Shinji Nishimoto

Area MT has been an important target for studies of motion processing. However, previous neurophysiological studies of MT have used simple stimuli that do not contain many of the motion signals that occur during natural vision. In this study we sought to determine whether views of area MT neurons developed using simple stimuli can account for MT responses under more naturalistic conditions. We recorded responses from macaque area MT neurons during stimulation with naturalistic movies. We then us

We introduce a novel class of white-noise analyses, named local spectral reverse correlation (LSRC), which is capable of revealing various aspects of visual receptive field profiles that were undetectable previously in a single simple measurement. The method is based on spectral analyses in a two-dimensional spatial frequency domain for spatially localized areas within and around their receptive fields. Extracellular single-unit recordings were performed for area 17 and 18 neurons in anesthetize

We experience a rich variety of emotions in daily life, and a fundamental goal of affective neuroscience is to determine how these emotions are represented in the brain. Recent psychological studies have used naturalistic stimuli (e.g., movies) to reveal high dimensional representational structures of diverse daily-life emotions. However, relatively little is known about how such diverse emotions are represented in the brain because most of the affective neuroscience studies have used only a sma

Orientation and spatial frequency selectivities are fundamental properties of cells in the early visual cortex. Although they are customarily tested with drifting sinusoidal gratings, a recently developed subspace reverse correlation method may be a better replacement for obtaining a selectivity map in a joint orientation and spatial frequency domain at higher resolution efficiently. These two methods are examined for their accuracy and data compatibility for cells in areas 17 and 18 of anesthet

Our findings have elucidated the quantitative representation of music genres in the human cortex, indicating the possibility of modeling this categorization of complex auditory stimuli based on brain activity.

In vivo calcium (Ca²⁺) imaging using two-photon microscopy allows activity to be monitored simultaneously from hundreds of individual neurons within a local population. While this allows us to gain important insights into how cortical neurons represent sensory information, factors such as photo-bleaching of the Ca²⁺ indicator limit imaging duration (and thus the numbers of stimuli that can be tested), which in turn hampers the full characterization of neuronal response prop

During natural vision, humans make frequent eye movements but perceive a stable visual world. It is therefore likely that the human visual system contains representations of the visual world that are invariant to eye movements. Here we present an experiment designed to identify visual areas that might contain eye-movement-invariant representations. We used functional MRI to record brain activity from four human subjects who watched natural movies. In one condition subjects were required to fixat

The human linguistic system is characterized by modality invariance and attention selectivity. Previous studies have examined these properties independently and reported perisylvian region involvement for both; however, their relationship and the linguistic information they harbor remain unknown. Participants were assessed by functional magnetic resonance imaging, while spoken narratives (auditory) and written texts (visual) were presented, either separately or simultaneously. Participants were

This dataset includes functional magnetic resonance imaging (fMRI) data collected while five subjects extensively listened to 540 music pieces from 10 music genres over the course of 3 days. Behavioral data are also available. Data are separated into training and test samples to facilitate the application of machine learning algorithms. Test stimuli were repeated four times and can be used to evaluate the signal to noise ratio of brain activity. Using this dataset, both neuroimaging and machine