University of Connecticut University of UC Title Fallback Connecticut


Monty A. Escabi


  • B.S., Electrical Engineering, Florida International University, 1993
  • M.S., Electrical Engineering: Signal Processing and Stochastic Modeling, Columbia University, 1995
  • Ph. D., Bioengineering, University of California at Berkeley and San Francisco, 2000

Research Interests:
The overall goal of my research is to understand how the central nervous system processes complex sounds in their environment, such as speech, back-ground noise and animal vocalizations. It is very easy for animals (including humans) to detect and discriminate vocalizations in the midst of high background noise and yet it is still not clear how this process is achieved within the central nervous system. Artificial systems for voice recognition and hearing aids fail miserably at this simple task. Currently I am using a number of techniques to gain insight into this basic process.

The bulk of my work deals with performing neurophysiologic recordings of single neuron activity in response to complex synthetic stimuli. I use techniques derived from signal processing, systems identification, control systems, and information theory in order to characterize how auditory neurons process spectral and temporal acoustic information. Most of this work is performed in three distinct brain regions: the inferior colliculus (ICC), the auditory thalamus (MGBv) and the primary auditory cortex (AI). Presently I am interested in how 1) acoustic signals are encoded by single neuron activity in the central auditory system, 2) how the activity of single neurons gives rise to spatially distributed representations or “sensory maps” for acoustic signal attributes, 3) how these representations are transformed from one auditory station to the other, and 4) how this functional organization relates to the anatomical substrate.

I am also attempting to link physiologic results with psychoacoustic findings in humans. I use complex spectro-temporal sound sequences to gain insight into the ability of humans to discriminate and detect temporal and spectral acoustic information. This is important since pertinent sound, such as speech, are largely composed of both spectral and temporal acoustic features. I am using the knowledge gained from these studies to design computational algorhithms for pre-processing sounds in artificial hearing devices.