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SUMMARY:LCN Seminar: Unlocking the neural code in neurons and neural popul
 ations using advanced statistical models and methods
DTSTART:20140109T134500
DTEND:20140109T144500
DTSTAMP:20260510T135051Z
UID:f4f619cbb6a6d110e638b3804334e56b118080541f246ce63c5a377f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Jonathan PILLOW\; Psychology & Neurobiology\, The University o
 f Texas at Austin\nA major goal of computational neuroscience is to unders
 tand how neurons work together to build representations of sensory\, motor
 \, and cognitive variables necessary for behavior. A popular approach to t
 his problem is to develop statistical models of the relationship between n
 eural activity and relevant external variables (e.g.\, sensory stimuli or 
 motor output). \nIn this talk\, I will describe several projects aimed at
  unlocking the neural code in different brain areas at several different l
 evels of biophysical detail. First\, I will describe recent work on charac
 terizing the representation of sensory-motor decisions by neural populatio
 ns in the lateral intra-parietal cortex (area LIP) in primates. Second\, I
  will describe new methods for rapidly characterizing high-dimensional res
 ponse properties of single neurons in visual cortex using closed-loop "ada
 ptive" experimental design. Third\, I will present a new model-based techn
 ique for estimating intracellular excitatory and inhibitory synaptic condu
 ctances from extracellular spike trains recorded in primate retina. \nI w
 ill discuss the implications of these findings for understanding neural co
 des and the mechanisms by which they are constructed\, and the promise of 
 such statistical approaches for gaining insights into neural datasets of i
 ncreasing size and complexity.
LOCATION:SG 0213
STATUS:CONFIRMED
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