Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles

PLoS One. 2014 Apr 3;9(4):e93062. doi: 10.1371/journal.pone.0093062. eCollection 2014.

Abstract

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acoustic Stimulation*
  • Action Potentials / physiology*
  • Animals
  • Discrimination Learning*
  • Electrophysiology
  • Gerbillinae
  • Linear Models
  • Models, Neurological*
  • Neurons / physiology*
  • Normal Distribution*

Grant support

This study was supported by the German Research Foundation (DFG) within the Collaborative Research Centre SFB/TRR31 "The Active Auditory System". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.