Humans and other mammals are able to recognize and discriminate sounds even when masked by substantial irrelevant noise. Although this process is often effortless for animals, common sources of environmental noise severely confound automatic speech processors and distort the output of hearing aids and prosthetics. Understanding how complex noisy sounds are processed in central brain areas can provide critical insights into how to address these ongoing challenges. The goal of this project is to study cortical responses to naturalistic noisy auditory stimuli in order to understand neurophysiological mechanisms for the robust perception of noisy signals. Initial experiments will study automatic enhancement of natural signals in neural representations during passive listening. These experiments will focus specifically on environmental noise that challenges engineered auditory processing systems. Further experiments will study how neuronal mechanisms facilitate this process when selective attention is directed to auditory and multisensory audio-visual features. Computational analysis will be used to understand the algorithms employed by single neurons and neural populations to enhance the representation of important signals. In addition to revealing basic neural mechanisms of sensory processing, these experiments will provide insight into how sound processors can be improved for hearing-impaired patients.
The benefits of hearing aids and prosthetics are often limited by common environmental noise, which can severely distort their outputs. In contrast, normal-hearing humans and other mammals are exquisitely adept at recognizing complex sounds, even in very noisy conditions. We propose to study how the brain processes noisy sounds in order to understand the neural mechanisms underlying this remarkable ability and to learn how sound processors might be improved for hearing-impaired patients.
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|McGinley, Matthew J; David, Stephen V; McCormick, David A (2015) Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection. Neuron 87:179-92|
|Thorson, Ivar L; Liénard, Jean; David, Stephen V (2015) The Essential Complexity of Auditory Receptive Fields. PLoS Comput Biol 11:e1004628|
|Slee, Sean J; David, Stephen V (2015) Rapid Task-Related Plasticity of Spectrotemporal Receptive Fields in the Auditory Midbrain. J Neurosci 35:13090-102|
|Atiani, Serin; David, Stephen V; Elgueda, Diego et al. (2014) Emergent selectivity for task-relevant stimuli in higher-order auditory cortex. Neuron 82:486-99|
|Mesgarani, Nima; David, Stephen V; Fritz, Jonathan B et al. (2014) Mechanisms of noise robust representation of speech in primary auditory cortex. Proc Natl Acad Sci U S A 111:6792-7|
|Englitz, B; David, S V; Sorenson, M D et al. (2013) MANTA--an open-source, high density electrophysiology recording suite for MATLAB. Front Neural Circuits 7:69|
|Englitz, Bernhard; Akram, S; David, S V et al. (2013) Putting the tritone paradox into context: insights from neural population decoding and human psychophysics. Adv Exp Med Biol 787:157-64|
|David, Stephen V; Shamma, Shihab A (2013) Integration over multiple timescales in primary auditory cortex. J Neurosci 33:19154-66|
|Klampfl, Stefan; David, Stephen V; Yin, Pingbo et al. (2012) A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons. J Neurophysiol 108:1366-80|
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