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Stimulus onset quenches neural variability: a widespread cortical phenomenon
Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the meanExpand
Influence of heart rate on the BOLD signal: The cardiac response function
TLDR
It is demonstrated that a convolution model including RV and HR can explain significantly more variance in gray matter BOLD signal than a model that includes RV alone, and an average HR response function is proposed that well characterizes the subject population. Expand
A High-Performance Neural Prosthesis Enabled by Control Algorithm Design
TLDR
A new control algorithm is presented, the recalibrated feedback intention–trained Kalman filter (ReFIT-KF) that incorporates assumptions about the nature of closed-loop neural prosthetic control and demonstrates repeatable high performance for years after implantation in two monkeys, thereby increasing the clinical viability of neural prostheses. Expand
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
TLDR
A novel method for extracting neural trajectories-Gaussian-process factor analysis (GPFA) is presented-which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework and shows how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Expand
Linear dimensionality reduction: survey, insights, and generalizations
TLDR
This survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology. Expand
Dimensionality reduction for large-scale neural recordings
TLDR
This review examines three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets, and practical advice about selecting methods and interpreting their outputs. Expand
Bayesian Optimization with Inequality Constraints
TLDR
This work presents constrained Bayesian optimization, which places a prior distribution on both the objective and the constraint functions, and evaluates this method on simulated and real data, demonstrating that constrainedBayesian optimization can quickly find optimal and feasible points, even when small feasible regions cause standard methods to fail. Expand
BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisyExpand
Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?
TLDR
It is found that at the level of a single neuron, preparatory tuning was weakly correlated with movement-period tuning, which suggests that preparatory activity may not represent specific factors, and may instead play a more mechanistic role. Expand
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex.
TLDR
It is suggested that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. Expand
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