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Link to original content: http://pubmed.ncbi.nlm.nih.gov/38396044/
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. 2024 Feb 23;14(1):4457.
doi: 10.1038/s41598-024-54865-5.

Increased reliance on temporal coding when target sound is softer than the background

Nima Alamatsaz  1   2 , Merri J Rosen  3   4   5 , Antje Ihlefeld  6
Affiliations

Increased reliance on temporal coding when target sound is softer than the background

Nima Alamatsaz et al. Sci Rep. .

Abstract

Everyday environments often contain multiple concurrent sound sources that fluctuate over time. Normally hearing listeners can benefit from high signal-to-noise ratios (SNRs) in energetic dips of temporally fluctuating background sound, a phenomenon called dip-listening. Specialized mechanisms of dip-listening exist across the entire auditory pathway. Both the instantaneous fluctuating and the long-term overall SNR shape dip-listening. An unresolved issue regarding cortical mechanisms of dip-listening is how target perception remains invariant to overall SNR, specifically, across different tone levels with an ongoing fluctuating masker. Equivalent target detection over both positive and negative overall SNRs (SNR invariance) is reliably achieved in highly-trained listeners. Dip-listening is correlated with the ability to resolve temporal fine structure, which involves temporally-varying spike patterns. Thus the current work tests the hypothesis that at negative SNRs, neuronal readout mechanisms need to increasingly rely on decoding strategies based on temporal spike patterns, as opposed to spike count. Recordings from chronically implanted electrode arrays in core auditory cortex of trained and awake Mongolian gerbils that are engaged in a tone detection task in 10 Hz amplitude-modulated background sound reveal that rate-based decoding is not SNR-invariant, whereas temporal coding is informative at both negative and positive SNRs.

Keywords: Auditory cortex; Central masking; Dip-listening; Gerbil; Modulation masking release; SNR invariance; Signal-to-noise ratio.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Testing apparatus and behavioral design. (A) The test setup included a loudspeaker above the test area, a nose poke and a lick spout. In addition, for chronically implanted animals, a wireless system recorded the cortical traces. (B) The background sound (brown), consisting of 10 Hz amplitude modulated noise, was continuously present. On Go trials, a target sound (blue) was additionally played, consisting of a 1 s, 1 kHz tone and randomly chosen from -10, 0 or 10 dB SNR. (C) The gerbil triggered a new trial by breaking a light beam inside the nose poke, and could obtain water reward through the lick spout. A loudspeaker mounted above the test area played the sounds. The gerbil then responded to the trial condition either by licking the water spout, or by withholding a response through waiting or by poking the nose poke once more. Depending on the stimulus condition, this response resulted in either a Hit, a Correct Reject, a Miss or a False Alarm.
Figure 2
Figure 2
Average number of sessions per each training and testing stage shown as shaded progress bars for each of the two gerbil groups. The lower and upper bounds of session counts are indicated with error bars.
Figure 3
Figure 3
Average behavioral performance of each group of gerbils across the recorded sessions with active engagement (sound-detecting n = 4, non-sound-detecting n = 2, 5 sessions each). Error bars show one SEM.
Figure 4
Figure 4
Behavioral curves at 0 dB SNR as a function of tone duration for non-implanted gerbils (n = 2, sessions = 13 each on average). Error bars show one SEM.
Figure 5
Figure 5
Average first-spike latency of all single-units. Error bars show one SEM.
Figure 6
Figure 6
(A–D) Response time histogram for sound-detecting (n = 4, units = 151) and non-sound-detecting gerbils (n = 2, units = 56). NoGo histograms are depicted under each Go histogram for direct visual comparison. Timing of target indicated by blue shading, timing of nose-poke indicated by grey bar in active conditions. (E–H) Firing rate z-score of the neural response as a function of time, calculated in incremental windows relative to target onset. (I–L) Power of spectral density of the neural activity calculated with MTS at different frequencies. Ribbons indicate one standard error of the mean (SEM).
Figure 7
Figure 7
Vector strength during the sustained response period as a function of SNR. Here only phasic units are included which have at least one significant vector strength across all SNRs and task engagement conditions. Points mark the average, and error bars show one SEM.
Figure 8
Figure 8
(A) Schematic calculation of Pearson’s correlation ρ of neural responses for a 300 ms time-window starting at nose-poke. Firing rates at matching time points are correlated between each Go condition and the NoGo. (B,C) Running Pearson’s correlation of neural responses during Go trials in reference to NoGo. Right panels are time-expanded on the x-axis. Timing of target indicated by blue shading, timing of nose-poke indicated by grey bar.
Figure 9
Figure 9
Average of all neurometric rate and temporal measures that were calculated relative to NoGo for each gerbil group and task engagement condition. These metrics were derived for the sustained response period. (A) Mutual information (rate-based measure). (B) Mutual information for the sound-detecting gerbils during active task engagement, separated by HIT and MISS trials. (C) Similarity index calculated as slope of a linear regression fit (combining temporal- and rate-based information). (D) Spike count z-score (rate-based measure). (E) Power spectral density z-score at 10 Hz derived with MTS (temporal measure). (F) Target-evoked correlation response (ρ-index) z-score (temporal measure). Error bars show one SEM.
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