Michael Oliver

A biologically-inspired hierarchical convolutional energy model predicts V4 responses to natural videos

The paper develops a hierarchical convolutional energy model to predict V4 neuron responses to natural videos and finds it captures complex spatiotemporal selectivity in V4.

Stimulus novelty uncovers coding diversity in survey of visual cortex

The paper identifies diverse functional neuron types in the visual cortex that process novel and familiar stimuli in mice.

Removing independent noise in systems neuroscience data using DeepInterpolation

DeepInterpolation improves signal-to-noise ratio in neuroscience data without sacrificing resolution, enhancing detection of neuronal activity across multiple modalities.

A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

The paper presents a dataset and analysis of spiking activity across multiple mouse visual areas, revealing that functional connectivity mirrors anatomical hierarchy.

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

The paper presents a large-scale dataset of neural activity in the mouse visual cortex and shows that responses to different stimuli are largely independent, suggesting multi-layer hierarchical computation.

On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging.

This study calibrates two-photon calcium imaging to recover action potentials in mice, finding that fluorescence changes are inconsistent for small spike numbers and vary by mouse line.

The DeepTune framework for modeling and characterizing neurons in visual cortex area V4

The paper introduces DeepTune, a framework to interpret neural network models of V4 neurons, revealing diverse shape/texture tuning and suppressive responses in primate visual cortex.