Millisecond encoding precision of auditory cortex neurons

Abstract

Neurons in auditory cortex are central to our perception of sounds. However, the underlying neural codes, and the relevance of millisecond-precise spike timing in particular, remain debated. Here, we addressed this issue in the auditory cortex of alert nonhuman primates by quantifying the amount of information carried by precise spike timing about complex sounds presented for extended periods of time (random tone sequences and natural sounds). We investigated the dependence of stimulus information on the temporal precision at which spike times were registered and found that registering spikes at a precision coarser than a few milliseconds significantly reduced the encoded information. This dependence demonstrates that auditory cortex neurons can carry stimulus information at high temporal precision. In addition, we found that the main determinant of finely timed information was rapid modulation of the firing rate, whereas higher-order correlations between spike times contributed negligibly. Although the neural coding precision was high for random tone sequences and natural sounds, the information lost at a precision coarser than a few milliseconds was higher for the stimulus sequence that varied on a faster time scale (random tones), suggesting that the precision of cortical firing depends on the stimulus dynamics. Together, these results provide a neural substrate for recently reported behavioral relevance of precisely timed activity patterns with auditory cortex. In addition, they highlight the importance of millisecond-precise neural coding as general functional principle of auditory processing-from the periphery to cortex.hearing | neural code | spike timing | natural sounds | mutual information O ur auditory system can reliably encode natural sounds that vary simultaneously over many time scales, and from these sounds, it can extract behaviorally relevant information such as a speaker's identity or the sound qualities of musical instruments (1, 2). Neurons in the auditory cortex are sensitive to temporally structured acoustic stimuli and likely play a central role in mediating their perception (3). Yet, how exactly auditory cortex neurons use their temporal patterns of action potentials (spikes) to provide an information-rich representation of complex or natural sounds remains debated.One central question pertains to the relevance of millisecondprecise spike times of individual auditory cortical neurons for stimulus encoding. Previous work has shown that onset responses to isolated brief sounds can be millisecond-precise and carry acoustic information (4-6). However, auditory cortex neurons cannot lock their responses to rapid sequences of short stimuli. For example, during the repetitive presentation of brief sounds, their response precision degrades when the intersound interval becomes shorter than ≈10-20 ms. This finding argues against a role of millisecond-precise spike timing in the encoding of fast sequences of short sounds (7,8). In addition, studies on the decoding of complex sounds from single-tria...

Neurons with Stereotyped and Rapid Responses Provide a Reference Frame for Relative Temporal Coding in Primate Auditory Cortex

“…Natural sounds vary on multiple time scales, especially fast ones, and neurons in auditory areas represent them by finely timed responses (Liu et al, 2006;Engineer et al, 2008;Wang et al, 2008;Kayser et al, 2010). Our ability to detect and recognize these sounds suggests that the auditory system features mechanisms to extract information from precisely timed responses, such as those found in auditory cortex (Yang et al, 2008;Sharpee et al, 2011).…”

“…We performed a separate analysis using a framework of stimulus decoding that al-lowed us to consider longer time windows and to obtain estimates of cumulative stimulus information. Specifically, we used a linear discriminant decoder in conjunction with a leave-one-out cross-validation procedure (Nelken and Chechik, 2007;Russ et al, 2008;Kayser et al, 2010). For each individual trial of a given stimulus (s i ), this proceeded as follows.…”

“…In auditory cortex, for example, ethological stimuli such as vocalizations or speech are encoded by precisely timed responses of individual neurons on the few tens of millisecond scale (Nelken et al, 2005;Chechik et al, 2006;Russ et al, 2008). Importantly, this information is considerably reduced when integrating the same responses over much longer time scales, e.g., when considering spike counts in windows of several tens of milliseconds (Schnupp et al, 2006;Engineer et al, 2008;Kayser et al, 2010). These results, together with the high information provided by response onset latencies about the sound frequency of tones or noises (Nelken et al, 2005;Bizley et al, 2010), support the importance of time-dependent responses for auditory information processing.…”

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

“…MI has been used to compare different neural response codes [Ince et al, 2013; Kayser et al, 2009; Reich et al, 2001], characterize different neurons [Sharpee, 2014] as well as quantify the effect of correlations between neurons [Ince et al, 2009, 2010; Moreno‐Bote et al, 2014] and the importance of spike timing [Kayser et al, 2010; Nemenman et al, 2008; Panzeri et al, 2001]. Recent studies have begun to explore its application to neuroimaging [Afshin‐Pour et al, 2011; Caballero‐Gaudes et al, 2013; Gross et al, 2013; Guggenmos et al, 2015; Ostwald and Bagshaw, 2011; Panzeri et al, 2008; Salvador et al, 2007; Saproo and Serences, 2010; Schyns et al, 2011; Serences et al, 2009].…”

Seasonal Plasticity of Precise Spike Timing in the Avian Auditory System

“…Despite decades of work establishing the importance of precise spike timing in the auditory nerve (Kiang, 1965;Rose et al, 1967;Young and Sachs, 1979), the role of temporal coding has only recently been explored in more central regions: midbrain (Holmstrom et al, 2010;Woolley and Portfors, 2013), thalamus (Huetz et al, 2009), and forebrain (Narayan et al, 2006;Schnupp et al, 2006;Engineer et al, 2008;Kayser et al, 2010).…”