Coding of FM sweep trains and twitter calls in area CM of marmoset auditory cortex

Self Cite

Section: Discussionmentioning

confidence: 73%

Responses of primate frontal cortex neurons during natural vocal communication

Miller

Thomas

Nummela

et al. 2015

Self Cite

“…This result is consistent with the observation that phase locking is better for multiunits than single units (Oshurkova et al 2008), and if such a strategy were to be implemented the selection of cells with similar properties might well be based on columnar tuning. It would be interesting to see whether pooling can improve detection thresholds found with other temporally based codes, such as those based on overall spike timing (Furukawa and Middlebrooks 2002;Kajikawa et al 2008;Malone et al 2007;Wang et al 2007) and interspike interval (ISI) distributions (Imaizumi et al 2010), and to what extent pooling results using alternate codes depend on whether neurons with similar properties are sampled.…”

Section: Discussionmentioning

confidence: 99%

“…The importance of the envelope is underscored by the fact that neurons are responsive to amplitude modulation (AM) in a wide range of natural sounds and natural acoustic environments (Attias and Schreiner 1998;Chandrasekaran et al 2010;DiMattina and Wang 2006;Kajikawa et al 2008;Nagarajan et al 2002;Nelken et al 1999;Singh and Theunissen 2003). AM plays a notable role in speech perception (Delgutte et al 1998;Drullman et al 1994;Shannon et al 1995;Steinschneider et al 2003;Young 2008).…”

mentioning

confidence: 99%

Ability of primary auditory cortical neurons to detect amplitude modulation with rate and temporal codes: neurometric analysis

Johnson

Yin

O’Connor

et al. 2012

Amplitude modulation (AM) is a common feature of natural sounds, and its detection is biologically important. Even though most sounds are not fully modulated, the majority of physiological studies have focused on fully modulated (100% modulation depth) sounds. We presented AM noise at a range of modulation depths to awake macaque monkeys while recording from neurons in primary auditory cortex (A1). The ability of neurons to detect partial AM with rate and temporal codes was assessed with signal detection methods. On average, single-cell synchrony was as or more sensitive than spike count in modulation detection. Cells are less sensitive to modulation depth if tested away from their best modulation frequency, particularly for temporal measures. Mean neural modulation detection thresholds in A1 are not as sensitive as behavioral thresholds, but with phase locking the most sensitive neurons are more sensitive, suggesting that for temporal measures the lower-envelope principle cannot account for thresholds. Three methods of preanalysis pooling of spike trains (multiunit, similar to convergence from a cortical column; within cell, similar to convergence of cells with matched response properties; across cell, similar to indiscriminate convergence of cells) all result in an increase in neural sensitivity to modulation depth for both temporal and rate codes. For the across-cell method, pooling of a few dozen cells can result in detection thresholds that approximate those of the behaving animal. With synchrony measures, indiscriminate pooling results in sensitive detection of modulation frequencies between 20 and 60 Hz, suggesting that differences in AM response phase are minor in A1.

“…Although much of the psychophysical literature on FM processing has focused on the "conversion" of FM to AM (Saberi and Hafter 1995;Zwicker 1952) from a neurophysiological perspective, both AM and FM are represented by neural spiking patterns throughout the ascending auditory pathway. Despite the fact that SFM signals have commonly been used in psychophysics, neurophysiological studies have generally focused on FM sweeps (Atencio et al 2007;Godey et al 2005;Heil 1997;Kajikawa et al 2008;Mendelson et al 1993;Nelken and Versnel 2000;Qin et al 2008;Tian and Rauschecker 2004;Trujillo et al 2011;Zhang et al 2011), limiting comparisons to both the psychophysical literature and to the large corpus of neurophysiological studies of SAM processing (see Joris et al 2004 and Malone and Schreiner 2010 for review). By demonstrating that many cortical neurons effectively discriminate among SFM and SAM signals that are identical in carrier level, CF, and modulation frequency, and as similar as possible in modulation depth, our results suggest that FM is not "converted" to AM in the cortical representation.…”

Section: Modulation Frequency (Hz) Spikes Per Modulation Cyclementioning

confidence: 99%

Encoding frequency contrast in primate auditory cortex

Malone

Scott

Semple

2014

Changes in amplitude and frequency jointly determine much of the communicative significance of complex acoustic signals, including human speech. We have previously described responses of neurons in the core auditory cortex of awake rhesus macaques to sinusoidal amplitude modulation (SAM) signals. Here we report a complementary study of sinusoidal frequency modulation (SFM) in the same neurons. Responses to SFM were analogous to SAM responses in that changes in multiple parameters defining SFM stimuli (e.g., modulation frequency, modulation depth, carrier frequency) were robustly encoded in the temporal dynamics of the spike trains. For example, changes in the carrier frequency produced highly reproducible changes in shapes of the modulation period histogram, consistent with the notion that the instantaneous probability of discharge mirrors the moment-by-moment spectrum at low modulation rates. The upper limit for phase locking was similar across SAM and SFM within neurons, suggesting shared biophysical constraints on temporal processing. Using spike train classification methods, we found that neural thresholds for modulation depth discrimination are typically far lower than would be predicted from frequency tuning to static tones. This "dynamic hyperacuity" suggests a substantial central enhancement of the neural representation of frequency changes relative to the auditory periphery. Spike timing information was superior to average rate information when discriminating among SFM signals, and even when discriminating among static tones varying in frequency. This finding held even when differences in total spike count across stimuli were normalized, indicating both the primacy and generality of temporal response dynamics in cortical auditory processing.