Spontaneous behaviors drive multidimensional, brainwide activity

C Stringer, M Pachitariu, N Steinmetz, CB Reddy… - Science, 2019‏ - science.org
INTRODUCTION In the absence of sensory inputs, the brain produces structured patterns of
activity, which can be as large as or larger than sensory-driven activity. Ongoing activity …

[HTML][HTML] How learning unfolds in the brain: toward an optimization view

JA Hennig, ER Oby, DM Losey, AP Batista, MY Byron… - Neuron, 2021‏ - cell.com
How do changes in the brain lead to learning? To answer this question, consider an artificial
neural network (ANN), where learning proceeds by optimizing a given objective or cost …

Diverse coupling of neurons to populations in sensory cortex

M Okun, NA Steinmetz, L Cossell, MF Iacaruso, H Ko… - Nature, 2015‏ - nature.com
A large population of neurons can, in principle, produce an astronomical number of distinct
firing patterns. In cortex, however, these patterns lie in a space of lower dimension,,,, as if …

Kilohertz frame-rate two-photon tomography

A Kazemipour, O Novak, D Flickinger, JS Marvin… - Nature …, 2019‏ - nature.com
Point-scanning two-photon microscopy enables high-resolution imaging within scattering
specimens such as the mammalian brain, but sequential acquisition of voxels fundamentally …

The nature of shared cortical variability

IC Lin, M Okun, M Carandini, KD Harris - Neuron, 2015‏ - cell.com
Neuronal responses of sensory cortex are highly variable, and this variability is correlated
across neurons. To assess how variability reflects factors shared across a neuronal …

Linear dynamical neural population models through nonlinear embeddings

Y Gao, EW Archer, L Paninski… - Advances in neural …, 2016‏ - proceedings.neurips.cc
A body of recent work in modeling neural activity focuses on recovering low-dimensional
latent features that capture the statistical structure of large-scale neural populations. Most …

Searching for collective behavior in a large network of sensory neurons

G Tkačik, O Marre, D Amodei… - PLoS computational …, 2014‏ - journals.plos.org
Maximum entropy models are the least structured probability distributions that exactly
reproduce a chosen set of statistics measured in an interacting network. Here we use this …

Noise as a resource for computation and learning in networks of spiking neurons

W Maass - Proceedings of the IEEE, 2014‏ - ieeexplore.ieee.org
We are used to viewing noise as a nuisance in computing systems. This is a pity, since noise
will be abundantly available in energy-efficient future nanoscale devices and circuits. I …

A large-scale neural network training framework for generalized estimation of single-trial population dynamics

MR Keshtkaran, AR Sedler, RH Chowdhury… - Nature …, 2022‏ - nature.com
Achieving state-of-the-art performance with deep neural population dynamics models
requires extensive hyperparameter tuning for each dataset. AutoLFADS is a model-tuning …

Cortical state determines global variability and correlations in visual cortex

ML Schölvinck, AB Saleem, A Benucci… - Journal of …, 2015‏ - jneurosci.org
The response of neurons in sensory cortex to repeated stimulus presentations is highly
variable. To investigate the nature of this variability, we compared the spike activity of …