TY - JOUR
T1 - Searching for optimal stimuli
T2 - Ascending a neuron's response function
AU - Koelling, Melinda Evrithiki
AU - Nykamp, Duane Q.
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. However, some neurons are strongly sensitive to multiple directions in stimulus space and have a highly nonlinear response. It can be difficult to find optimal stimuli for these neurons. We demonstrate how successive linear approximations of neuronal response can effectively carry out gradient ascent and move through stimulus space towards local maxima of the response. We demonstrate search results for a simple model neuron and two models of a highly selective neuron.
AB - Many methods used to analyze neuronal response assume that neuronal activity has a fundamentally linear relationship to the stimulus. However, some neurons are strongly sensitive to multiple directions in stimulus space and have a highly nonlinear response. It can be difficult to find optimal stimuli for these neurons. We demonstrate how successive linear approximations of neuronal response can effectively carry out gradient ascent and move through stimulus space towards local maxima of the response. We demonstrate search results for a simple model neuron and two models of a highly selective neuron.
KW - Closed loop experiment
KW - Gradient ascent
KW - Mean firing rate maximization
KW - Reverse correlation
KW - Spike-triggered average
UR - http://www.scopus.com/inward/record.url?scp=84874108909&partnerID=8YFLogxK
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U2 - 10.1007/s10827-012-0395-7
DO - 10.1007/s10827-012-0395-7
M3 - Article
C2 - 22580579
AN - SCOPUS:84874108909
VL - 33
SP - 449
EP - 473
JO - Journal of Computational Neuroscience
JF - Journal of Computational Neuroscience
SN - 0929-5313
IS - 3
ER -