Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex.
Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices.
Hearing Research Center and Department of Biomedical Engineering, Boston University , Boston, Massachusetts 02215.
David C
0000-0001-8091-3827
DC
Sloas
Department of Mathematics and Statistics, Boston University , Boston, Massachusetts 02215.
Ran
R
Zhuo
Department of Mathematics and Statistics, Boston University , Boston, Massachusetts 02215.
Hongbo
H
Xue
Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary , Boston, Massachusetts 02114, and.
Anna R
AR
Chambers
Department of Mathematics and Statistics, Boston University , Boston, Massachusetts 02215.
Eric
E
Kolaczyk
Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts 02114, and; Department of Otolaryngology, Harvard Medical School, Boston, Massachusetts 02115.
Daniel B
DB
Polley
Hearing Research Center and Department of Biomedical Engineering, Boston University , Boston, Massachusetts 02215.
Kamal
K
Sen
D000161:Acoustic Stimulation; D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001303:Auditory Cortex / Q000502:physiology*; D001307:Auditory Perception / Q000502:physiology*; D051379:Mice; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted
auditory; evolutionary algorithm; generalized additive model; integration; sensory