The temporal pattern of impulses in primary afferents analogously encodes touch and hearing information

An open question in neuroscience is the contribution of temporal relations between individual impulses in primary afferents in conveying sensory information. We investigated this question in touch and hearing, while looking for any shared coding scheme. In both systems, we artificially induced temporally diverse afferent impulse trains and probed the evoked perceptions in human subjects using psychophysical techniques. First, we investigated whether the temporal structure of a fixed number of impulses conveys information about the magnitude of tactile intensity. We found that clustering the impulses into periodic bursts elicited graded increases of intensity as a function of burst impulse count, even though fewer afferents were recruited throughout the longer bursts. The interval between successive bursts of peripheral neural activity (the burst-gap) has been demonstrated in our lab to be the most prominent temporal feature for coding skin vibration frequency, as opposed to either spike rate or periodicity. Given the similarities between tactile and auditory systems, second, we explored the auditory system for an equivalent neural coding strategy. By using brief acoustic pulses, we showed that the burst-gap is a shared temporal code for pitch perception between the modalities. Following this evidence of parallels in temporal frequency processing, we next assessed the perceptual frequency equivalence between the two modalities using auditory and tactile pulse stimuli of simple and complex temporal features in cross-sensory frequency discrimination experiments. Identical temporal stimulation patterns in tactile and auditory afferents produced equivalent perceived frequencies, suggesting an analogous temporal frequency computation mechanism. The new insights into encoding tactile intensity through clustering of fixed charge electric pulses into bursts suggest a novel approach to convey varying contact forces to neural interface users, requiring no modulation of either stimulation current or base pulse frequency. Increasing control of the temporal patterning of pulses in cochlear implant users might improve pitch perception and speech comprehension. The perceptual correspondence between touch and hearing not only suggests the possibility of establishing cross-modal comparison standards for robust psychophysical investigations, but also supports the plausibility of cross-sensory substitution devices

Exploring Deep Neural Network Models for Classification of High-resolution Panoramas

The objective of this thesis is to explore Deep Learning algorithms for classifying high-resolution images. While most deep learning algorithms focus on relatively low-resolution imagery (under 400×400 pixels), very high-resolution image classification poses unique challenges. These images occur in pathology and remote sensing, but here we focus on the classification of invasive plant species. We aimed to develop a computer vision system that can provide geo-coordinates of the locations of invasive plants by processing Google Map Street View images at using finite computational resources. We explore six methods for classifying these images and compare them. Our results could significantly impact the management of invasive plant species, which pose both economic and ecological threats

EARLY DETECTION OF FACTORS, INCLUDING PANDEMICS AND DISASTERS, LEADING TO LANGUAGE ENDANGERMENT: THINKING STATISTICALLY

The target of this research work is to use a statistical technique on different languages to identify significant factors of endangered languages with similar characteristics to build a model for language endangerment. Factor analysis is used to identify factors. The factors are used to construct a model with and without interaction terms. First three variables (i.e. speakers, longitude and latitude) are analyzed to identify two factors and then these three variables and three interaction terms are used to construct the model. Different variables were identified and a model with and without interaction terms is built using the identified factors. The result shows that the model has significant predictive power. The predictors were retrieved from the dataset. The outcome encourages future studies towards defining techniques of language endangerment prediction for analyzing factors of language endangerment

Animation: From Black Screen to Green Screen

In my research proposal, this research is aimed to study the evolution of Green Screen/CHROMA and the techniques used for doing CHROMA. Second focus of the paper is to study the challenges faced by VFX industry in India