Daniel A. Butts

Simple, static, spiking nonlinearities can account for seemingly complex interactions between visual, motor and cognitive variables in the visual cortex.

The paper shows that simple nonlinearities in the visual cortex can explain complex interactions between visual, motor, and cognitive variables through additive input modulation.

Active sensing explains cortical variability and improves visual information

The paper shows fixational eye movements in marmosets significantly contribute to neuronal variability and enhance spatial information in visual cortex.

Activity in primate visual cortex is minimally driven by spontaneous movements

The paper investigates whether spontaneous body movements drive activity in primate visual cortex and finds that such activity is largely explained by retinal image changes caused by movements.

Hierarchical VAEs provide a normative account of motion processing in the primate brain

This study shows hierarchical VAEs better model motion perception by aligning with brain function and ecological stimulus statistics.

Model-based characterization of the selectivity of neurons in primary visual cortex

The paper links complex encoding models of V1 neurons to classical measures of selectivity, revealing new insights into spatial frequency tuning and cell classification.

Running modulates primate and rodent visual cortex differently

The paper compares how running affects visual cortex activity in marmosets and mice, finding smaller and suppressive modulations in marmosets compared to mice.

Beyond Fixation: detailed characterization of neural selectivity in free-viewing primates

This paper characterizes neural selectivity in free-viewing primates, showing that receptive fields and tuning properties can be recovered from multiple cortical areas using natural behavior and high-precision eye tracking.

A latent variable approach to decoding neural population activity

This paper introduces a decoding framework that leverages low-dimensional latent variables to improve decoding performance in neural population activity.

Functional characterization of retinal ganglion cells using tailored nonlinear modeling

The paper develops a statistical model to characterize retinal ganglion cell computation and identifies distinct receptive fields and cell types in mouse retinas.

Current models cannot account for V1’s specialisation for binocular natural image statistics

The paper tests whether linear-nonlinear models can explain V1 neuron responses to natural and unnatural stimuli, finding they fail to capture strong responses to binocularly correlated stimuli.

Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models

This paper introduces nonlinear latent variable models to analyze shared variability in neural populations and finds that different brain regions exhibit distinct nonlinear structures.

Behavioral response to visual motion impacts population coding in the mouse visual thalamus

The paper examines how visual motion affects behavioral responses and neural coding in the mouse visual thalamus, finding that back-to-front motion biases population responses during stillness.

Spatial organization of the mouse auditory cortex to sound dynamics revealed using automated image segmentation

The paper uses automated image segmentation to study spatial organization of sound dynamics in mouse auditory cortex, revealing differences in response to sound onset and offset.

Revealing unobserved factors underlying cortical activity using a rectified latent variable model applied to neural population recordings

This paper introduces a rectified latent variable model to identify unobserved factors influencing cortical activity in neural recordings and shows it outperforms traditional methods.