TY - GEN
T1 - Maximally informative dimensions
T2 - 16th Annual Neural Information Processing Systems Conference, NIPS 2002
AU - Sharpee, Tatyana
AU - Rust, Nicole C.
AU - Bialek, William
PY - 2003
Y1 - 2003
N2 - We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli, which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of the high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Therefore we maximize the mutual information between the sequence of elicited neural responses and an ensemble of stimuli that has been projected on trial directions in the stimulus space. The procedure can be done iteratively by increasing the number of directions with respect to which information is maximized. Those directions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is much smaller than that of the overall stimulus space, it may become experimentally feasible to map out the neuron's input-output function even under fully natural stimulus conditions. This contrasts with methods based on correlations functions (reverse correlation, spike-triggered covariance,⋯) which all require simplified stimulus statistics if we are to use them rigorously.
AB - We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli, which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of the high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Therefore we maximize the mutual information between the sequence of elicited neural responses and an ensemble of stimuli that has been projected on trial directions in the stimulus space. The procedure can be done iteratively by increasing the number of directions with respect to which information is maximized. Those directions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is much smaller than that of the overall stimulus space, it may become experimentally feasible to map out the neuron's input-output function even under fully natural stimulus conditions. This contrasts with methods based on correlations functions (reverse correlation, spike-triggered covariance,⋯) which all require simplified stimulus statistics if we are to use them rigorously.
UR - http://www.scopus.com/inward/record.url?scp=84898987309&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898987309&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84898987309
SN - 0262025507
SN - 9780262025508
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PB - Neural information processing systems foundation
Y2 - 9 December 2002 through 14 December 2002
ER -