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.
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Acknowledgements We thank Teresa Nick for inspiring this line of research, Greg Horwitz for helpful discussions, and the anonymous reviewers for their suggestions. This research was supported by the National Science Foundation grant DMS-0719724 (DQN).
- Closed loop experiment
- Gradient ascent
- Mean firing rate maximization
- Reverse correlation
- Spike-triggered average