Effects of Exogenous Auditory Attention on Temporal and Spectral Resolution

Previous research in the visual domain suggests that exogenous attention in form of peripheral cueing increases spatial but lowers temporal resolution. It is unclear whether this effect transfers to other sensory modalities. Here, we tested the effects of exogenous attention on temporal and spectral resolution in the auditory domain. Eighteen young, normal-hearing adults were tested in both gap and frequency change detection tasks with exogenous cuing. Benefits of valid cuing were only present in the gap detection task while costs of invalid cuing were observed in both tasks. Our results suggest that exogenous attention in the auditory system improves temporal resolution without compromising spectral resolution

Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2

Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus–response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that “foreground” signals should be enhanced while “background” signals should be selectively suppressed. We test how adaptation changes the input–response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity

The Impact of Anesthetic State on Spike-Sorting Success in the Cortex: A Comparison of Ketamine and Urethane Anesthesia

Spike sorting is an essential first step in most analyses of extracellular in vivo electrophysiological recordings. Here we show that spike-sorting success depends critically on characteristics of coordinated population activity that can differ between anesthetic states. In tetrode recordings from mouse auditory cortex, spike sorting was significantly less successful under ketamine/medetomidine (ket/med) than urethane anesthesia. Surprisingly, this difficulty with sorting under ket/med anesthesia did not appear to result from either greater millisecond-scale burstiness of neural activity or increased coordination of activity among neighboring neurons. Rather, the key factor affecting sorting success appeared to be the amount of coordinated population activity at long time intervals and across large cortical distances. We propose that spike-sorting success is directly dependent on overall coordination of activity, and is most disrupted by large-scale fluctuations in cortical population activity. Reliability of single-unit recording may therefore differ not only between urethane-anesthetized and ket/med-anesthetized states as demonstrated here, but also between synchronized and desynchronized states, asleep and awake states, or inattentive and attentive states in unanesthetized animals

A Neural Mechanism for Time-Window Separation Resolves Ambiguity of Adaptive Coding

<div><p>The senses of animals are confronted with changing environments and different contexts. Neural adaptation is one important tool to adjust sensitivity to varying intensity ranges. For instance, in a quiet night outdoors, our hearing is more sensitive than when we are confronted with the plurality of sounds in a large city during the day. However, adaptation also removes available information on absolute sound levels and may thus cause ambiguity. Experimental data on the trade-off between benefits and loss through adaptation is scarce and very few mechanisms have been proposed to resolve it. We present an example where adaptation is beneficial for one task—namely, the reliable encoding of the pattern of an acoustic signal—but detrimental for another—the localization of the same acoustic stimulus. With a combination of neurophysiological data, modeling, and behavioral tests, we show that adaptation in the periphery of the auditory pathway of grasshoppers enables intensity-invariant coding of amplitude modulations, but at the same time, degrades information available for sound localization. We demonstrate how focusing the response of localization neurons to the onset of relevant signals separates processing of localization and pattern information temporally. In this way, the ambiguity of adaptive coding can be circumvented and both absolute and relative levels can be processed using the same set of peripheral neurons.</p></div

Summary of the adaptation (<i>τ_a</i>) and recovery (<i>τ_r</i>) time constants for the <i>T. oceanicus</i> and <i>T. leo</i> AN2 cells.

<p>See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000182#pcbi-1000182-g003" target="_blank">Figure 3</a> for the adaptation protocols. SD is the standard deviation across the <i>n</i> cells.</p

Adaptation induced changes in the mutual information between the stimulus and the neural response.

<p>(A1,A2) Distribution and combined posterior density of changes in the transmitted mutual information when considering the whole stimulus range (relative intensity from −4.5 dB to 4.5 dB) and the trimodal amplitude distribution. For each cell the change of the mutual information is calculated as the difference of the mutual information for the ‘trimodal’ (neural response adapted to the trimodal stimulus) and the ‘bimodal’ (neural response adapted to the bimodal stimulus) response curve. The distribution in (A1) is based on the mean values of changes in mutual information for individual cells. (B1,B2) Distribution and combined posterior density of changes in the transmitted mutual information when considering the stimulus range from −4.5 dB to 1.5 dB (including only the two low-intensity peaks of the trimodal stimulus distribution). (C1,C2) Distribution and combined posterior density of changes in the transmitted mutual information when considering the stimulus range from 1.5 dB to 4.5 dB (including only the high-intensity peak of the trimodal stimulus distribution). Triangles denote the median value. The distribution of cells that showed changes that were significant (Bayesian posterior intervals, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000182#s2" target="_blank">Methods</a>, Bayesian data analysis) is marked black in (A1,B1,C1). Shaded areas depict the left-tailed 95% posterior intervals in (A2,B2) and the two-tailed 95% posterior interval in (C2).</p

ILD coding improves with additional intrinsic adaptation in the central, ascending neurons.

<p>A: Response of a pair of modeled direction coding central neurons to an artificial grasshopper song played back from three directions (indicated by line colors) and the difference between the responses of the ipsi- and contralateral ascending neuron (bottom panel). B: Same simulation, but with intrinsic adaptation added to the model of the ascending/central neurons. C: Decoding of the ILD from the responses as pictured in (A). Responses of the AN pair to combinations of ten directions (ILDs) and 33 mean levels were used and classified for decoding performance of ILDs. Left panel: all syllables of the song taken into account, right panel: only responses to the first syllable of the song were used for classification. D: Classification as in C but with adaptation added to the central neurons in the model. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002096#pbio.1002096.s002" target="_blank">S2 Code</a> for the code ran to model the network responses and classification of these responses.</p

Combined posterior distribution (cf. Methods, Bayesian data analysis) of the adaptation time constants <i>τ_a</i> (A) and the recovery time constants <i>τ_r</i> (B) for the <i>T. oceanicus</i> (solid line) and <i>T. leo</i> (dotted line) AN2 cells.

<p>Solid (dotted) lines on top of the figures depict the 95% posterior intervals.</p

Typical recording trace from a cricket AN2 neuron (<i>T. oceanicus</i>).

<p>The figure shows the voltage trace during constant stimulation (duration 1 s) with a sinusoidal tone of 16 kHz frequency. The shaded area depicts the spike detection window, bounded by the lower and upper threshold.</p