Current Research Interests
The Natural Sounds and Neural Coding Laboratory investigates the processing of complex natural sounds using a combination of experimental and computational techniques. Electrophysiological techniques are used to record neural responses from hierarchical stages of auditory processing. Theoretical method from areas such as statistical signal processing, systems theory, probability theory, information theory and pattern recognition are applied to characterize how neurons in the brain encode natural sounds. Computational models are constructed to understand the processing of natural sounds both at the single neuron and the network level, to model neural selectivity and discrimination, and to explore brain-inspired sound processing techniques for improving hearing assistive devices.
Our current focus is on Cortical Circuits that enable Complex Scene Analysis (CSA). Our overarching vision is to integrate research and cutting-edge technologies at the intersection of neuroscience, photonics, computing, and engineering to unravel the cortical circuits underlying CSA; model and engineer these circuits; and then harness the computational power of these circuits in a brain-inspired algorithm.
Decoding Auditory Attention:
Decoding auditory attention using a combination of multimodal neuroimaging (fNIRS and EEG) and machine learning approaches.
Cortical Circuits for Complex Scene Analysis:
Unraveling the underlying neural circuitry responsible for the brain’s remarkable ability to selectively attend to specific objects in acoustically complex scenes.
Brain-Inspired Audio Processing Algorithms:
Developing and testing brain inspired audio processing algorithms and designed to isolate intended sound sources in complex scenes.
Jio Nocon, Ph.D.