Multidimensional receptive field processing by cat primary auditory cortical neurons

Abstract

The receptive fields of many auditory cortical neurons are multidimensional and are best represented by more than one stimulus feature. The number of these dimensions, their characteristics, and how they differ with stimulus context have been relatively unexplored. Standard methods that are often used to characterize multidimensional stimulus selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MIDs), are either limited to Gaussian stimuli or are only able to recover a small number of stimulus features due to data limitations. An information theoretic extension of STC, the maximum noise entropy (MNE) model, can be used with non-Gaussian stimulus distributions to find an arbitrary number of stimulus dimensions. When we applied the MNE model to auditory cortical neurons, we often found more than two stimulus features that influenced neuronal firing. Excitatory and suppressive features coded different acoustic contexts: excitatory features encoded higher temporal and spectral modulations, while suppressive features had lower modulation frequency preferences. We found that the excitatory and suppressive features themselves were sensitive to stimulus context when we employed two stimuli that differed only in their short-term correlation structure: while the linear features were similar, the secondary features were strongly affected by stimulus stimulus. These results show that multidimensional receptive field processing depends on feature type and stimulus context.

A theory of perceptual number encoding.

“…The perceptual interdependence we have described for number and non-numerical magnitude is not unique to humans or the encoding of magnitude. This type of coding exists in the auditory cortex of birds, cats, and ferrets for dimensions such as pitch and timbre (Atencio, & Sharpee, 2017;Sharpee, Nagel, & Doupe, 2011;Walker, Bizley, King, & Schnupp, 2011), as well as within the navigation circuitry of bats and rodents, for each dimension of three-dimensional space (Finkelstein et al, 2015;Wilson, Alexandre, Trentin, & Tripodi, 2018). The pervasiveness of such representations suggests a general organizing principle of multidimensional stimuli in the minds and brains of animals.…”

Recent advances in understanding the auditory cortex

“…With a maximally informative dimensions approach, it has been found that multiple stimulus dimensions are required to describe the responses of neurons in A1 12 , 13 , whereas a similar approach requires only a single dimension to describe most neurons in the inferior colliculus (IC) in the midbrain 14 . This suggests that neuronal complexity is higher in the cortex and that this complexity can be captured by nonlinear combination of the responses of multiple simpler units, a finding that also applies to high-level neurons in songbirds 15 , 16 .…”

Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding

“…Second-order nonlinear models can require a squaring of the number of parameters, but many of those parameters are not actually required. Subspace projections and targeted nonlinearities provide a means of reducing dimensionality while providing greater explanatory power than linear models (Atencio and Sharpee, 2017; David and Shamma, 2013). These strategies may provide the best direction toward more comprehensive context-dependent encoding models.…”

“…The linear STRF can be conceived as the first-order Volterra series expansion of a nonlinear stimulus-response function (Aertsen and Johannesma, 1981; Eggermont, 1993). Some nonlinear models incorporate second-order nonlinearities, in which the response is the linear weighted sum of the stimulus spectrogram plus a weighted sum of the product of values in the spectrogram (Atencio et al, 2008; Atencio and Sharpee, 2017). These approaches have been integrated with probability theory-based models of neural coding and neural network models from machine learning (Atencio et al, 2008; Calabrese et al, 2011; Pillow et al, 2008).…”