University of Connecticut University of UC Title Fallback Connecticut

Postdoctoral Position in Auditory Neuroscience

Position: The Physiological Acoustics Lab ( at the University of Connecticut seeks applicants for a postdoctoral position in auditory neuroscience. We are seeking highly motivated applicants that can lead a project on coding of natural sound in the auditory midbrain and cortex of awake animals. The project involves performing large-scale neural array and tetrode recordings in awake animals to characterize how neural populations encode and categorize natural sounds and to identify the neural transformations between the auditory midbrain and cortex. The primary appointment will be in Biomedical Engineering, but the work will be conducted in collaboration with Psychology department (Statistical Neuroscience Lab,; Sensory Perception and Neuroscience Lab, and Electrical and Computer Engineering.

Qualifications: A PhD in Neuroscience, Biomedical Engineering, Electrical Engineering, or related field is required. The ideal candidate will have an interdisciplinary background with prior research experience in animal neurophysiology. Experience with MATLAB and a strong analytic background in neural data analysis is desirable, particularly analyzing datasets from large-scale multi-channel neural recordings.

Appointment: The position is funded through an R01 and is available immediately. Salaries follow NIH post-doctoral scale and are based on experience. Applicants should email “escabi at engr dot uconn dot edu” a single PDF file containing:


1) a resume including past research experience and published work

2) a one page statement of prior research experience

3) a one page statement of future research interests and objectives

4) the names of at least two individuals who can provide reference letters.

Escabi, Read and Stevenson receive 5 year NIH grant in computational neuroscience

CRCNS: The role of sound statistics for neural discrimination and coding of sounds

The brains ability to accurately recognize and categorize sounds despite highly variable acoustic structure and in the presence of environmental noises is a remarkable trait of the normal functioning auditory system. How the brain achieves this seemingly simple task is poorly understood and new working models are necessary to understand how the brain deals with and exploits statistical structure for basic auditory recognition tasks. Since background noise and acoustic variability present significant challenges for individuals with hearing loss, this knowledge is essential to develop biologically informed auditory prosthetic that account for normal acoustic variability and can deemphasize unwanted background noise.

This study proposes a novel framework to understand how the normal auditory system encodes high-level statistical regularities in sounds that are essential for sound discrimination and recognition behavior. Computational approaches will be used to identify salient high-level statistical features in sounds that contribute to sound recognition. Neural recordings in two critical mammalian auditory structures (inferior colliculus, IC; auditory
cortex, AC) of the awake rabbit will be obtained to identify neural coding mechanisms that exploit statistical structure for sound recognition. The study will provide the groundwork for developing a general theory of how the brain encodes and discriminates sounds based on high-order statistical features and will enable future auditory prosthetic that complement normal hearing function.