Active data collection for efficient estimation and comparison of nonlinear neural models

Christopher DiMattina, Kechen Zhang

Research output: Contribution to journalLetterpeer-review

Abstract

The stimulus-response relationship of many sensory neurons is nonlinear, but fully quantifying this relationship by a complex nonlinear model may require too much data to be experimentally tractable. Here we present a theoretical study of a general two-stage computational method that may help to significantly reduce the number of stimuli needed to obtain an accurate mathematical description of nonlinear neural responses. Our method of active data collection first adaptively generates stimuli that are optimal for estimating the parameters of competing nonlinear models and then uses these estimates to generate stimuli online that are optimal for discriminating these models. We applied our method to simple hierarchical circuit models, including nonlinear networks built on the spatiotemporal or spectral-temporal receptive fields, and confirmed that collecting data using our two-stage adaptive algorithmwas far more effective for estimating and comparing competing nonlinear sensory processing models than standard nonadaptive methods using random stimuli.

Original languageEnglish (US)
Pages (from-to)2242-2288
Number of pages47
JournalNeural Computation
Volume23
Issue number9
DOIs
StatePublished - Sep 1 2011

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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