Contextual effects of noise on vocalization encoding in primary auditory cortex.

Robust auditory perception plays a pivotal function for processing behaviorally relevant sounds, particularly with distractions from the environment. The neuronal coding enabling this ability, however, is still not well understood. In this study, we recorded single-unit activity from the primary auditory cortex (A1) of awake marmoset monkeys (Callithrix jacchus) while delivering conspecific vocalizations degraded by two different background noises: broadband white noise and vocalization babble. Noise effects on neural representation of target vocalizations were quantified by measuring the responses' similarity to those elicited by natural vocalizations as a function of signal-to-noise ratio. A clustering approach was used to describe the range of response profiles by reducing the population responses to a summary of four response classes (robust, balanced, insensitive, and brittle) under both noise conditions. This clustering approach revealed that, on average, approximately two-thirds of the neurons change their response class when encountering different noises. Therefore, the distortion induced by one particular masking background in single-unit responses is not necessarily predictable from that induced by another, suggesting the low likelihood of a unique group of noise-invariant neurons across different background conditions in A1. Regarding noise influence on neural activities, the brittle response group showed addition of spiking activity both within and between phrases of vocalizations relative to clean vocalizations, whereas the other groups generally showed spiking activity suppression within phrases, and the alteration between phrases was noise dependent. Overall, the variable single-unit responses, yet consistent response types, imply that primate A1 performs scene analysis through the collective activity of multiple neurons. NEW & NOTEWORTHY: The understanding of where and how auditory scene analysis is accomplished is of broad interest to neuroscientists. In this paper, we systematically investigated neuronal coding of multiple vocalizations degraded by two distinct noises at various signal-to-noise ratios in nonhuman primates. In the process, we uncovered heterogeneity of single-unit representations for different auditory scenes yet homogeneity of responses across the population.

Spontaneous activity is correlated with coding density in primary auditory cortex.

Sensory neurons across sensory modalities and specific processing areas have diverse levels of spontaneous firing rates (SFRs) in the absence of sensory stimuli. However, the functional significance of this spontaneous activity is not well-understood. Previous studies in the auditory system have demonstrated that different levels of spontaneous activity are correlated with a variety of physiological and anatomic properties, suggesting that neurons with differing SFRs make unique contributions to the encoding of auditory stimuli. Additionally, altered SFRs are a correlate of tinnitus, arising in several auditory areas after exposure to ototoxic substances and noise trauma. In this study, we recorded single-unit activity from primary auditory cortex of awake marmoset monkeys while delivering wide-band random-spectrum stimuli and white Gaussian noise (WGN) to examine any divergences in stimulus encoding properties across SFR classes. We found that higher levels of spontaneous activity were associated with both higher levels of activation relative to suppression across a variety of wide-band stimuli and higher driven rates in response to WGN. Moreover, response latencies to WGN were negatively correlated with the level of activation in response to both stimulus types. These findings are consistent with a novel view of the role spontaneous spiking may play during normal stimulus processing in primary auditory cortex and how it may malfunction in cases of tinnitus.

Modeling of topology-dependent neural network plasticity induced by activity-dependent electrical stimulation.

Activity-dependent electrical stimulation can induce cerebrocortical reorganization in vivo by activating brain areas using stimulation derived from the statistics of neural or muscular activity. Due to the nature of synaptic plasticity, network topology is likely to influence the effectiveness of this type of neuromodulation, yet its effect under different network topologies is unclear. To address this issue, we simulated small-scale three-neuron networks to explore topology-dependent network plasticity. The induced neuroplastic changes were evaluated by network coherence and unit-pair mutual information measures. We demonstrated that involvement of monosynaptic feedforward and reciprocal connections is more likely to lead to persistent decreased network coherence and increased network mutual information independent of the global network topology. On the contrary, disynaptic feedforward connections exhibit heterogeneous coherence and unit-pair mutual information sensitivity that depends strongly upon the network context.