Dissociable Roles of the Auditory Midbrain and Cortex in Processing the Statistical Features of Natural Sound Textures.

Sound texture perception takes advantage of a hierarchy of time-averaged statistical features of acoustic stimuli, but much remains unclear about how these statistical features are processed along the auditory pathway. Here, we compared the neural representation of sound textures in the inferior colliculus (IC) and auditory cortex (AC) of anesthetized female rats. We recorded responses to texture morph stimuli that gradually add statistical features of increasingly higher complexity. For each texture, several different exemplars were synthesized using different random seeds. An analysis of transient and ongoing multiunit responses showed that the IC units were sensitive to every type of statistical feature, albeit to a varying extent. In contrast, only a small proportion of AC units were overtly sensitive to any statistical features. Differences in texture types explained more of the variance of IC neural responses than did differences in exemplars, indicating a degree of "texture type tuning" in the IC, but the same was, perhaps surprisingly, not the case for AC responses. We also evaluated the accuracy of texture type classification from single-trial population activity and found that IC responses became more informative as more summary statistics were included in the texture morphs, while for AC population responses, classification performance remained consistently very low. These results argue against the idea that AC neurons encode sound type via an overt sensitivity in neural firing rate to fine-grain spectral and temporal statistical features.

Exploring the distribution of statistical feature parameters for natural sound textures.

Sounds like "running water" and "buzzing bees" are classes of sounds which are a collective result of many similar acoustic events and are known as "sound textures". A recent psychoacoustic study using sound textures has reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the 'cochlear envelope'. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures' marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 64% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 88% of the variance in the 496 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.

Sensitivity of neural responses in the inferior colliculus to statistical features of sound textures.

Previous psychophysical studies have identified a hierarchy of time-averaged statistics which determine the identity of natural sound textures. However, it is unclear whether the neurons in the inferior colliculus (IC) are sensitive to each of these statistical features in the natural sound textures. We used 13 representative sound textures spanning the space of 3 statistics extracted from over 200 natural textures. The synthetic textures were generated by incorporating the statistical features in a step-by-step manner, in which a particular statistical feature was changed while the other statistical features remain unchanged. The extracellular activity in response to the synthetic texture stimuli was recorded in the IC of anesthetized rats. Analysis of the transient and sustained multiunit activity after each transition of statistical feature showed that the IC units were sensitive to the changes of all types of statistics, although to a varying extent. For example, we found that more neurons were sensitive to the changes in variance than that in the modulation correlations. Our results suggest that the sensitivity of the statistical features in the subcortical levels contributes to the identification and discrimination of natural sound textures.

Application of Artificial Intelligence in Pharmaceutical and Biomedical Studies.

BACKGROUND: Artificial intelligence (AI) is the way to model human intelligence to accomplish certain tasks without much intervention of human beings. The term AI was first used in 1956 with The Logic Theorist program, which was designed to simulate problem-solving ability of human beings. There have been a significant amount of research works using AI in order to determine the advantages and disadvantages of its applicabication and, future perspectives that impact different areas of society. Even the remarkable impact of AI can be transferred to the field of healthcare with its use in pharmaceutical and biomedical studies crucial for the socioeconomic development of the population in general within different studies, we can highlight those that have been conducted with the objective of treating diseases, such as cancer, neurodegenerative diseases, among others. In parallel, the long process of drug development also requires the application of AI to accelerate research in medical care. METHODS: This review is based on research material obtained from PubMed up to Jan 2020. The search terms include "artificial intelligence", "machine learning" in the context of research on pharmaceutical and biomedical applications. RESULTS: This study aimed to highlight the importance of AI in the biomedical research and also recent studies that support the use of AI to generate tools using patient data to improve outcomes. Other studies have demonstrated the use of AI to create prediction models to determine response to cancer treatment. CONCLUSION: The application of AI in the field of pharmaceutical and biomedical studies has been extensive, including cancer research, for diagnosis as well as prognosis of the disease state. It has become a tool for researchers in the management of complex data, ranging from obtaining complementary results to conventional statistical analyses. AI increases the precision in the estimation of treatment effect in cancer patients and determines prediction outcomes.