity\, obtaining stat
 ionary activity patterns as observed in standard attractor network models.
 \n 
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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