Dataset Information

Visual stimulus features that elicit activity in object-vector cells.

ABSTRACT: Object-vector (OV) cells are cells in the medial entorhinal cortex (MEC) that track an animal's distance and direction to objects in the environment. Their firing fields are defined by vectorial relationships to free-standing 3-dimensional (3D) objects of a variety of identities and shapes. However, the natural world contains a panorama of objects, ranging from discrete 3D items to flat two-dimensional (2D) surfaces, and it remains unclear what are the most fundamental features of objects that drive vectorial responses. Here we address this question by systematically changing features of experimental objects. Using an algorithm that robustly identifies OV firing fields, we show that the cells respond to a variety of 2D surfaces, with visual contrast as the most basic visual feature to elicit neural responses. The findings suggest that OV cells use plain visual features as vectorial anchoring points, allowing vector-guided navigation to proceed in environments with few free-standing landmarks.

SUBMITTER: Andersson SO

PROVIDER: S-EPMC8545948 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Visual stimulus features that elicit activity in object-vector cells.

Andersson Sebastian O SO Moser Edvard I EI Moser May-Britt MB

Communications biology 20211025 1

Object-vector (OV) cells are cells in the medial entorhinal cortex (MEC) that track an animal's distance and direction to objects in the environment. Their firing fields are defined by vectorial relationships to free-standing 3-dimensional (3D) objects of a variety of identities and shapes. However, the natural world contains a panorama of objects, ranging from discrete 3D items to flat two-dimensional (2D) surfaces, and it remains unclear what are the most fundamental features of objects that d ...[more]

PMID: 34697385

Similar Datasets

Project description:Given the limited processing capabilities of the sensory system, it is essential that attended information is gated to downstream areas, whereas unattended information is blocked. While it has been proposed that alpha band (8-13 Hz) activity serves to route information to downstream regions by inhibiting neuronal processing in task-irrelevant regions, this hypothesis remains untested. Here we investigate how neuronal oscillations detected by electroencephalography in visual areas during working memory encoding serve to gate information reflected in the simultaneously recorded blood-oxygenation-level-dependent (BOLD) signals recorded by functional magnetic resonance imaging in downstream ventral regions. We used a paradigm in which 16 participants were presented with faces and landscapes in the right and left hemifields; one hemifield was attended and the other unattended. We observed that decreased alpha power contralateral to the attended object predicted the BOLD signal representing the attended object in ventral object-selective regions. Furthermore, increased alpha power ipsilateral to the attended object predicted a decrease in the BOLD signal representing the unattended object. We also found that the BOLD signal in the dorsal attention network inversely correlated with visual alpha power. This is the first demonstration, to our knowledge, that oscillations in the alpha band are implicated in the gating of information from the visual cortex to the ventral stream, as reflected in the representationally specific BOLD signal. This link of sensory alpha to downstream activity provides a neurophysiological substrate for the mechanism of selective attention during stimulus processing, which not only boosts the attended information but also suppresses distraction. Although previous studies have shown a relation between the BOLD signal from the dorsal attention network and the alpha band at rest, we demonstrate such a relation during a visuospatial task, indicating that the dorsal attention network exercises top-down control of visual alpha activity.

Project description:Animate and inanimate objects differ in their intermediate visual features. For instance, animate objects tend to be more curvilinear compared to inanimate objects (e.g., Levin, Takarae, Miner, & Keil, 2001). Recently, it has been demonstrated that these differences in the intermediate visual features of animate and inanimate objects are sufficient for categorization: Human participants viewing synthesized images of animate and inanimate objects that differ largely in the amount of these visual features classify objects as animate/inanimate significantly above chance (Long, Stormer, & Alvarez, 2017). A remaining question, however, is whether the observed categorization is a consequence of top-down cognitive strategies (e.g., rectangular shapes are less likely to be animals) or a consequence of bottom-up processing of their intermediate visual features, per se, in the absence of top-down cognitive strategies. To address this issue, we repeated the classification experiment of Long et al. (2017) but, unlike Long et al. (2017), matched the synthesized images, on average, in the amount of image-based and perceived curvilinear and rectilinear information. Additionally, in our synthesized images, global shape information was not preserved, and the images appeared as texture patterns. These changes prevented participants from using top-down cognitive strategies to perform the task. During the experiment, participants were presented with these synthesized, texture-like animate and inanimate images and, on each trial, were required to classify them as either animate or inanimate with no feedback given. Participants were told that these synthesized images depicted abstract art patterns. We found that participants still classified the synthesized stimuli significantly above chance even though they were unaware of their classification performance. For both object categories, participants depended more on the curvilinear and less on the rectilinear, image-based information present in the stimuli for classification. Surprisingly, the stimuli most consistently classified as animate were the most dangerous animals in our sample of images. We conclude that bottom-up processing of intermediate features present in the visual input is sufficient for animate/inanimate object categorization and that these features may convey information associated with the affective content of the visual stimuli.

Project description:It is debated whether the representation of objects in inferior temporal (IT) cortex is distributed over activities of many neurons or there are restricted islands of neurons responsive to a specific set of objects. There are lines of evidence demonstrating that fusiform face area (FFA-in human) processes information related to specialized object recognition (here we say within category object recognition such as face identification). Physiological studies have also discovered several patches in monkey ventral temporal lobe that are responsible for facial processing. Neuronal recording from these patches shows that neurons are highly selective for face images whereas for other objects we do not see such selectivity in IT. However, it is also well-supported that objects are encoded through distributed patterns of neural activities that are distinctive for each object category. It seems that visual cortex utilize different mechanisms for between category object recognition (e.g., face vs. non-face objects) vs. within category object recognition (e.g., two different faces). In this study, we address this question with computational simulations. We use two biologically inspired object recognition models and define two experiments which address these issues. The models have a hierarchical structure of several processing layers that simply simulate visual processing from V1 to aIT. We show, through computational modeling, that the difference between these two mechanisms of recognition can underlie the visual feature and extraction mechanism. It is argued that in order to perform generic and specialized object recognition, visual cortex must separate the mechanisms involved in within category from between categories object recognition. High recognition performance in within category object recognition can be guaranteed when class-specific features with intermediate size and complexity are extracted. However, generic object recognition requires a distributed universal dictionary of visual features in which the size of features does not have significant difference.

Dataset Information

Visual stimulus features that elicit activity in object-vector cells.

Publications

Visual stimulus features that elicit activity in object-vector cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets