Jesse A. Livezey

This paper demonstrates closed-loop optimization of visual stimuli to modulate steady-state visual evoked response (SSVEP) power in non-invasive EEG recordings.

The paper identifies and mitigates bias in neural data models by resolving non-identifiability, showing that coupling is overestimated and tuning is underestimated in common methods.

The paper analyzes cortical surface electrical potentials (CSEPs) to show that high-gamma components are band-limited, not broadband, and identifies multiple band-limited frequency components in ECoG signals.

The paper shows that correlated variability in neural populations leads to highly suboptimal sensory coding according to novel null models.

The paper shows that heterogeneous synaptic weights can improve neural coding in the presence of common noise by enhancing Fisher and Shannon information, even when amplifying noise.

The paper applies an improved statistical inference framework to neuroscience models, finding more accurate and sparse parameter estimates that reshape interpretations across multiple datasets.

The paper investigates whether retinal spatial whitening is necessary for V1 to achieve sparse coding of natural scenes using biologically plausible learning rules.