MERLiN recovers causal signals from observed mixtures in EEG data.
problem Recovering causal signals from observed mixtures in neuroimaging data.
method MERLiN algorithms construct causal variables from non-causal variables using linear combinations.
result MERLiN allows recovery of a cortical signal affected by activity in a brain region, not directly caused by the stimulus.
Method decomposes neural signals into rhythmic and non-rhythmic components.
problem Analyzing complex neural signals with spatiotemporal dynamics.
method Linearized model of dynamic neural states, stochastic differential equations, Gaussian process regression.
result Demonstrates efficacy in identifying meaningful modulations of oscillatory signals.
Inspired by LSTMs, a new neural network model mimics cortical microcircuits.
problem Understanding the computational principles of cortical microcircuits.
method Introducing a gated-recurrent neural network (subLSTM) based on inhibitory cells.
result SubLSTM units achieve similar performance to LSTM units in sequential tasks.
Neural network predicts intelligence from brain structure measurements.
problem Predicting intelligence scores from brain structure measurements.
method Four-layer fully-connected neural network (FNN) using volumes, WM/GM contrast, and cortical thickness.
result Achieved MSE of 94.0270 in test set.
New method aligns brain regions across subjects better than existing approaches.
problem Inaccurate mapping of functional brain regions across different subjects in fMRI studies.
method Locally optimized registration method that maximizes functional correlation and allows for non-smooth deformations.
result Method outperforms existing alternatives in overlap and consistency of predicted regions.
Beta and gamma rhythms mediate different maturation trajectories of cortical networks.
problem Understanding how distinct cortical rhythms influence network maturation.
method Magnetoencephalography (MEG) to map frequency band-specific maturation from age 7 to 29 in 162 participants.
result Beta band mediated networks follow a linear trajectory, while gamma band networks follow an asymptotic one.
Paper proposes a new method for population-wise matching of sulcal graphs.
problem Challenges in matching cortical fold variations across individuals.
method Population-wise multi-graph matching of sulcal graphs.
result Effectiveness of multi-graph matching in obtaining consistent labeling of sulcal basins.
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
A neuro-inspired architecture learns without supervision using clustering and predictive coding.
problem Achieving continual learning without supervision.
method Neuro-inspired architecture based on online clustering and hierarchical predictive coding.
result The architecture achieves continual learning without supervision.
Learning and inferring features that generate sensory input is a task continuously performed by cortex. In recent years, novel algorithms and learning rules have been proposed that allow neural network models to learn such features from natural images, written text, audio signals, etc. These networks usually involve de…
Automated detection of MS lesions improves to 67% with 7T MRI.
problem Accurate detection of small, scarce cortical lesions in MS patients.
method 3D U-Net with brain tissue segmentation, supervised training on 7T MRI.
result 67% lesion detection rate with 42% false positives.
Improves MRI-based brain surface reconstruction with minimal deformation energy loss.
problem Ensuring optimal deformation energy and consistency in learning-based cortical surface reconstruction.
method Design and implementation of a Minimal Energy Deformation (MED) loss in the V2C-Flow model.
result Significant improvements in training consistency and reproducibility without sacrificing reconstruction accuracy and topological correctness.
In this study we present a kernel based convolution model to characterize neural responses to natural sounds by decoding their time-varying acoustic features. The model allows to decode natural sounds from high-dimensional neural recordings, such as magnetoencephalography (MEG), that track timing and location of human …
Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability. Regardless of their origin, fluctuations in these signal…
A new image completion method inspired by brain cells.
problem Image restoration from corrupted data.
method Biologically-inspired sub-Riemannian model with frequency and phase.
result Completion of two-dimensional images using cortical cell responses.
New method identifies key channels for extreme brain events.
problem Identifying channels responsible for extreme brain events like seizures.
method Extends canonical correlation to tail dependence, developing TPDM for clustering.
result Tail connectivity provides additional discriminatory power for seizure risk.
New algorithm recovers non-linear cause-effect relationships from mixed neuroimaging data.
problem Recovering meaningful cause-effect relationships from linearly mixed neuroimaging data.
method MERLiN (Mixture Effect Recovery in Linear Networks) algorithm, extended to handle non-linear cause-effect relationships.
result The algorithm can recover non-linear cause-effect relationships from linearly mixed neuroimaging data.
Neural networks learn to mimic brain neurons with two-input activation functions, improving performance and robustness.
problem Training neural networks to mimic the complex interactions of brain neurons.
method Developed a network-in-network architecture with two-input activation functions, optimized hyperparameters, and compared to conventional ReLU networks.
result Two-input activation functions can learn soft XOR functions, improving network performance and robustness.
The paper develops models to understand sensory coding and cortical topography.
problem Understanding how visual cortical areas' receptive fields and topographic maps relate to environmental statistical structure.
method Energy-based models applied to probability density estimation, constrained by biological constraints.
result The models qualitatively reproduce receptive field and map properties found in vivo.
A new model uses 'ghost units' to enable efficient backpropagation in deep neural networks.
problem How to achieve efficient backpropagation in deep neural networks with biological plausibility.
method Introduces 'ghost units' to cancel feedback, enabling efficient error backpropagation.
result Demonstrates that the model can approximate error gradients and achieve good performance on classification tasks.
This work optimizes reservoir computing models by linking recurrence and non-linear dynamics.
problem Understanding how recurrence and non-linear dynamics in cortical networks contribute to their function.
method Transformed time-continuous, recurrent dynamics into an effective feed-forward structure of linear and non-linear temporal kernels.
result Optimal time-series classifiers can be built from random reservoir networks, demonstrating significant performance gains.
RGPs connect predictive coding to Bayesian inference, providing a neural substrate.
problem Scalable implementations of Bayesian inference respecting neurobiological constraints.
method Formal connection between predictive coding and Recursive Gaussian Processes (RGPs).
result RGPs intrinsically implement hierarchical Bayesian inference and uncertainty propagation.
V1 cortex reconstructs images as Poisson equation solutions with varying weights.
problem Reconstructing images from V1 cortical cell receptive profiles.
method Solves a heterogeneous Poisson equation with varying weights representing neural connectivity.
result Reconstructions converge to homogeneous solutions using homogenization techniques.
EEGNet creates a compact CNN for EEG-based BCIs across various paradigms.
problem Designing a single CNN for EEG-based BCIs that works across different paradigms.
method Use of depthwise and separable convolutions to create an EEG-specific model.
result EEGNet generalizes across paradigms better than current state-of-the-art approaches.
DIVE models brain disease progression with high spatial resolution.
problem Reconstruct long-term brain pathology from short-term data.
method Clusters vertex-wise biomarker measurements, estimates average trajectories, and identifies disease-specific patterns.
result Reveals distinct patterns of pathology in different diseases and biomarker types.
New method aligns brain data across individuals for better brain decoding.
problem Inter-individual variability in brain response patterns limits decoder generalization.
method SpectralOT method that embeds cortical geometry into Laplace-Beltrami eigenmodes.
result SpectralOT strikes balance between aligning functional features and preserving anatomical structure.
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…
Gaudy images help train deep neural networks with less data.
problem Training deep neural networks with limited real data from visual cortex neurons.
method Used high-contrast binarized natural images (gaudy images) to train DNNs.
result Reduced training data needed for accurate DNN predictions of visual cortex neuron responses.
Despite its size and complexity, the human cortex exhibits striking anatomical regularities, suggesting there may simple meta-algorithms underlying cortical learning and computation. We expect such meta-algorithms to be of interest since they need to operate quickly, scalably and effectively with little-to-no specializ…
New BMI interface stabilizes user experience over long periods.
problem Inconsistent neural signal data leads to interface recalibration.
method Adversarial Domain Adaptation Network to match residual distributions.
result Adversarial Domain Adaptation Network outperforms other methods.
New model explains visual illusions using sub-Riemannian geometry.
problem Mathematical modeling of Poggendorff-type visual illusions.
method Cortical-inspired sub-Riemannian model with sub-Laplacian evolution.
result Sub-Riemannian kernel enhances visual misperceptions and biases.
DKT transfers biomarker information between neurodegenerative diseases.
problem Estimating biomarker trajectories in rare neurodegenerative diseases with limited data.
method DKT is a joint-disease generative model that transfers biomarker progressions from common neurodegenerative diseases to rare ones.
result DKT estimates plausible multimodal biomarker trajectories in rare diseases like PCA using only unimodal data.
Machine learning for ASD diagnosis using morphological MRI networks.
problem Challenging to diagnose ASD using MRI due to heterogeneity and incomplete network neuroscience.
method Crowdsourced Kaggle competition to develop and benchmark ML pipelines.
result First-ranked team achieved 70% accuracy, 72.5% sensitivity, and 67.5% specificity.
Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables. The resulting diversity of neural tuning often obscures the represented information. Here we introduce a novel dimensionality reduction technique, demixed principal component analysis (dPC…
Model explains optical illusions using geometric sub-Riemannian geodesics.
problem Understanding and explaining geometrical optical illusions.
method Neuro-mathematical model based on sub-Riemannian geodesics in the Roto-Translation Group.
result Illusory contours are described as geodesics in a new metric.
Correlated noise improves deep CNN performance on occluded images.
problem Understanding and leveraging correlated variability in neural networks.
method Implemented correlated noise models in deep convolutional neural networks, defined as a function of neuron selectivity and distance.
result Correlated noise models often improve performance on occluded images compared to other regularization techniques.
Paper proposes a method to compare vector fields across surfaces, useful for analyzing brain folding patterns.
problem Comparing vector fields across surfaces of different geometries is challenging.
method The paper introduces a framework to transport vector fields onto a common space using differential geometry.
result The proposed framework enables the computation of statistics on vector fields, demonstrating its effectiveness in analyzing brain folding patterns.
VNNs transfer well across datasets for brain age prediction using cortical thickness features.
problem Predicting brain age using anatomical features.
method Transferability of coVariance neural networks (VNNs) in brain age prediction.
result VNNs can assign anatomical interpretability to elevated brain age gap in AD.
The paper relaxes constraints on predictive coding models, making them more biologically plausible.
problem Neurophysiological models of predictive coding are not fully biologically plausible.
method The paper relaxes constraints on standard predictive coding algorithms by removing neurally implausible features.
result The removal of neurally implausible features does not significantly affect learning performance.
Deep learning has recently led to great successes in tasks such as image recognition (e.g Krizhevsky et al., 2012). However, deep networks are still outmatched by the power and versatility of the brain, perhaps in part due to the richer neuronal computations available to cortical circuits. The challenge is to identify …
Model learns spatiotemporal patterns on graphs from longitudinal data.
problem Learning spatiotemporal patterns on graphs from longitudinal data.
method Mixed-effects model with stochastic Expectation-Maximization algorithm (MCMC-SAEM).
result Personalized model accurately predicts cortical thickness maps in patients.
The elastic net was introduced as a heuristic algorithm for combinatorial optimisation and has been applied, among other problems, to biological modelling. It has an energy function which trades off a fitness term against a tension term. In the original formulation of the algorithm the tension term was implicitly based…
Paper revisits graph-CNNs using Laplace-Beltrami spectral filters and polynomials.
problem Improving spectral graph convolutional neural networks (graph-CNNs).
method Developed Laplace-Beltrami CNN (LB-CNN) by replacing graph Laplacian with LB operator and approximating spectral filters using Chebyshev, Laguerre, and Hermite polynomials.
result Classification accuracy of LB-CNN is not dependent on the type of polynomials or operators.
Novel deep learning architecture decodes imagined speech from EEG.
problem Insufficient training samples for deep learning in decoding imagined speech.
method Employed a novel deep learning architecture with CSP for feature selection, DWT for feature extraction, and majority voting for classification.
result Achieved accuracies comparable to state-of-the-art results.
We propose a primitive called PJOIN, for "predictive join," which combines and extends the operations JOIN and LINK, which Valiant proposed as the basis of a computational theory of cortex. We show that PJOIN can be implemented in Valiant's model. We also show that, using PJOIN, certain reasonably complex learning and …
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model)…
A new model for complex cells accounts for insensitivity to image shifts.
problem Complex cells' response to image shifts.
method Linear combination of Gaussian derivatives at a single position.
result Maximum response insensitive to small shifts of the image.
New VAE models reveal hierarchical visual cortex computations.
problem Capturing hierarchical visual cortex computations in generative models.
method Sparse coding hierarchical VAEs trained on natural images with varied generative and recognition components.
result Representations similar to those in visual cortex emerge under inductive biases.