139 citations · 163 across the 5 of their papers we have counts for
5 papers
Application of the hierarchical bootstrap to multi-level data in neuroscience
Varun Saravanan, Gordon J Berman, Samuel J Sober
A common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across…
Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds
Damián G. Hernández, Samuel J. Sober, Ilya Nemenman
The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzy…
Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds
Baohua Zhou, David Hofmann, Itai Pinkoviezky +2
Traditional theories of sensorimotor learning posit that animals use sensory error signals to find the optimal motor command in the face of Gaussian sensory and motor noise. Howeve…
Motor control by precisely timed spike patterns
Kyle H. Srivastava, Caroline M. Holmes, Michiel Vellema +4
A fundamental problem in neuroscience is to understand how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditiona…
Millisecond-scale motor encoding in a cortical vocal area
Claire Tang, Diala Chehayeb, Kyle Srivastava +2
Studies of motor control have almost universally examined firing rates to investigate how the brain shapes behavior. In principle, however, neurons could encode information through…