Experimental grease for the squeaky model: Zeroing in on the mechanisms that
matter for processing natural sounds in auditory cortex
A major goal of sensory neuroscience is to understand how the brain represents and
extracts information from complex natural stimuli. General predictive models that map
the functional relationship between arbitrary complex stimuli and neural responses
provide one approach to this problem. However, this approach has been limited by
problems of dimensionality. Models that make few assumptions about mechanism tend to
require a large number of parameters. Low-dimensional models that explicitly model
important mechanisms in fewer parameters should, in theory, perform well, but knowing
a priori which mechanisms are important is not possible. Here we describe an iterative
approach that uses complex nonlinear models focused in a limited stimulus domain to
identify key mechanisms that can subsequently be tested in a more general model
framework.
Previous studies of primary auditory cortex (A1) have suggested that a major limitation
of current models is their ability to predict the nonlinear temporal dynamics of neural
responses. In recent experiments, we have collected data from A1 using a reduceddimensionality stimulus composed of band-pass noise modulated by a natural sound
envelope. This stimulus permits fitting models that span only a single spectral dimension,
leaving more power to model complex temporal integration. The resulting fits suggest
that feedforward synaptic depression and interactions between co-tuned excitatory and
inhibitory inputs both play a role in neural response dynamics. Ongoing work is
implementing these mechanisms explicitly in more general spectro-temporal models.