Interaural intensity disparities (IIDs), the cues all animals use to localize high frequency sounds, are initially processed in the lateral superior olive (LSO) by a subtractive process where inputs from one ear excite and inputs from the other ear inhibit LSO neurons. Such cells are called excitatory-inhibitory (EI) neurons and are prominent not only in the LSO but also in higher nuclei, which include the dorsal nucleus of the lateral lemniscus (DNLL) and inferior colliculus (IC). The IC is of particular interest since its EI cells receive diverse innervation patterns from a large number of lower nuclei, which include the DNLLs and LSOs, and thus comprise a population with diverse binaural properties. The first part of this review focuses on the circuits that create EI cells in the LSO, DNLL and IC. The second section then turns to the responses evoked by dynamic IIDs that change over time, as with multiple sounds that emanate from different regions of space or moving sound sources. I show that many EI neurons in the IC respond to dynamic IIDs in ways that are not predictable from their responses to static IIDs, IIDs presented one at a time. In the final section, results from in vivo whole cell recording in the IC are presented and address the connectional basis for the responsiveness to dynamic IIDs. The principal conclusion is that EI cells comprise a diverse population. The diversity is created by the particular set of inputs each EI type receives and is expressed in the differences in the responses to dynamic IIDs that are generated by those inputs. These results show that the construction of EI neurons in the IC imparts features that not only encode the location of an individual sound source, but also that allow animals to determine the direction of a moving sound and to focus and localize a single sound in midst of many sounds, as typically occurs in the daily lives of all animals.
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http://dx.doi.org/10.1016/j.heares.2012.01.011 | DOI Listing |
Sci Rep
September 2024
Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamil Nadu, India.
Smart cities have developed advanced technology that improves people's lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS.
View Article and Find Full Text PDFEnviron Res
August 2024
Indian Institute of Remote Sensing, Dehradun, Uttarakhand-248001, India.
The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas.
View Article and Find Full Text PDFSci Total Environ
January 2024
Ecopath International Initiative (EII), Barcelona, Spain.
In the Western Mediterranean Sea, forage fishes have changed in abundance, body condition, growth, reproduction, and distribution in the last decades. Different hypotheses have been proposed to explain these changes, including increase in fishing mortality; changes in environmental conditions affecting species fitness, and planktonic productivity and quality; recovery of top predators; and increase in competitors. We investigated the main drivers and changes of the pelagic ecosystem and their effects using an ecosystem-based modelling approach.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
March 2023
Department of Irrigation and Drainage Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India.
Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models.
View Article and Find Full Text PDFJ Biomol Struct Dyn
October 2022
Centre of Bioinformatics, IIDS, University of Allahabad, Allahabad, Uttar Pradesh, India.
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