My Auditory Projects

Auditory Neuroscience

The Interplay of Excitation and Inhibition During Adaptation in the Inferior Colliculus

Nadja Schinkel-Bielefeld, Nicholas Lesica, Mai El-Zonkoly, Benedikt Grothe and Daniel Butts

Neuronal processing changes with stimulus context. For example, addition of background noise to vocalization stimulus can cause a shift in temporal aspects of linear receptive fields of neurons in the inferior colliculus (IC). This can be advantageous for stimulus processing. However, how these changes are implemented in neurons is difficult to infer from the linear receptive field alone.

Here, we use a General Nonlinear Model (GNM) to describe stimulus processing of IC neurons for temporally modulated narrow band stimuli that reproduce the temporal characteristics of vocalizations. For characterization of separate excitatory and inhibitory inputs, intracellular recordings are typically necessary. However, the GNM employs efficient maximum likelihood estimation techniques, and can describe the separate influences of putative excitatory and inhibitory contributions to neuronal processing, using extracellular data alone.

The GNM finds excitation and inhibition with similar tuning, but with a relative delay between them. Capturing this putative excitation and inhibition can double the predictive power compared with models based on single receptive fields, as measured on cross-validated vocalization stimuli. Furthermore, the GNM suggests an underlying source for the “adaptive” changes observed in the context of linear models in the presence of background noise. While linear modeling predicts that changes in stimulus processing result from temporal shifts in the receptive field, we see that such changes arise from the modulation of the relative strength of excitation and inhibition. Because the temporal processing of excitation and inhibition is temporally offset, changing their relative response has effects on the overall temporal processing of the neuron in general, reflected in the change in the time kernels of linear models.

Thus, considering the interplay of excitation and inhibition provides a more accurate description of the underlying computation on the stimulus performed by IC neuron responses, and provides insight into their adaptation in different stimulus contexts.

We use a single spike train (40 s) with a 0.5 ms temporal resolution to fit the GNM and linear models, and cross-validate using 100 repeats of a 3-sec unique stimulus sequence. Fig. A (above) shows the cross-validated PSTH for the GNM (magenta), the generalized linear model (which includes a spike term to take into account spike history, dark blue) and the linear-nonlinear model (no spike term, light blue). Fig. B: A comparison between the percent explainable variance (predictive power) of the linear-nonlinear model (red) and the generalized linear model (green). On average, the GNM explains around twice as much of the explainable variance of the neurons response, compared with these other models (N = 23).

A Nonlinear Model of Cortical Encoding of Natural Speech

Nadja Schinkel-Bielefeld, Stephen David, Shihab Shamma and Daniel Butts

Understanding the function of neurons in the auditory cortex relies on characterizing non-linear neuronal processing in the context of complex stimuli. In particular, it is well known that A1 neurons receive both excitatory and inhibitory inputs, often representing the same or overlapping frequencies. The resulting neuronal processing often depends on the precise nature of this balance between excitation and inhibition and its dynamical interplay in complex stimulus contexts. For example, balanced excitation and inhibition is assumed to sharpen neuronal responses in time and frequency, flanking inhibition can enhance sensitivity to a center frequency, and completely unbalanced excitation and inhibition can lead to intensity tuning.

Here, we address these issues using non-linear modeling of extracellular recordings from neurons in primary auditory cortex of a passively listening ferret in the context of speech stimuli. The standard approach to mapping stimulus selectivity using extracellular data is through measurements of the spectro-temporal receptive field (STRF), which offers a first-order characterization of the features that the neuron responds to. However, STRF-based linear models have difficulties identifying inhibition, especially if excitation and inhibition are balanced or the spontaneous firing rate is low. For the separate characterization of excitation and inhibition, usually intracellular recordings are necessary.

We use a newly developed Generalized Non-Linear Modeling (GNM) approach to characterize A1 neurons. This approach is based on efficient maximum likelihood estimation techniques developed for Generalized Linear Models, but incorporates additional static non-linearities operating on the output of each linear element of the model. Importantly, this framework also allows for the identification of multiple spectrotemporal features that influence the response and their associated non-linearities, such as distinct excitatory and inhibitory contributions. The GNM approach can be readily applied to highly correlated stimuli such as speech, and has similar data requirements to standard STRF estimation experiments.

We find that the GNM performs as well or better on cross-validated speech data than models built using a standard STRF-based Linear-Nonlinear (NL) model. The GNM typically identifies the spectrotemporal tuning observed in the spike-triggered average, and in most cases it also finds a second suppressive or “inhibitory” kernel, based on spectrotemporal tuning that is usually not apparent in the STRF. Time kernels for excitation and inhibition generally look similar with excitation leading inhibition, while the relative frequency tuning is much more variable from neuron to neuron. While in many cases a sharpening of responses results from inhibition that balances or is slightly wider than excitation, there are also cases with additional inhibitory peaks that do not correspond to excitation, and can be distributed over more than three octaves.

We thus offer a detailed analysis of how putative excitatory and inhibitory inputs are tuned in complex stimulus contexts in primary auditory cortex. More generally, we demonstrate a modeling framework to identify multiple elements that contribute to the neuronal responses in complex stimulus contexts using extracellular data.