Faculty Opinions recommendation of Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

Research on the spatio-temporal dynamics of visual object recognition suggests a recurrent, interactive model whereby an initial feedforward sweep through the ventral stream to prefrontal cortex is followed by recurrent interactions. However, critical questions remain regarding the factors that mediate the degree of recurrent interactions necessary for meaningful object recognition. The novel prediction we test here is that recurrent interactivity is driven by increasing semantic integration demands as defined by the complexity of semantic information required by the task and driven by the stimuli. To test this prediction, we recorded magnetoencephalography data while participants named living and nonliving objects during two naming tasks. We found that the spatio-temporal dynamics of neural activity were modulated by the level of semantic integration required. Specifically, source reconstructed time courses and phase synchronization measures showed increased recurrent interactions as a function of semantic integration demands. These findings demonstrate that the cortical dynamics of object processing are modulated by the complexity of semantic information required from the visual input.

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Fusing bottom-up and top-down pathways in neural networks for visual object recognition

The 2010 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2010.5596497 ◽

2010 ◽

Cited By ~ 3

Author(s):

Yuhua Zheng ◽

Yan Meng ◽

Yaochu Jin

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Visual Object ◽

Visual Object Recognition ◽

Top Down ◽

Bottom Up

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Qualitative similarities and differences in visual object representations between brains and deep networks

Nature Communications ◽

10.1038/s41467-021-22078-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Georgin Jacob ◽

R. T. Pramod ◽

Harish Katti ◽

S. P. Arun

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Deep Neural Networks ◽

Relative Size ◽

Sufficient Conditions ◽

Human Perception ◽

Visual Object ◽

Object Representations ◽

Deep Networks ◽

Global Advantage

AbstractDeep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber’s law, relative size, multiple object normalization and correlated sparseness. Yet other phenomena were absent in trained networks, such as 3D shape processing, surface invariance, occlusion, natural parts and the global advantage. These findings indicate sufficient conditions for the emergence of these phenomena in brains and deep networks, and offer clues to the properties that could be incorporated to improve deep networks.

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Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition

10.1101/032656 ◽

2015 ◽

Cited By ~ 5

Author(s):

Radoslaw Cichy ◽

Dimitrios Pantazis ◽

Aude Oliva

Keyword(s):

Object Recognition ◽

Temporal Dynamics ◽

Imaging Techniques ◽

Visual Object ◽

Data Sets ◽

Visual Object Recognition ◽

Occipital Pole ◽

Spatio Temporal ◽

The Brain

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed a new integration approach that uses representational similarities to combine measurements from different imaging modalities - magnetoencephalography (MEG) and functional MRI (fMRI) - to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to two independent MEG-fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50-80ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. These results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions.

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