The Consequences of Response Nonlinearities for Interpretation of Spectrotemporal Receptive Fields

Abstract

Neurons in the central auditory system are often described by the spectrotemporal receptive field (STRF), conventionally defined as the best linear fit between the spectrogram of a sound and the spike rate it evokes. An STRF is often assumed to provide an estimate of the receptive field of a neuron, i.e., the spectral and temporal range of stimuli that affect the response. However, when the true stimulusresponse function is nonlinear, the STRF will be stimulus dependent, and changes in the stimulus properties can alter estimates of the sign and spectrotemporal extent of receptive field components. We demonstrate analytically and in simulations that, even when uncorrelated stimuli are used, interactions between simple neuronal nonlinearities and higher-order structure in the stimulus can produce STRFs that show contributions from time-frequency combinations to which the neuron is actually insensitive. Only when spectrotemporally independent stimuli are used does the STRF reliably indicate features of the underlying receptive field, and even then it provides only a conservative estimate. One consequence of these observations, illustrated using natural stimuli, is that a stimulus-induced change in an STRF could arise from a consistent but nonlinear neuronal response to stimulus ensembles with differing higher-order dependencies. Thus, although the responses of higher auditory neurons may well involve adaptation to the statistics of different stimulus ensembles, stimulus dependence of STRFs alone, or indeed of any overly constrained stimulus-response mapping, cannot demonstrate the nature or magnitude of such effects.

Auditory abstraction from spectro-temporal features to coding auditory entities

“…When the stimulus set has a rich correlation structure, averaging the stimuli that precede spikes usually reveals structures that characterize the stimulus itself (regardless of the responses). At the same time, averaging ignores high-order nonlinear dependencies in the stimuli that the neural responses may be sensitive to (20). All these cautionary notes apply to the natural and modified stimuli used in this paper and, as a result, the mean stimulus preceding spikes is not sensitive to interneuron differences.…”

“…All these cautionary notes apply to the natural and modified stimuli used in this paper and, as a result, the mean stimulus preceding spikes is not sensitive to interneuron differences. Although a number of sensitive methods have been devised to overcome these problems (19,21), using linear STRFs may have limited predictive accuracy even when correcting for the correlations in the stimuli (20,22).…”

Neural processing of natural sounds

“…Fourth, the stimulus-response functions of high-level auditory neurons are often dominated by non-linearities that are not captured in the STRF, which, in its simplest form as a model, predicts neural responses from a linear combination of spectro-temporal features. Estimating the nature of the non-linearities is not only important to fully capture the computations performed by the system but is important as they might impact the estimation of the linear STRF with natural stimuli [47,48]. There are many approaches to this problem.…”

Neuron-Specific Stimulus Masking Reveals Interference in Spike Timing at the Cortical Level

“…For example, it is known that STRFs are generally not good predictors of neural responses for stimuli with different spectrotemporal properties from those used to calculate the STRF (Theunissen et al 2000;Christianson et al 2008;Gourevitch et al 2009), so here we used the STRF only to estimate which frequencies mattered to each neuron in the relevant discrimination task stimuli (birdsong). Additionally, normalized reverse correlation imposes spectral smoothness on the STRF estimates (David et al 2007), and the resulting overestimation of the range of important frequencies allowed us to be conservative in our frequency removal procedure.…”