Acute silencing of hippocampal CA3 reveals a dominant role in place field responses.

Neurons in hippocampal output area CA1 are thought to exhibit redundancy across cortical and hippocampal inputs. Here we show instead that acute silencing of CA3 terminals drastically reduces place field responses for many CA1 neurons, while a smaller number are unaffected or have increased responses. These results imply that CA3 is the predominant driver of CA1 place cells under normal conditions, while also revealing heterogeneity in input dominance across cells

Complexity Scaling for Speech Denoising

Computational complexity is critical when deploying deep learning-based speech denoising models for on-device applications. Most prior research focused on optimizing model architectures to meet specific computational cost constraints, often creating distinct neural network architectures for different complexity limitations. This study conducts complexity scaling for speech denoising tasks, aiming to consolidate models with various complexities into a unified architecture. We present a Multi-Path Transform-based (MPT) architecture to handle both low- and high-complexity scenarios. A series of MPT networks present high performance covering a wide range of computational complexities on the DNS challenge dataset. Moreover, inspired by the scaling experiments in natural language processing, we explore the empirical relationship between model performance and computational cost on the denoising task. As the complexity number of multiply-accumulate operations (MACs) is scaled from 50M/s to 15G/s on MPT networks, we observe a linear increase in the values of PESQ-WB and SI-SNR, proportional to the logarithm of MACs, which might contribute to the understanding and application of complexity scaling in speech denoising tasks.Comment: Submitted to ICASSP202

A Noise-Robust Method with Smoothed \ell_1/\ell_2 Regularization for Sparse Moving-Source Mapping

The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smooth \ell_1/\ell_2 regularization term. As the mean of the noise in the power spectrum domain is dependent on its variance in the time domain, the proposed method includes a variance estimation step, which allows more robust blind deconvolution. Validation of the method on both simulated and real data, and of its performance, are compared with two well-known methods from the literature: the deconvolution approach for the mapping of acoustic sources, and sound density modeling

Locating and extracting acoustic and neural signals

This dissertation presents innovate methodologies for locating, extracting, and separating multiple incoherent sound sources in three-dimensional (3D) space; and applications of the time reversal (TR) algorithm to pinpoint the hyper active neural activities inside the brain auditory structure that are correlated to the tinnitus pathology. Specifically, an acoustic modeling based method is developed for locating arbitrary and incoherent sound sources in 3D space in real time by using a minimal number of microphones, and the Point Source Separation (PSS) method is developed for extracting target signals from directly measured mixed signals. Combining these two approaches leads to a novel technology known as Blind Sources Localization and Separation (BSLS) that enables one to locate multiple incoherent sound signals in 3D space and separate original individual sources simultaneously, based on the directly measured mixed signals. These technologies have been validated through numerical simulations and experiments conducted in various non-ideal environments where there are non-negligible, unspecified sound reflections and reverberation as well as interferences from random background noise. Another innovation presented in this dissertation is concerned with applications of the TR algorithm to pinpoint the exact locations of hyper-active neurons in the brain auditory structure that are directly correlated to the tinnitus perception. Benchmark tests conducted on normal rats have confirmed the localization results provided by the TR algorithm. Results demonstrate that the spatial resolution of this source localization can be as high as the micrometer level. This high precision localization may lead to a paradigm shift in tinnitus diagnosis, which may in turn produce a more cost-effective treatment for tinnitus than any of the existing ones