Complex Spectral Interactions Encoded by Auditory Cortical Neurons: Relationship Between Bandwidth and Pattern

The focus of most research on auditory cortical neurons has concerned the effects of rather simple stimuli, such as pure tones or broad-band noise, or the modulation of a single acoustic parameter. Extending these findings to feature coding in more complex stimuli such as natural sounds may be difficult, however. Generalizing results from the simple to more complex case may be complicated by non-linear interactions occurring between multiple, simultaneously varying acoustic parameters in complex sounds. To examine this issue in the frequency domain, we performed a parametric study of the effects of two global features, spectral pattern (here ripple frequency) and bandwidth, on primary auditory (A1) neurons in awake macaques. Most neurons were tuned for one or both variables and most also displayed an interaction between bandwidth and pattern implying that their effects were conditional or interdependent. A spectral linear filter model was able to qualitatively reproduce the basic effects and interactions, indicating that a simple neural mechanism may be able to account for these interdependencies. Our results suggest that the behavior of most A1 neurons is likely to depend on multiple parameters, and so most are unlikely to respond independently or invariantly to specific acoustic features

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

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

Early Stages of Melody Processing: Stimulus-Sequence and Task-Dependent Neuronal Activity in Monkey Auditory Cortical Fields A1 and R

To explore the effects of acoustic and behavioral context on neuronal responses in the core of auditory cortex (fields A1 and R), two monkeys were trained on a go/no-go discrimination task in which they learned to respond selectively to a four-note target (S+) melody and withhold response to a variety of other nontarget (S−) sounds. We analyzed evoked activity from 683 units in A1/R of the trained monkeys during task performance and from 125 units in A1/R of two naive monkeys. We characterized two broad classes of neural activity that were modulated by task performance. Class I consisted of tone-sequence–sensitive enhancement and suppression responses. Enhanced or suppressed responses to specific tonal components of the S+ melody were frequently observed in trained monkeys, but enhanced responses were rarely seen in naive monkeys. Both facilitatory and suppressive responses in the trained monkeys showed a temporal pattern different from that observed in naive monkeys. Class II consisted of nonacoustic activity, characterized by a task-related component that correlated with bar release, the behavioral response leading to reward. We observed a significantly higher percentage of both Class I and Class II neurons in field R than in A1. Class I responses may help encode a long-term representation of the behaviorally salient target melody. Class II activity may reflect a variety of nonacoustic influences, such as attention, reward expectancy, somatosensory inputs, and/or motor set and may help link auditory perception and behavioral response. Both types of neuronal activity are likely to contribute to the performance of the auditory task

Coding of amplitude modulation in primary auditory cortex.

Conflicting results have led to different views about how temporal modulation is encoded in primary auditory cortex (A1). Some studies find a substantial population of neurons that change firing rate without synchronizing to temporal modulation, whereas other studies fail to see these nonsynchronized neurons. As a result, the role and scope of synchronized temporal and nonsynchronized rate codes in AM processing in A1 remains unresolved. We recorded A1 neurons' responses in awake macaques to sinusoidal AM noise. We find most (37-78%) neurons synchronize to at least one modulation frequency (MF) without exhibiting nonsynchronized responses. However, we find both exclusively nonsynchronized neurons (7-29%) and "mixed-mode" neurons (13-40%) that synchronize to at least one MF and fire nonsynchronously to at least one other. We introduce new measures for modulation encoding and temporal synchrony that can improve the analysis of how neurons encode temporal modulation. These include comparing AM responses to the responses to unmodulated sounds, and a vector strength measure that is suitable for single-trial analysis. Our data support a transformation from a temporally based population code of AM to a rate-based code as information ascends the auditory pathway. The number of mixed-mode neurons found in A1 indicates this transformation is not yet complete, and A1 neurons may carry multiplexed temporal and rate codes

Dorsal prefrontal and premotor cortex of the ferret as defned by distinctive patterns of thalamo‑cortical projections

Recent studies of the neurobiology of the dorsal frontal cortex (FC) of the ferret have illuminated its key role in the attention network, top-down cognitive control of sensory processing, and goal directed behavior. To elucidate the neuroanatomical regions of the dorsal FC, and delineate the boundary between premotor cortex (PMC) and dorsal prefrontal cortex (dPFC), we placed retrograde tracers in adult ferret dorsal FC anterior to primary motor cortex and analyzed thalamo-cortical connectivity. Cyto- and myeloarchitectural differences across dorsal FC and the distinctive projection patterns from thalamic nuclei, especially from the subnuclei of the medial dorsal (MD) nucleus and the ventral thalamic nuclear group, make it possible to clearly differentiate three separate dorsal FC fields anterior to primary motor cortex: polar dPFC (dPFCpol), dPFC, and PMC. Based on the thalamic connectivity, there is a striking similarity of the ferret's dorsal FC fields with other species. This possible homology opens up new questions for future comparative neuroanatomical and functional studies.Federal Ministry of Education & Research (BMBF) FKZ: EO 0901 Graduate School for Systemic Neurosciences Munich (GSN) Human Frontier Science Program RGY0068/2014 Wellcome Trust WT098418AIA Royal Society of London WT098418AIA CONICYT-PFCHA post-doctoral fellowship 74170109 United States Department of Health & Human Services National Institutes of Health (NIH) - USA R01-DC-00577