Auditory Cortex Of Squirrel Monkeys Research Articles

Background noise exerts diverse effects on the cortical encoding of foreground sounds.

In natural listening conditions, many sounds must be detected and identified in the context of competing sound sources, which function as background noise. Traditionally, noise is thought to degrade the cortical representation of sounds by suppressing responses and increasing response variability. However, recent studies of neural network models and brain slices have shown that background synaptic noise can improve the detection of signals. Because acoustic noise affects the synaptic background activity of cortical networks, it may improve the cortical responses to signals. We used spike train decoding techniques to determine the functional effects of a continuous white noise background on the responses of clusters of neurons in auditory cortex to foreground signals, specifically frequency-modulated sweeps (FMs) of different velocities, directions, and amplitudes. Whereas the addition of noise progressively suppressed the FM responses of some cortical sites in the core fields with decreasing signal-to-noise ratios (SNRs), the stimulus representation remained robust or was even significantly enhanced at specific SNRs in many others. Even though the background noise level was typically not explicitly encoded in cortical responses, significant information about noise context could be decoded from cortical responses on the basis of how the neural representation of the foreground sweeps was affected. These findings demonstrate significant diversity in signal in noise processing even within the core auditory fields that could support noise-robust hearing across a wide range of listening conditions.NEW & NOTEWORTHY The ability to detect and discriminate sounds in background noise is critical for our ability to communicate. The neural basis of robust perceptual performance in noise is not well understood. We identified neuronal populations in core auditory cortex of squirrel monkeys that differ in how they process foreground signals in background noise and that may contribute to robust signal representation and discrimination in acoustic environments with prominent background noise.

Processing of twitter-call fundamental frequencies in insula and auditory cortex of squirrel monkeys.

Amplitude-modulated (AM) and frequency-modulated (FM) elements are prominent periodic sound features of squirrel monkeys' twitter calls. To investigate how the periodic FM elements are represented in the spike activity of cortical neurons, single units in the insula, primary auditory field (AI) and rostral auditory field (R) were recorded. In five monkeys, 566 units (insula, n = 181; AI, n = 221; R, n = 164) were exposed to synthesized fundamental frequencies and one natural twitter call. Neuronal encoding of periodic FM elements takes place by phase-locking to either the up- or the down-directed FM sweeps. The phase-locking was strongly influenced by the FM-period repetition rate. The ability of neurons in both auditory fields and the insula to encode all periodic FM elements showed a marked reduction at 16 Hz FM-period repetition rate. The neurons' best frequency (BF) influenced the quality of periodicity encoding, but neurons with BFs outside the frequency range of the fundamentals also responded with periodic discharge rates. Even neurons in AI (6.8%) and the insula (22.6%) that did not respond to pure tones showed clear periodic FM encoding. The percentage of neurons able to encode all periodic FM elements within the twitter fundamental was significantly higher in field R than in AI and the insula. From 58 simultaneously recorded pairs of units in AI and the insula that had positive cross-correlation coefficients of spontaneous activity, the influence of the FM-period repetition rate on neuronal correlation was investigated. Correlated firing of AI and insula neurons seems limited to low-period repetition rates. The cross-correlation coefficients obtained for spontaneous activity and six different periodic FM sounds showed a band-pass characteristic. The natural twitter call evoked stronger neuronal responses in all fields than the synthesized fundamental frequencies with corresponding bi-directional FM sweeps. The better encoding of the transient features in the natural call can be attributed to the amplitude modulation added to the FM elements in the natural call. These amplitude modulations divide the FM elements of twitter calls into syllable-like sound elements. It is probable that encoding the complex pattern in the time and frequency domains of a call must undergo some integration at a cortical level. Additionally, these data provide the first evidence that insula neurons contribute to the encoding of complex FM signals.

Searching for neural correlates of the hearing sensation fluctuation strength in the auditory cortex of squirrel monkeys.

Sounds with slow (less than 20 Hz) fluctuations may elicit the hearing sensation fluctuation strength. For AM tones, neural correlates of fluctuation strength were searched in the auditory cortex of unanesthetized squirrel monkeys. To enable a comparison of psychophysical and physiological data, the 'modulation' of the peristimulus time histogram was fitted by a sinusoidal function. The dependence of the amplitude of this function on modulation frequency, modulation depth and sound pressure level was often comparable to the dependence of fluctuation strength on the same stimulus parameters. In particular, as a function of modulation frequency, the neural data also show a bandpass characteristic at low modulation frequencies as was found for the hearing sensation fluctuation strength.

Lability in the responses of cells in the auditory cortex of squirrel monkeys to species-specific vocalizations.

The activity of 28 cells located mainly in the secondary auditory cortex (A II) of awake squirrel-monkeys, was extracellularly recorded for periods of up to 6 h. Seven different species-specific vocalizations, which were repeatedly presented to the monkey, were used as auditory stimuli. Twenty-six cells responded, at least once, to one or more vocalizations; 22 cells revealed some change in their response (pattern or strength) to at least one vocalization ("change in response"). Twenty-one cells exhibited a change in the number and/or type of vocalization to which they responded during the recording period ("change in selectivity"). At some time during the recording period all the responding cells exhibited a "change in response" and/or a "change in selectivity" ("change in responsiveness"). A "change in response" of a cell to a vocalization did not necessarily exclude a "change in selectivity", associated with the same vocalization, later in time and vice-versa. A "change in responsiveness" to one vocalization was not necessarily correlated with "changes in responsiveness" to other vocalizations.

Responses of single neurons in the auditory cortex of squirrel monkeys to variants of a single call type

Responses of single neurons in the auditory cortex of squirrel monkeys to variants of a single call type

Multiple coding of species-specific vocalizations in the auditory cortex of squirrel monkeys

Eighty-three cells in the superior temporal gyrus of awake squirrel monkeys were tested with 12 different species-specific vocalizations; 75 (90%) responded to one or more calls. A wide range of selectivity and response probabilities were seen. Eighty-nine percent of the cells responded to more than half of the vocalizations. In only one case did a cell respond to only one call type (‘peep’). This cell also responded to steady tone bursts, but only in a narrow range of frequencies approximating the major band of acoustic energy in peeps. In most cases, no obvious acoustic relationships were seen between calls effective for a given cell. Likewise, in most cases the frequency range over which a cell responded to steady tone bursts did not readily explain responses to vocalizations. More than half of the cells discriminated between one or more pairs of acoustically similar vocalizations. The response patterns of different cells to the same vocalization showed considerable variety. This was especially true for responses to the temporally more complex calls. The variety of responses given by different cells to the same vocalization suggests that no vocalization is coded by one unique discharge pattern. Our results suggest that many neurons in the STG do not select between different classes of vocalizations according to presence or absence of simple acoustic features.

Coding of Species-Specific Vocalizations by the Squirrel Monkey Auditory Cortex

Single neurons in the auditory cortex of squirrel monkeys (Saimiri sciureus) were tested with tape-recorded vocalizations representing this species' six major call groups. A small minority of these cells were highly selective, responding only to one or two call types. For these cells, a role in categorizing discrete call types appears likely. Some selective cells behaved as “tuned filters,” by virtue of the correlation between the best frequency of steady tone bursts for a cell and the major acoustic energy in the effective call type. Most of the neurons responded to several vocalizations. A simple scheme of “feature extraction” does not appear to explain readily the responses of these cells: (a) calls effective for a given cell usually represented structurally different call groups and did not share the same simple acoustic features; (b) many cells responding across call group boundaries did not respond to all the calls of an effective call group; (c) while most of the neurons responded to steady tone bursts, their responses to tones were generally not satisfactory indicators of their responsiveness to vocalizations; (d) in some cases, the nature of a cell's response was shown to depend upon interactions of nonadjacent parts of a vocalization.

Auditory cortex of squirrel monkey: response patterns of single cells to species-specific vocalizations.

Most of the neurons tested in the superior temporal cortex of awake squirrel monkeys responded to recorded species-specific vocalizations. Some cells responded with temporally complex patterns to many vocalizations. Other cells responded with simpler patterns to only one call. Most cells lay between these two extremes. On-line deletion of parts of a vocalization revealed the role of temporal interactions in determining the nature of some responses.