title: Methods for first-order kernel estimation: simple-cell receptive fields from responses to natural scenes.
abstract: Recent studies have recovered receptive-field maps of simple cells in visual cortex from their responses to natural scene stimuli. Natural scenes have many theoretical and practical advantages over traditional, artificial stimuli; however, the receptive-field estimation methods are more complex than for white-noise stimuli. Here, we describe and justify several of these methods-spectral correction of the reverse correlation estimate, direct least-squares solution, iterative least-squares algorithms and regularized least-squares solutions. We investigate the pros and cons of the different methods, and evaluate them in a head-to-head comparison for simulated simple-cell data. This shows that, at least for quasilinear simulated simple cells, a regularized solution ('reginv') is most efficient, requiring fewer stimulus presentations for high-resolution reconstruction of the first-order kernel. We also investigate several practical issues that determine the success of this kind of experiment-the effects of neuronal nonlinearities, response variability and the choice of stimulus regime.
authors.affiliation: Department of Physiology, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK. benwill@socrates.berkeley.edu
authors.forename: Ben
authors.initials: B
authors.lastname: Willmore
authors.forename: Darragh
authors.initials: D
authors.lastname: Smyth
mesh_terms: D019368:Nature; D009474:Neurons / Q000166:cytology* / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D013223:Statistics as Topic; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology; D014794:Visual Fields / Q000502:physiology*