A Spike-Timing Code for Discriminating Conspecific Vocalizations in the Thalamocortical System of Anesthetized and Awake Guinea Pigs

Abstract

Understanding how communication sounds are processed and encoded in the central auditory system is critical to understanding the neural bases of acoustic communication. Here, we examined neuronal representations of species-specific vocalizations, which are communication sounds that many species rely on for survival and social interaction. In some species, the evoked responses of auditory cortex neurons are stronger in response to natural conspecific vocalizations than to their time-reversed, spectrally identical, counterparts. We applied information theory-based analyses to single-unit spike trains collected in the auditory cortex (n ϭ 139) and auditory thalamus (n ϭ 135) of anesthetized animals as well as in the auditory cortex (n ϭ 119) of awake guinea pigs during presentation of four conspecific vocalizations. Few thalamic and cortical cells (Ͻ10%) displayed a firing rate preference for the natural version of these vocalizations. In contrast, when the information transmitted by the spike trains was quantified with a temporal precision of 10 -50 ms, many cells (Ͼ75%) displayed a significant amount of information (i.e., Ͼ2SD above chance levels), especially in the awake condition. The computed correlation index between spike trains (R corr , defined by Schreiber et al., 2003) indicated similar spike-timing reliability for both the natural and time-reversed versions of each vocalization, but higher reliability for awake animals compared with anesthetized animals. Based on temporal discharge patterns, even cells that were only weakly responsive to vocalizations displayed a significant level of information. These findings emphasize the importance of temporal discharge patterns as a coding mechanism for natural communication sounds, particularly in awake animals.

What can spike train distances tell us about the neural code?

“…This is because I * (S P , S; τ ) can have piecewise constant intervals (e. g. Victor and Purpura, 1996;Di Lorenzo and Victor, 2003;Roussin et al, 2008;Huetz et al, 2006Huetz et al, , 2009. Different criteria have been chosen in experimental studies that apply the discrimination analysis, but often there is not an explicit explanation of how to calculate τ * .…”

“…For D V we have chosen τ V = 2 q . This selection has been used in several previous studies (Victor and Purpura, 1996;Reich et al, 2001b;Huetz et al, 2009), but it is also common to use τ V = 1 q (e. g. Victor and Purpura, 1997;Samonds and Bonds, 2004;Huetz et al, 2006). Notice that, although for an individual pair of spikes 2/q corresponds to the maximum time shift allowed, for complete spike trains τ V = 2/q does not indicate the time scale of the time shifts actually used in the minimal cost transformation.…”

“…This affects the determination of the optimal time scale τ * as well as the significance of temporal coding quantified by Θ. However, the particular exponent used is usually not specified in experimental studies Machens et al, 2001;Aronov et al, 2003;Di Lorenzo and Victor, 2003;Samonds and Bonds, 2004;Di Lorenzo and Victor, 2007;Roussin et al, 2008;Huetz et al, 2009). When explicitly stated (Mechler et al, 1998;Macleod et al, 1998;Huetz et al, 2006), it is usually not motivated.…”

“…Discrimination analysis has mainly been applied in this context to discriminate among naturalistic sounds (Machens et al, 2001(Machens et al, , 2003Narayan et al, 2005Narayan et al, , 2006Huetz et al, 2006;Wohlgemuth and Ronacher, 2007;Huetz et al, 2009). In this context, the timedependent stimuli are regarded as a discrete set of different stimuli rather than simply different realizations from an underlying common probability distribution.…”

Efficient Encoding of Vocalizations in the Auditory Midbrain

“…The "correlation-based similarity measure" (R X ), as proposed by Schreiber et al (2003) and used by Wang et al (2007) and Huetz et al (2009), measures how well two binary spike trains of equal duration are temporally correlated. The temporal similarity between two binary spike trains, b 1 ជ and b 2 ជ, is found by first convolving each spike train with a Gaussian kernel with mean 0 and SD .…”