Publications

Neuron

DOI: 10.1016/j.neuron.2018.11.034

Trial-to-trial variability is a reflection of the circuitry and cellular physiology that make up a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional.

Conference on Lasers and Electro-Optics

DOI: 10.1364/cleo_at.2019.am1i.5

We present a novel remote focusing and demultiplexing scheme that allows simultaneous two- and three-photon imaging of two-layer neural activities, featuring large axial separation, independent foci tunability and large imaging depth enabled by three-photon microscopy.

arXiv

Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI.

32nd Conference on Neural Information Processing Systems (NeurIPS 2018)

As we show in this paper, mean absolute error (MAE) can perform poorly with deep neural networks and challenging datasets. Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and categorical cross entropy.

DOI: 10.1101/457416

The first main objective of this study was to use machine learning methods to predict a chronic stroke individual’s motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data.

Optics Letters

Interferometric spatial frequency modulation for imaging (I-SPIFI) is demonstrated for the first time, to our knowledge. Significantly, this imaging modality can be seamlessly combined with nonlinear SPIFI imaging and operates through single-element detection.

Optics Letters

DOI: 10.1364/ol.43.005351

Interferometric spatial frequency modulation for imaging (I-SPIFI) is demonstrated for the first time, to our knowledge. Significantly, this imaging modality can be seamlessly combined with nonlinear SPIFI imaging and operates through single-element detection.

eLife

DOI: 10.7554/elife.38105

How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question.

Nature Methods

DOI: 10.1038/s41592-018-0109-9

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. Deeper understanding of this structure requires studying phenomena detected in single trials, which is challenging.