scholarly journals Categorization in monkey inferior temporal cortex determined by image features, not acquired knowledge

2017 ◽  
Vol 17 (10) ◽  
pp. 1237
Author(s):  
Xiaomin Yue ◽  
Marissa Yetter ◽  
Leslie Ungerleider
Keyword(s):  
Temporal Cortex ◽  
Image Features ◽  
Inferior Temporal Cortex

scholarly journals Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge

2019 ◽  
Author(s):  
Kamila M. Jozwik ◽  
Michael Lee ◽  
Tiago Marques ◽  
Martin Schrimpf ◽  
Pouya Bashivan
Keyword(s):  
Large Scale ◽  
Visual Stimuli ◽  
Temporal Cortex ◽  
Image Features ◽  
Inferior Temporal Cortex ◽  
Network Architectures ◽  
Image Preprocessing ◽  
Neural Representations ◽  
Brain Responses ◽  
Early Visual Cortex

Image features computed by specific convolutional artificial neural networks (ANNs) can be used to make state-of-the-art predictions of primate ventral stream responses to visual stimuli.However, in addition to selecting the specific ANN and layer that is used, the modeler makes other choices in preprocessing the stimulus image and generating brain predictions from ANN features. The effect of these choices on brain predictivity is currently underexplored.Here, we directly evaluated many of these choices by performing a grid search over network architectures, layers, image preprocessing strategies, feature pooling mechanisms, and the use of dimensionality reduction. Our goal was to identify model configurations that produce responses to visual stimuli that are most similar to the human neural representations, as measured by human fMRI and MEG responses. In total, we evaluated more than 140,338 model configurations. We found that specific configurations of CORnet-S best predicted fMRI responses in early visual cortex, and CORnet-R and SqueezeNet models best predicted fMRI responses in inferior temporal cortex. We found specific configurations of VGG-16 and CORnet-S models that best predicted the MEG responses.We also observed that downsizing input images to ~50-75% of the input tensor size lead to better performing models compared to no downsizing (the default choice in most brain models for vision). Taken together, we present evidence that brain predictivity is sensitive not only to which ANN architecture and layer is used, but choices in image preprocessing and feature postprocessing, and these choices should be further explored.

Organization of local horizontal functional interactions between neurons in the inferior temporal cortex of macaque monkeys

2014 ◽  
Vol 111 (12) ◽  
pp. 2589-2602 ◽  
Author(s):  
Hiroshi Tamura ◽  
Yoshiya Mori ◽  
Hidekazu Kaneko
Keyword(s):  
Neuronal Activity ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Visual Object ◽  
Detailed Knowledge ◽  
Visual Response ◽  
Stimulus Preference ◽  
Macaque Monkeys ◽  
Functional Interactions ◽  
Cross Correlation Analysis

Detailed knowledge of neuronal circuitry is necessary for understanding the mechanisms underlying information processing in the brain. We investigated the organization of horizontal functional interactions in the inferior temporal cortex of macaque monkeys, which plays important roles in visual object recognition. Neuronal activity was recorded from the inferior temporal cortex using an array of eight tetrodes, with spatial separation between paired neurons up to 1.4 mm. We evaluated functional interactions on a time scale of milliseconds using cross-correlation analysis of neuronal activity of the paired neurons. Visual response properties of neurons were evaluated using responses to a set of 100 visual stimuli. Adjacent neuron pairs tended to show strong functional interactions compared with more distant neuron pairs, and neurons with similar stimulus preferences tended to show stronger functional interactions than neurons with different stimulus preferences. Thus horizontal functional interactions in the inferior temporal cortex appear to be organized according to both cortical distances and similarity in stimulus preference between neurons. Furthermore, the relationship between strength of functional interactions and similarity in stimulus preference observed in distant neuron pairs was more prominent than in adjacent pairs. The results suggest that functional circuitry is specifically organized, depending on the horizontal distances between neurons. Such specificity endows each circuit with unique functions.

scholarly journals Functional MRI evidence for a role of frontal and inferior temporal cortex in amodal components of priming

Brain ◽  
2000 ◽  
Vol 123 (3) ◽  
pp. 620-640 ◽  
Author(s):  
R. L. Buckner
Keyword(s):  
Functional Mri ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex

scholarly journals Object Representation in Inferior Temporal Cortex Is Organized Hierarchically in a Mosaic-Like Structure

2013 ◽  
Vol 33 (42) ◽  
pp. 16642-16656 ◽  
Author(s):  
T. Sato ◽  
G. Uchida ◽  
M. D. Lescroart ◽  
J. Kitazono ◽  
M. Okada ◽  
...  
Keyword(s):  
Object Representation ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex

A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction

2010 ◽  
Vol 22 (12) ◽  
pp. 2979-3035 ◽  
Author(s):  
Stefan Klampfl ◽  
Wolfgang Maass
Keyword(s):  
Feature Extraction ◽  
Theoretical Basis ◽  
Learning Algorithm ◽  
Temporal Cortex ◽  
Pattern Discrimination ◽  
Stimulus Onset ◽  
Inferior Temporal Cortex ◽  
Linear Discriminant ◽  
Discrimination Capability ◽  
The Brain

Neurons in the brain are able to detect and discriminate salient spatiotemporal patterns in the firing activity of presynaptic neurons. It is open how they can learn to achieve this, especially without the help of a supervisor. We show that a well-known unsupervised learning algorithm for linear neurons, slow feature analysis (SFA), is able to acquire the discrimination capability of one of the best algorithms for supervised linear discrimination learning, the Fisher linear discriminant (FLD), given suitable input statistics. We demonstrate the power of this principle by showing that it enables readout neurons from simulated cortical microcircuits to learn without any supervision to discriminate between spoken digits and to detect repeated firing patterns that are embedded into a stream of noise spike trains with the same firing statistics. Both these computer simulations and our theoretical analysis show that slow feature extraction enables neurons to extract and collect information that is spread out over a trajectory of firing states that lasts several hundred ms. In addition, it enables neurons to learn without supervision to keep track of time (relative to a stimulus onset, or the initiation of a motor response). Hence, these results elucidate how the brain could compute with trajectories of firing states rather than only with fixed point attractors. It also provides a theoretical basis for understanding recent experimental results on the emergence of view- and position-invariant classification of visual objects in inferior temporal cortex.

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