Katrin Vonderschen

Distribution of interaural time difference in the barn owl s inferior colliculus in the low- and high-frequency ranges.

Interaural time differences are an important cue for azimuthal sound localization. It is still unclear whether the same neuronal mechanisms underlie the representation in the brain of interaural time difference in different vertebrates and whether these mechanisms are driven by common constraints, such as optimal coding. Current sound localization models may be discriminated by studying the spectral distribution of response peaks in tuning curves that measure the sensitivity to interaural time difference. The sound localization system of the barn owl has been studied intensively, but data that would allow discrimination between currently discussed models are missing from this animal. We have therefore obtained extracellular recordings from the time-sensitive subnuclei of the barn owls inferior colliculus. Response peaks were broadly scattered over the physiological range of interaural time differences. A change in the representation of the interaural phase differences with frequency was not observed. In some neurons, response peaks fell outside the physiological range of interaural time differences. For a considerable number of neurons, the peak closest to zero interaural time difference was not the behaviorally relevant peak. The data are in best accordance with models suggesting that a place code underlies the representation of interaural time difference. The data from the high-frequency range, but not from the low-frequency range, are consistent with predictions of optimal coding. We speculate that the deviation of the representation of interaural time difference from optimal-coding models in the low-frequency range is attributable to the diminished importance of low frequencies for catching prey in this species.

Parallel Coding of First- and Second-Order Stimulus Attributes by Midbrain Electrosensory Neurons

Natural stimuli often have time-varying first-order (i.e., mean) and second-order (i.e., variance) attributes that each carry critical information for perception and can vary independently over orders of magnitude. Experiments have shown that sensory systems continuously adapt their responses based on changes in each of these attributes. This adaptation creates ambiguity in the neural code as multiple stimuli may elicit the same neural response. While parallel processing of first- and second-order attributes by separate neural pathways is sufficient to remove this ambiguity, the existence of such pathways and the neural circuits that mediate their emergence have not been uncovered to date. We recorded the responses of midbrain electrosensory neurons in the weakly electric fish Apteronotus leptorhynchus to stimuli with first- and second-order attributes that varied independently in time. We found three distinct groups of midbrain neurons: the first group responded to both first- and second-order attributes, the second group responded selectively to first-order attributes, and the last group responded selectively to second-order attributes. In contrast, all afferent hindbrain neurons responded to both first- and second-order attributes. Using computational analyses, we show how inputs from a heterogeneous population of ON- and OFF-type afferent neurons are combined to give rise to response selectivity to either first- or second-order stimulus attributes in midbrain neurons. Our study thus uncovers, for the first time, generic and widely applicable mechanisms by which parallel processing of first- and second-order stimulus attributes emerges in the brain.

Sparse and dense coding of natural stimuli by distinct midbrain neuron subpopulations in weakly electric fish

While peripheral sensory neurons respond to natural stimuli with a broad range of spatiotemporal frequencies, central neurons instead respond sparsely to specific features in general. The nonlinear transformations leading to this emergent selectivity are not well understood. Here we characterized how the neural representation of stimuli changes across successive brain areas, using the electrosensory system of weakly electric fish as a model system. We found that midbrain torus semicircularis (TS) neurons were on average more selective in their responses than hindbrain electrosensory lateral line lobe (ELL) neurons. Further analysis revealed two categories of TS neurons: dense coding TS neurons that were ELL-like and sparse coding TS neurons that displayed selective responses. These neurons in general responded to preferred stimuli with few spikes and were mostly silent for other stimuli. We further investigated whether information about stimulus attributes was contained in the activities of ELL and TS neurons. To do so, we used a spike train metric to quantify how well stimuli could be discriminated based on spiking responses. We found that sparse coding TS neurons performed poorly even when their activities were combined compared with ELL and dense coding TS neurons. In contrast, combining the activities of as few as 12 dense coding TS neurons could lead to optimal discrimination. On the other hand, sparse coding TS neurons were better detectors of whether their preferred stimulus occurred compared with either dense coding TS or ELL neurons. Our results therefore suggest that the TS implements parallel detection and estimation of sensory input.

Tuning to Interaural Time Difference and Frequency Differs Between the Auditory Arcopallium and the External Nucleus of the Inferior Colliculus

Barn owls process sound-localization information in two parallel pathways, the midbrain and the forebrain pathway. Exctracellular recordings of neural responses to auditory stimuli from far advanced stations of these pathways, the auditory arcopallium in the forebrain and the external nucleus of the inferior colliculus in the midbrain, demonstrated that the representations of interaural time difference and frequency in the forebrain pathway differ from those in the midbrain pathway. Specifically, low-frequency representation was conserved in the forebrain pathway, while it was lost in the midbrain pathway. Variation of interaural time difference yielded symmetrical tuning curves in the midbrain pathway. By contrast, the typical forebrain-tuning curve was asymmetric with a steep slope crossing zero time difference and a less-steep slope toward larger contralateral time disparities. Low sound frequencies contributed sensitivity to contralateral leading sounds underlying these asymmetries, whereas high frequencies enhanced the steepness of slopes at small interaural time differences. Furthermore, the peaks of time-disparity tuning curves were wider in the forebrain than in the midbrain. The distribution of the steepest slopes of best interaural time differences in the auditory arcopallium, but not in the external nucleus of the inferior colliculus, was centered at zero time difference. The distribution observed in the auditory arocpallium is reminiscent of the situation observed in small mammals. We speculate that the forebrain representation may serve as a population code supporting fine discrimination of central interaural time differences and coarse indication of laterality of a stimulus for large interaural time differences.

Transformation from a Pure Time Delay to a Mixed Time and Phase Delay Representation in the Auditory Forebrain Pathway

Birds and mammals exploit interaural time differences (ITDs) for sound localization. Subsequent to ITD detection by brainstem neurons, ITD processing continues in parallel midbrain and forebrain pathways. In the barn owl, both ITD detection and processing in the midbrain are specialized to extract ITDs independent of frequency, which amounts to a pure time delay representation. Recent results have elucidated different mechanisms of ITD detection in mammals, which lead to a representation of small ITDs in high-frequency channels and large ITDs in low-frequency channels, resembling a phase delay representation. However, the detection mechanism does not prevent a change in ITD representation at higher processing stages. Here we analyze ITD tuning across frequency channels with pure tone and noise stimuli in neurons of the barn owls auditory arcopallium, a nucleus at the endpoint of the forebrain pathway. To extend the analysis of ITD representation across frequency bands to a large neural population, we employed Fourier analysis for the spectral decomposition of ITD curves recorded with noise stimuli. This method was validated using physiological as well as model data. We found that low frequencies convey sensitivity to large ITDs, whereas high frequencies convey sensitivity to small ITDs. Moreover, different linear phase frequency regimes in the high-frequency and low-frequency ranges suggested an independent convergence of inputs from these frequency channels. Our results are consistent with ITD being remodeled toward a phase delay representation along the forebrain pathway. This indicates that sensory representations may undergo substantial reorganization, presumably in relation to specific behavioral output.

Sparse coding of natural communication signals in midbrain neurons

Meeting abstracts - A single PDF containing all abstracts in this Supplement is available here . http://www. biomedcentral.co m/content/pdf/14 71-2202 -10-S1-info.pdf

Parallel coding of first and second order stimulus attributes

Natural stimuli often have time varying first (i.e. mean) and second order (i.e. variance) attributes that each carry critical information for perception and can vary independently over orders of magnitude. We recorded the responses of midbrain electrosensory neurons in the weakly electric fish Apteronotus leptorhynchus to stimuli with first and second order attributes that varied independently in time. We found two distinct groups of midbrain neurons: the first group responded to both first and second order attributes while the other responded selectively to second order attributes. Using computational analyses, we show how inputs from a heterogeneous population of ON- and OFF-type afferent neurons are combined in order to give rise to response selectivity to second order stimulus attributes in midbrain neurons. Our study thus uncovers, for the first time, generic and widely applicable mechanisms by which selectivity to second order stimulus attributes emerges in the brain.