Brian Malone (UCSF) on Separating Signal From Noise in Auditory Cortex
Date:
Fri, 10/19/2018 - 10:30am - 12:00pm
Location:
Seminar Room at the Knoll
Event Type:
Hearing Seminar Thus neural networks do adapt to background noise to maintain useful representations better than current ML models. Brian's findings show that neural processing in the absence of noise can be very different from processing in the presence of noise and that some neurons specialize in low-SNR processing. He's promised to highlight a few ideas that may be applicable to ML models operating in noise.
Who: Brian Malone (UCSF)
What: Separating Signal From Noise in Auditory Cortex
When: Date: Fri, 10/19/2018 - 10:30am - 12:00pm
Where: Seminar Room at the Knoll
Why: When is noise useful??!?!?
I promise there will be more signal than noise at this Friday’s CCRMA Hearing Seminar. We will talk about how noise can help our perception.
- Malcolm
Abstract
In natural listening conditions, many sounds must be detected and identified in the context of competing sound sources, which function as background noise. Traditionally, noise is thought to degrade the cortical representation of sounds by suppressing responses and increasing response variability. However, recent studies of neural network models and brain slices have shown that background synaptic noise can improve the detection of signals. Because acoustic noise affects the synaptic background activity of cortical networks, it may improve the cortical responses to signals. We used spike train decoding techniques to determine the functional effects of a continuous white noise background on the responses of clusters of neurons in auditory cortex to foreground signals, specifically frequency-modulated sweeps (FMs) of different velocities, directions, and amplitudes. Whereas the addition of noise progressively suppressed the FM responses of some cortical sites in the core fields with decreasing signal-to-noise ratios (SNRs), the stimulus representation remained robust or was even significantly enhanced at specific SNRs in many others. Even though the background noise level was typically not explicitly encoded in cortical responses, significant information about noise context could be decoded from cortical responses on the basis of how the neural representation of the foreground sweeps was affected. These findings demonstrate significant diversity in signal in noise processing even within the core auditory fields that could support noise-robust hearing across a wide range of listening conditions.
NEW & NOTEWORTHY The ability to detect and discriminate sounds in background noise is critical for our ability to communicate. The neural basis of robust perceptual performance in noise is not well understood. We identified neuronal populations in core auditory cortex of squirrel monkeys that differ in how they process foreground signals in background noise and that may contribute to robust signal representation and discrimination in acoustic environments with prominent background noise.
Biography
Brian got his PhD in Neural Science at The Center for Neural Science at NYU with Malcolm Semple working on audition, with an emphasis on the neural representation of acoustic context. He did postdoctoral work with Dario Ringach at UCLA studying receptive field dynamics in primary visual cortex. He then joined Christoph Schreiner’s lab at UCSF as a Research Scientist, and joined the faculty as an Assistant Professor after receiving an R01 to study nonlinear features of auditory encoding of complex sounds.
FREE
Open to the Public