- 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
The ability to categorize and recognize sounds is an amazing trait of the mammalian brain. We can easily recognize a familiar voice, identify a musical tune, or segregate a single speaker in a crowded room. Yet these seemingly simple tasks present major challenges to our most sophisticated computers, which fail miserably at such sound recognition tasks especially in the presence of background noise. My laboratory uses large-scale neural recordings, computational models, machine learning and statistical inference to explore how the brain achieves such seemingly simple feats. By understanding the computational principles performed during natural hearing, we hope to develop superior sound recognition technologies and treatment strategies that reverse the detrimental effects of hearing loss.
Lab Website: http://escabilab.uconn.edu
● L.M. Miller, M.A. Escabí, H.L. Read, and C.E. Schreiner (2001). Functional convergence of response properties in the auditory thalamocortical system. Neuron 32(1): 151-60. (PDF)
● L.M. Miller, M.A. Escabí, H.L. Read, and C.E. Schreiner (2002). Spectrotemporal receptive fields in the lemniscal auditory thalamus and cortex. J Neurophysiol 87(1): 516-27. (PDF)
● M.A. Escabí and C.E. Schreiner (2002). Nonlinear spectrotemporal sound analysis by neurons in the auditory midbrain. J Neurosci 22(10): 4114-31. (PDF)
● Escabí MA, Miller LM, Read HL, Schreiner CE. (2003) Naturalistic auditory contrast improves spectrotemporal coding in the cat inferior colliculus. J Neurosci. 23 (37):11489-504. (PDF)
● Y. Zheng and M.A. Escabi (2008) Distinct roles for onset and sustained activity in the neural code for temporal periodicity and acoustic envelope shape. J Neurosci. 28(52):14230–44.
● F.A. Rodríguez, H.L. Read, M.A. Escabí (2010) Spectrotemporal Modulation Tradeoff Along the Tonotopic Axis of the Inferior Colliculus. Journal of Neurophysiol. 103: 887-903.
● F.A. Rodriguez, C. Chen, H.L. Read & M.A. Escabi. (2010) Neural modulation tuning characteristics scale to efficiently encode natural sound statistics. J Neurosci 30, 15969-15980.
● Chen C, Read HL, Escabí MA (2012). Precise feature based time scales and frequency decorrelation lead to a sparse auditory code. J Neurosci. 20;32(25):8454-68.
● Y. Zheng and M.A. Escabi (2013) Proportional spike-timing precision and firing reliability underlie efficient temporal processing of periodicity and envelope shape cues. J Neurophysiology. 110(3):587-606.
● Escabí MA, Read HL, Viventi J, Kim DH, Higgins NC, Storace DA, Liu AS, Gifford AM, Burke JF, Campisi M, Kim YS, Avrin AE, Spiegel Jan Vd, Huang Y, Li M, Wu J, Rogers JA, Litt B, Cohen YE. A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings. J Neurophysiol. 2014 Sep 15;112(6):1566-83. doi: 10.1152/jn.00179.2013.
National Science Foundation, $890,842.
- H.L. Read (PI), M.A. Escabi (Co-PI). Cortical Specializations for Behavioral Discrimination of Temporal Shape and Rhythm of Sound (NSF 1355065). Aug 2014-July 2018.
National Institutes of Health (NIDCD), $1,448,437.
- Monty Escabí (PI), Heather L. Read (Co-PI). CRCNS: The role of statistical regularities for neural discrimination and coding of sounds (1R01DC015138-01), Aug. 2015-Jul. 2020.
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