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Found 28 papers, 7 papers with code
Semantic Data Set Construction from Human Clustering and Spatial Arrangement
We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity.
Source Invariance and Probabilistic Transfer: A Testable Theory of Probabilistic Neural Representations
As animals interact with their environments, they must infer properties of their surroundings.
Generative Adversarial Collaborations: A practical guide for conference organizers and participating scientists
Generative adversarial collaborations (GACs) are a form of formal teamwork between groups of scientists with diverging views.
How does the primate brain combine generative and discriminative computations in vision?
The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it.
How to optimize neuroscience data utilization and experiment design for advancing primate visual and linguistic brain models?
In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior.
The Topology and Geometry of Neural Representations
In the simulations, the ground truth is a data-generating layer representation in a neural network model and the models are the same and other layers in different model instances (trained from different random seeds).
Affinity-based Attention in Self-supervised Transformers Predicts Dynamics of Object Grouping in Humans
We found that our models of affinity spread that were built on feature maps from the self-supervised Transformers showed significant improvement over baseline and CNN based models on predicting reaction time patterns of humans, despite not being trained on the task or with any other object labels.
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development.
The neuroconnectionist research programme
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism.
Inferring exemplar discriminability in brain representations
The tests include previously used and novel, parametric and nonparametric tests, which treat subject as a random or fixed effect, and are based on different dissimilarity measures, different test statistics, and different inference procedures.
Testing the limits of natural language models for predicting human language judgments
Neural network language models can serve as computational hypotheses about how humans process language.
Statistical inference on representational geometries
However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data.
Capturing the objects of vision with neural networks
Deep neural network (DNN) models of visual object recognition, by contrast, remain largely tethered to the sensory input, despite achieving human-level performance at labeling objects.
Neural tuning and representational geometry
A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behavior.
An ecologically motivated image dataset for deep learning yields better models of human vision
This dataset comprises images from 1, 000 categories, selected to provide a challenging testbed for automated visual object recognition systems.
Evidence against implicitly recurrent computations in residual neural networks
To test this hypothesis, we manipulate the degree of weight sharing across layers in ResNets using soft gradient coupling.
Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction
We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems.
Going in circles is the way forward: the role of recurrence in visual inference
Biological visual systems exhibit abundant recurrent connectivity.
Controversial stimuli: pitting neural networks against each other as models of human recognition
To efficiently compare models' ability to predict human responses, we synthesize controversial stimuli: images for which different models produce distinct responses.
Visualizing Representational Dynamics with Multidimensional Scaling Alignment
Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise distances of the response patterns as a representational dissimilarity matrix (RDM).
Recurrence is required to capture the representational dynamics of the human visual system
Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning.
Deep Learning for Cognitive Neuroscience
There are many levels at which cognitive neuroscientists can use deep learning in their work, from inspiring theories to serving as full computational models.
Neural network models and deep learning - a primer for biologists
Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence, where they are used to approximate functions and dynamics by learning from examples.
Adaptive Geo-Topological Independence Criterion
We show that these criteria, like the distance correlation and RKHS-based criteria, provide dependence indicators.
Cognitive computational neuroscience
To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments.
Neurons and Cognition
Building machines that adapt and compute like brains
Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains.
Robustly representing uncertainty in deep neural networks through sampling
We tested the calibration of the probabilistic predictions of Bayesian convolutional neural networks (CNNs) on MNIST and CIFAR-10.
Representational Distance Learning for Deep Neural Networks
We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher.
