Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming

2012 ◽  
pp. 315-325 ◽  
Author(s):  
Eddie Clemente ◽  
Gustavo Olague ◽  
León Dozal ◽  
Martín Mancilla
Keyword(s):  
Object Recognition ◽  
Genetic Programming ◽  
Ventral Stream ◽  
Stream Model

scholarly journals Object comparison in the lateral intraparietal area

2017 ◽  
Vol 118 (4) ◽  
pp. 2458-2469 ◽  
Author(s):  
Wei Song Ong ◽  
Koorosh Mirpour ◽  
James W. Bisley
Keyword(s):  
Prefrontal Cortex ◽  
Object Recognition ◽  
Visual Information ◽  
Posterior Parietal Cortex ◽  
Ventral Stream ◽  
Object Identity ◽  
Stimulus Similarity ◽  
Lateral Intraparietal Area ◽  
Dynamic Representation ◽  
And Task

We can search for and locate specific objects in our environment by looking for objects with similar features. Object recognition involves stimulus similarity responses in ventral visual areas and task-related responses in prefrontal cortex. We tested whether neurons in the lateral intraparietal area (LIP) of posterior parietal cortex could form an intermediary representation, collating information from object-specific similarity map representations to allow general decisions about whether a stimulus matches the object being looked for. We hypothesized that responses to stimuli would correlate with how similar they are to a sample stimulus. When animals compared two peripheral stimuli to a sample at their fovea, the response to the matching stimulus was similar, independent of the sample identity, but the response to the nonmatch depended on how similar it was to the sample: the more similar, the greater the response to the nonmatch stimulus. These results could not be explained by task difficulty or confidence. We propose that LIP uses its known mechanistic properties to integrate incoming visual information, including that from the ventral stream about object identity, to create a dynamic representation that is concise, low dimensional, and task relevant and that signifies the choice priorities in mental matching behavior. NEW & NOTEWORTHY Studies in object recognition have focused on the ventral stream, in which neurons respond as a function of how similar a stimulus is to their preferred stimulus, and on prefrontal cortex, where neurons indicate which stimulus is being looked for. We found that parietal area LIP uses its known mechanistic properties to form an intermediary representation in this process. This creates a perceptual similarity map that can be used to guide decisions in prefrontal areas.

scholarly journals Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition

2020 ◽  
Author(s):  
Kohitij Kar ◽  
James J DiCarlo
Keyword(s):  
Prefrontal Cortex ◽  
Object Recognition ◽  
Late Phase ◽  
Ventral Stream ◽  
Neural Population ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Neural Network Models ◽  
Recurrent Processing ◽  
Ventral Prefrontal Cortex

SummaryDistributed neural population spiking patterns in macaque inferior temporal (IT) cortex that support core visual object recognition require additional time to develop for specific (“late-solved”) images suggesting the necessity of recurrent processing in these computations. Which brain circuit motifs are most responsible for computing and transmitting these putative recurrent signals to IT? To test whether the ventral prefrontal cortex (vPFC) is a critical recurrent circuit node in this system, here we pharmacologically inactivated parts of the vPFC and simultaneously measured IT population activity, while monkeys performed object discrimination tasks. Our results show that vPFC inactivation deteriorated the quality of the late-phase (>150 ms from image onset) IT population code, along with commensurate, specific behavioral deficits for “late-solved” images. Finally, silencing vPFC caused the monkeys’ IT activity patterns and behavior to become more like those produced by feedforward artificial neural network models of the ventral stream. Together with prior work, these results argue that fast recurrent processing through the vPFC is critical to the production of behaviorally-sufficient object representations in IT.

Contrast Sensitivity in Human Visual Areas and Its Relationship to Object Recognition

2002 ◽  
Vol 87 (6) ◽  
pp. 3102-3116 ◽  
Author(s):  
Galia Avidan ◽  
Michal Harel ◽  
Talma Hendler ◽  
Dafna Ben-Bashat ◽  
Ehud Zohary ◽  
...  
Keyword(s):  
Object Recognition ◽  
High Sensitivity ◽  
Ventral Stream ◽  
High Order ◽  
Object Representations ◽  
Lateral Occipital Complex ◽  
Line Drawings ◽  
Visual Areas ◽  
The Face ◽  
Contrast Invariance

An important characteristic of visual perception is the fact that object recognition is largely immune to changes in viewing conditions. This invariance is obtained within a sequence of ventral stream visual areas beginning in area V1 and ending in high order occipito-temporal object areas (the lateral occipital complex, LOC). Here we studied whether this transformation could be observed in the contrast response of these areas. Subjects were presented with line drawings of common objects and faces in five different contrast levels (0, 4, 6, 10, and 100%). Our results show that indeed there was a gradual trend of increasing contrast invariance moving from area V1, which manifested high sensitivity to contrast changes, to the LOC, which showed a significantly higher degree of invariance at suprathreshold contrasts (from 10 to 100%). The trend toward increased invariance could be observed for both face and object images; however, it was more complete for the face images, while object images still manifested substantial sensitivity to contrast changes. Control experiments ruled out the involvement of attention effects or hemodynamic “ceiling” in producing the contrast invariance. The transition from V1 to LOC was gradual with areas along the ventral stream becoming increasingly contrast-invariant. These results further stress the hierarchical and gradual nature of the transition from early retinotopic areas to high order ones, in the build-up of abstract object representations.

scholarly journals A performance-optimized model of neural responses across the ventral visual stream

2016 ◽  
Author(s):  
Darren Seibert ◽  
Daniel L Yamins ◽  
Diego Ardila ◽  
Ha Hong ◽  
James J DiCarlo ◽  
...  
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Object Recognition ◽  
Recognition Performance ◽  
Ventral Stream ◽  
Visual Object ◽  
Emergent Properties ◽  
Visual Stream ◽  
Response Properties ◽  
High Level

Human visual object recognition is subserved by a multitude of cortical areas. To make sense of this system, one line of research focused on response properties of primary visual cortex neurons and developed theoretical models of a set of canonical computations such as convolution, thresholding, exponentiating and normalization that could be hierarchically repeated to give rise to more complex representations. Another line or research focused on response properties of high-level visual cortex and linked these to semantic categories useful for object recognition. Here, we hypothesized that the panoply of visual representations in the human ventral stream may be understood as emergent properties of a system constrained both by simple canonical computations and by top-level, object recognition functionality in a single unified framework (Yamins et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Guclu and van Gerven, 2015). We built a deep convolutional neural network model optimized for object recognition and compared representations at various model levels using representational similarity analysis to human functional imaging responses elicited from viewing hundreds of image stimuli. Neural network layers developed representations that corresponded in a hierarchical consistent fashion to visual areas from V1 to LOC. This correspondence increased with optimization of the model's recognition performance. These findings support a unified view of the ventral stream in which representations from the earliest to the latest stages can be understood as being built from basic computations inspired by modeling of early visual cortex shaped by optimization for high-level object-based performance constraints.

scholarly journals Impaired Visual Object Recognition and Dorsal/Ventral Stream Interaction in Schizophrenia

2002 ◽  
Vol 59 (11) ◽  
pp. 1011 ◽  
Author(s):  
Glen M. Doniger ◽  
John J. Foxe ◽  
Micah M. Murray ◽  
Beth A. Higgins ◽  
Daniel C. Javitt
Keyword(s):  
Object Recognition ◽  
Ventral Stream ◽  
Visual Object ◽  
Visual Object Recognition

scholarly journals Object recognition in Williams syndrome: uneven ventral stream activation

2010 ◽  
Vol 14 (3) ◽  
pp. 549-565 ◽  
Author(s):  
Kirsten O’Hearn ◽  
Jennifer K. Roth ◽  
Susan M. Courtney ◽  
Beatriz Luna ◽  
Whitney Street ◽  
...  
Keyword(s):  
Object Recognition ◽  
Williams Syndrome ◽  
Ventral Stream

scholarly journals Action and object representation in the ventral "what" stream

2021 ◽  
Author(s):  
Moritz Wurm ◽  
Alfonso Caramazza
Keyword(s):  
Object Recognition ◽  
Action Recognition ◽  
Object Representation ◽  
Ventral Stream ◽  
Object Representations ◽  
Visual Stream ◽  
Ventral Visual Stream ◽  
Occipitotemporal Cortex ◽  
Object Features

The ventral visual stream is conceived as a pathway for object recognition. However, we also recognize the actions an object can be involved in. Here, we show that action recognition relies on a pathway in lateral occipitotemporal cortex, partially overlapping and topographically aligned with object representations that are precursors for action recognition. By contrast, object features that are more relevant for object recognition, such as color and texture, are restricted to medial areas of the ventral stream. We argue that the ventral stream bifurcates into lateral and medial pathways for action and object recognition, respectively. This account explains a number of observed phenomena, such as the duplication of object domains and the specific representational profiles in lateral and medial areas.

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