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Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior.

Nature neuroscience
Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If rec...

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Amyloid : the international journal of experimental and clinical investigation : the official journal of the International Society of Amyloidosis
Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from availabl...

Bayesian Computation through Cortical Latent Dynamics.

Neuron
Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged and has had a major impact on models of per...

Body Patches in Inferior Temporal Cortex Encode Categories with Different Temporal Dynamics.

Journal of cognitive neuroscience
An unresolved question in cognitive neuroscience is how representations of object categories at different levels (basic and superordinate) develop during the course of the neural response within an area. To address this, we decoded categories of diff...

Sensory processing and categorization in cortical and deep neural networks.

NeuroImage
Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pil...

Deep Learning Neural Encoders for Motor Cortex.

IEEE transactions on bio-medical engineering
Intracortical brain-machine interfaces (BMIs) transform neural activity into control signals to drive a prosthesis or communication device, such as a robotic arm or computer cursor. To be clinically viable, BMI decoders must achieve high accuracy and...

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and ...

Neuronal population correlates of target selection and distractor filtering.

NeuroImage
Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal populatio...

Efficient inverse graphics in biological face processing.

Science advances
Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or "analysis-by-synthesis", presents a possible solution, but its...

A neural network for online spike classification that improves decoding accuracy.

Journal of neurophysiology
Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on dec...