title: Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields.
abstract: A primary goal of auditory neuroscience is to identify the sound features extracted and represented by auditory neurons. Linear encoding models, which describe neural responses as a function of the stimulus, have been primarily used for this purpose. Here, we provide theoretical arguments and experimental evidence in support of an alternative approach, based on decoding the stimulus from the neural response. We used a Bayesian normative approach to predict the responses of neurons detecting relevant auditory features, despite ambiguities and noise. We compared the model predictions to recordings from the primary auditory cortex of ferrets and found that: (1) the decoding filters of auditory neurons resemble the filters learned from the statistics of speech sounds; (2) the decoding model captures the dynamics of responses better than a linear encoding model of similar complexity; and (3) the decoding model accounts for the accuracy with which the stimulus is represented in neural activity, whereas linear encoding model performs very poorly. Most importantly, our model predicts that neuronal responses are fundamentally shaped by "explaining away," a divisive competition between alternative interpretations of the auditory scene.
  
  
  
  Neural responses in the auditory cortex are dynamic, nonlinear, and hard to predict. Traditionally, encoding models have been used to describe neural responses as a function of the stimulus. However, in addition to external stimulation, neural activity is strongly modulated by the responses of other neurons in the network. We hypothesized that auditory neurons aim to collectively decode their stimulus. In particular, a stimulus feature that is decoded (or explained away) by one neuron is not explained by another. We demonstrated that this novel Bayesian decoding model is better at capturing the dynamic responses of cortical neurons in ferrets. Whereas the linear encoding model poorly reflects selectivity of neurons, the decoding model can account for the strong nonlinearities observed in neural data.
authors.affiliation: Group for Neural Theory, Laboratoire de Neurosciences Cognitives, Département d'Etudes Cognitives, Ecole Normale Supérieure, 75005 Paris, France, and izzet.burak.yildiz@gmail.com.
authors.forename: Izzet B
authors.identifier: 0000-0002-7998-4449
authors.initials: IB
authors.lastname: Yildiz
authors.affiliation: Department of Electrical Engineering, Columbia University, New York, New York 10027.
authors.forename: Nima
authors.initials: N
authors.lastname: Mesgarani
authors.affiliation: Group for Neural Theory, Laboratoire de Neurosciences Cognitives, Département d'Etudes Cognitives, Ecole Normale Supérieure, 75005 Paris, France, and.
authors.forename: Sophie
authors.initials: S
authors.lastname: Deneve
mesh_terms: D000161:Acoustic Stimulation; D000465:Algorithms; D000818:Animals; D001303:Auditory Cortex / Q000502:physiology; D001307:Auditory Perception / Q000502:physiology*; D001499:Bayes Theorem; D005260:Female; D005289:Ferrets / Q000502:physiology*; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology; D009622:Noise; D010700:Phonetics; D011984:Sensory Receptor Cells / Q000502:physiology*
keywords: Bayesian; auditory cortex; decoding; encoding; explaining away; predictive coding