Paper proposes a neural network for learning crossmodal stimuli.
problem Improving crossmodal processing in dynamic environments.
method Deep neural architecture trained by expectation learning.
result Self-adaptable deep learning model for crossmodal stimuli.
Experiment sets personalized emotion baselines for real-life prediction.
problem Establishing reliable ground-truth estimates for real-life emotion recognition.
method Controlled experiment with adaptive stimuli selection and user feedback.
result 85% accuracy in predicting emotional state with few features.
New method reconstructs brain stimuli from responses.
problem Reconstructing perceived stimuli from brain responses.
method Combining probabilistic inference and adversarial training of neural networks.
result Generates state-of-the-art reconstructions of perceived faces.
Paper classifies plant electrical signals to identify external stimuli.
problem Classifying external stimuli using plant electrical response.
method Computed 11 statistical features from plant electrical signals and used discriminant analysis.
result Raw electrical signals contain enough information for stimulus classification.
New method decomposes sensory information from neurons into specific stimuli and features.
problem Understanding how much and what specific information neurons encode.
method Introduced axioms for meaningful stimulus-wise decomposition and derived a tractable solution using diffusion models.
result Can efficiently estimate contributions of specific stimuli and features to encoded information.
Bayesian optimization generates personalized face stimuli for cognitive neuroscience.
problem Lack of personalized face stimuli in cognitive neuroscience studies.
method Combines GANs with Bayesian optimization to identify individual response patterns to faces.
result Algorithm efficiently generates optimal faces maximizing individual subject's response.
APA improves brain decoding accuracy for visual stimuli.
problem Decoding patterns in human brain using MVPA.
method Developed novel anatomical feature extraction and AdaBoost algorithm.
result Superior performance in decoding visual stimuli categories.
New neural network learns adaptive behaviors inspired by neuromodulation.
problem Current AI lacks the ability to adapt to changing environments.
method Inspired by cellular neuromodulation, a new deep neural network architecture is designed.
result Neuromodulation-based networks improve adaptation in meta-reinforcement learning tasks.
NTFA models participant and stimulus variations in fMRI data.
problem Lack of statistical tools to examine participant differences in neuroimaging studies.
method NTFA, a probabilistic factor analysis model inferring embeddings for participants and stimuli.
result NTFA improves predictive generalization and downstream tasks.
New method learns psychological similarity spaces for unseen stimuli.
problem Generalizing psychological similarity spaces to new stimuli.
method Learn mapping from raw stimuli to similarity space using ANNs.
result ANNs can successfully map raw stimuli into similarity spaces.
The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at single cell l…
This work synthesizes realistic data from neural excitation patterns to anonymize private data.
problem Lack of usable training data due to privacy regulations.
method Synthesize realistic data by exciting trained deep neural network neurons.
result Synthesized data can generalize well and anonymize participants' identities.
CNNs accurately model retinal responses to natural scenes.
problem Understanding neural computations in retinal responses to natural stimuli.
method Deep convolutional neural networks (CNNs) were used to model retinal responses to natural scenes.
result CNNs are more accurate than linear models in predicting retinal responses to natural scenes.
A mixture of experts model predicts brain activation from word stimuli.
problem Classical encoding models ignore connections among brain regions.
method Mixture of experts capturing ROI-specific brain activity patterns.
result Model predicts entire brain activation with high spatial accuracy.
Paper compares decision tree-based classification for detecting plant electrical signals from NaCl, O3, and H2SO4.
problem Detecting external chemical stimuli from plant electrical signals.
method Extracted features from filtered and raw plant electrical signals, used decision tree-based multi-class classification.
result Optimized feature and classifier combinations for distinguishing chemical stimuli.
Novel model for decoding visual stimuli in human brains.
problem Challenges in MVP techniques, including noise and sparsity, and the cost of brain studies.
method Automatic detection of active regions, new Gaussian smoothing method, combining fMRI data sets.
result Superior performance compared to state-of-the-art methods.
Estimates brain connectivity networks from natural stimuli, controlling type I error.
problem Estimating brain connectivity networks from natural stimuli with nuisance signals.
method Estimating stimulus-locked brain network by treating non-stimulus-induced signals as nuisance parameters. Testing maximum degree of network using inferential method.
result Proves type I error can be controlled and power increases asymptotically.
A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity an…
Derives a biologically plausible neural network for Slow Feature Analysis.
problem Learning latent features from time series data.
method Starting from an SFA objective, derives Bio-SFA with a biologically plausible neural network implementation.
result Validates Bio-SFA on naturalistic stimuli, reproducing interesting properties of brain cells.
Relational learning can be used to augment one data source with other correlated sources of information, to improve predictive accuracy. We frame a large class of relational learning problems as matrix factorization problems, and propose a hierarchical Bayesian model. Training our Bayesian model using random-walk Metro…
Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world --- contrast and luminance for vision, pitch and intensity for sound --- and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis o…
New method for predicting neuron activity with unknown stimuli.
problem Statistical inference of neuron activity with missing data and unknown sources.
method Maximum likelihood estimation with fixed-point iteration.
result Model increases system likelihood and reveals neural connections.
Survey of methods to visualize neural network features.
problem Understanding neural network activation patterns.
method Activation Maximization and Feature Visualization via Optimization.
result Probabilistic interpretation of AM techniques.
WaveNet reconstructs speech from brain activity, revealing acoustic features.
problem Reconstructing speech from brain activity with limited data.
method WaveNet model applied to STG intracranial recordings.
result WaveNet models reveal phoneme-level acoustic features.
The paper proposes a new model to capture specialized brain regions using mixture of regression experts.
problem Learning a forward mapping that relates stimuli to brain activation assumes all regions respond similarly, ignoring brain specialization.
method Clustering brain regions, learning different linear regression models for each cluster, using a mixture of linear experts.
result The proposed model predicts brain activation more accurately than conventional models.
A new method LDHA improves fMRI data alignment for cognitive state validation.
problem Accurate functional alignment across different subjects for MVP analysis.
method LDHA incorporates LDA into CCA for supervised fMRI alignment.
result LDHA outperforms other HA methods in MVP analysis.
A new encoding framework predicts brain activity from visual stimuli and intrinsic brain connections.
problem Traditional encoding models ignore brain inner states, limiting their performance in natural image identification.
method Proposes a novel encoding framework combining external stimuli and brain inner states, using a forward encoding model and an inner state model.
result The framework achieves better performance on natural image identification from fMRI responses than traditional models.
Combines psychological ratings with machine learning to map images to psychological similarity spaces.
problem Mapping images to psychological similarity spaces with machine learning.
method Combines psychologically derived similarity ratings with machine learning techniques to constrain the learning process.
result Results support the feasibility of mapping images to psychological similarity spaces.
AuGMEnT network struggles with long-term memory for hierarchical tasks.
problem Learning and memory in neural networks, especially hierarchical tasks.
method Introduced hybrid AuGMEnT with leaky and non-leaky memory units.
result Hybrid AuGMEnT solves hierarchical and distractor tasks.
Study confirms sparse coding in whole brain using MRI data.
problem Sparse coding in the whole brain's neural activities.
method Applied various matrix factorization methods to fMRI data.
result Sparse coding hypothesis in information representation in the whole human brain is confirmed.
Deep neural network features model human image categorization.
problem Modeling human categorization using natural images.
method Used convolutional neural network features to model human behavior.
result Representations from deep neural networks can model human natural image classifications.
The workshop explores AI and neuroscience, focusing on complex stimuli and neural network models.
problem Understanding and modeling brain function using AI and machine learning.
method Combining cognitive neuroscience with AI techniques like deep learning and attention mechanisms.
result The need for rich vector representations of stimuli for effective machine learning.
Graphical models are widely used in scienti fic and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce s…
Study uses simulation-based inference to decode brain activity from synthetic stimuli.
problem Reversing the process of brain activity emulation to recover stimuli or their properties.
method Pairing brain emulator with LLMs to learn a probabilistic mapping from brain maps to stimulus parameters.
result LLMs can serve as controllable stimulus generators and parameters can be recovered from brain maps.
Unified cognitive system from diverse fMRI studies using neural representations.
problem Aggregate heterogeneous brain function data into a universal cognitive system.
method Multi-task learning and multi-scale dimension reduction to learn low-dimensional cognitive representations.
result Achieves best prediction performance on large reference datasets and small datasets.
Novel Bayesian model improves EEG-based BCI character selection.
problem Accurately identifying target-related responses in EEG-based BCIs.
method Probit-link Split-and-merge Gaussian Process (P-SMGP) prior for feature selection.
result Reduces computational complexity and provides interpretable statistical interpretations.
In this study, the fluctuation-dissipation theory is invoked to shed light on input-output interindustrial relations at a macroscopic level by its application to IIP (indices of industrial production) data for Japan. Statistical noise arising from finiteness of the time series data is carefully removed by making use of…
SHA improves fMRI alignment for cognitive state discovery.
problem Optimal functional alignment in MVP analysis for multi-subject fMRI data.
method Supervised Hyperalignment (SHA) method that maximizes correlation within same categories and minimizes between distinct categories.
result SHA achieves up to 19% better performance for multi-class problems.
This work uses GANs to generate realistic vehicle test inputs.
problem Lack of realistic input sequences for automotive testing.
method Applying GANs to learn from unlabeled in-vehicle signals and generate synthetic inputs.
result Improved virtual test coverage and reduced need for expensive field tests.
VR game data for P300 BCI with raccoon vs demon stimuli.
problem Developing confidence metrics for P300 BCI.
method Multiclass labeled P300 dataset in VR game context.
result Estimation of model's confidence in stimulus predictions.
Model captures context-dependent neural correlations using Poisson mixtures.
problem Capturing context-dependent noise correlations in neural populations.
method Conditional finite mixtures of Poisson distributions, cross-validation for dimensionality, EM algorithm.
result Model successfully captures stimulus-dependent correlations in V1 neuron responses.
Study extends cognitive modeling to natural images, revealing the importance of image representation.
problem Extending cognitive modeling to natural images and understanding human categorization.
method Conducted a large-scale study with over 500,000 human judgments. Used deep and shallow machine learning methods to represent images. Applied psychological models of categorization to natural images.
result Simple models with abstract prototypes outperform complex exemplar accounts when using expressive, data-driven image representations.
Bayesian model improves BCI performance for ALS users.
problem Classifying EEG signals for P300 BCIs with low SNR and complex correlations.
method GLASS model with Gaussian Latent channel and Sparse time-varying effects.
result GLASS substantially improves BCI performance in ALS users.
Rotation-equivariant CNN reveals common features in V1 neurons.
problem V1 models fail to predict natural stimuli responses accurately.
method Rotation-equivariant convolutional neural network model.
result Rotation-equivariant network outperforms regular CNN and reveals common features.
New method allows sheets to morph into multiple shapes via spatially varying stimuli.
problem Limitation of current shape-programmed sheets to achieve only one target geometry.
method Patterning the stimulus itself for spatiotemporal control over local deformation magnitudes.
result A single physical sample can be induced to traverse a continuous family of target geometries.
Generator network replicates AAM's face recognition neuron responses.
problem Replicating AAM's face recognition neuron responses using a deep generative model.
method Using a variational auto-encoder, learned generator network from face images generated by AAM, capturing shape variations without explicit shape model.
result Inferred latent variables of the learned generator network have strong linear relationship with AAM's shape and appearance variables.
New method for nonlinear Granger causality improves predictive relationships.
problem Challenges in applying Granger causality to nonlinear data.
method Permutation of covariate set, artificial neural networks, consistent variance estimation.
result Permutation method outperforms other techniques in predicting nonlinear relationships.
A graph-based model aligns unaligned fMRI data across subjects efficiently.
problem Aligning fMRI data from different subjects with varying responses to stimuli.
method Develops a graph-based model to represent similarities between fMRI samples, regularizes the framework for efficient optimization, and uses kernel-based feature extraction.
result The method outperforms state-of-the-art techniques on both temporally-aligned and unaligned fMRI data.