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Deep neural networks: a new framework for modeling biological vision and brain information processing
Recent advances in neural network modeling have enabled major strides in computer vision
and other artificial intelligence applications. Human-level visual recognition abilities are …
and other artificial intelligence applications. Human-level visual recognition abilities are …
Understanding what we see: how we derive meaning from vision
Recognising objects goes beyond vision, and requires models that incorporate different
aspects of meaning. Most models focus on superordinate categories (eg, animals, tools) …
aspects of meaning. Most models focus on superordinate categories (eg, animals, tools) …
THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
Understanding object representations requires a broad, comprehensive sampling of the
objects in our visual world with dense measurements of brain activity and behavior. Here …
objects in our visual world with dense measurements of brain activity and behavior. Here …
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
The complex multi-stage architecture of cortical visual pathways provides the neural basis
for efficient visual object recognition in humans. However, the stage-wise computations …
for efficient visual object recognition in humans. However, the stage-wise computations …
[کتاب][B] Fundamentals of cognition
MW Eysenck, M Brysbaert - 2018 - api.taylorfrancis.com
Is it possible to learn something without being aware of it? How does emotion influence the
way we think? How can we improve our memory? Fundamentals of Cognition, third edition …
way we think? How can we improve our memory? Fundamentals of Cognition, third edition …
Recurrent convolutional neural networks: a better model of biological object recognition
Feedforward neural networks provide the dominant model of how the brain performs visual
object recognition. However, these networks lack the lateral and feedback connections, and …
object recognition. However, these networks lack the lateral and feedback connections, and …
The representational dynamics of task and object processing in humans
Despite the importance of an observer's goals in determining how a visual object is
categorized, surprisingly little is known about how humans process the task context in which …
categorized, surprisingly little is known about how humans process the task context in which …
Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision
Deep feedforward neural network models of vision dominate in both computational
neuroscience and engineering. The primate visual system, by contrast, contains abundant …
neuroscience and engineering. The primate visual system, by contrast, contains abundant …
A representational similarity analysis of the dynamics of object processing using single-trial EEG classification
The recognition of object categories is effortlessly accomplished in everyday life, yet its
neural underpinnings remain not fully understood. In this electroencephalography (EEG) …
neural underpinnings remain not fully understood. In this electroencephalography (EEG) …
Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition
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 …
spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation …