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.
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.
Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts whose cortical position is highly variable. As such, most ali…
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.
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.
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.
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.
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.
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.
Algorithm detects influential observations in high-dimensional data.
problem Challenges in identifying influential observations in high-dimensional datasets.
method Three-step algorithm based on expectiles and asymmetric correlations.
result Higher detection power than competing methods.
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 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.
Study uncovers that loan maturity layers in interbank networks are crucial for understanding their structure and functions.
problem Lack of maturity details in interbank lending networks hinders understanding of network structure and functions.
method Used a complete interbank loan contract dataset and applied the layered stochastic block model to investigate multiple maturity layers.
result Optimal maturity granularity reveals specific economic functions, such as liquidity intermediation and financing.
The paper adjusts stock and strike prices for dividends after maturity in stock call pricing.
problem Inconsistent pricing of European calls with dividends after maturity.
method Extension of the Black-Scholes formula to include dividends after maturity.
result Model-consistent pricing of calls over all maturities with dividends after maturity.
Improved AMM protocol supports diverse loan maturities in DeFi.
problem Challenges in designing AMMs for fixed-income lending with time-related complexities.
method Generalized BondMM protocol to support arbitrary maturities.
result BondMM-A protocol demonstrates superior performance in interest rate stability and financial robustness.
The paper analyzes the mathematics of the relationship between the default risk and yield-to-maturity of a coupon bond. It is shown that the yield-to-maturity is driven not only by the default probability and recovery rate of the bond but also by other contractual characteristics of the bond that are not commonly assoc…
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…
Bitcoin market matures as statistical properties approach mature market indicators.
problem Assessing maturity of Bitcoin market using statistical properties.
method Analysis of 1-minute price changes since 2012, focusing on return distributions, volatility autocorrelation, Hurst exponents, and multiscaling effects.
result Statistical properties of Bitcoin market approach mature market indicators over time.
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.
The new notion of maturity-independent risk measures is introduced and contrasted with the existing risk measurement concepts. It is shown, by means of two examples, one set on a finite probability space and the other in a diffusion framework, that, surprisingly, some of the widely utilized risk measures cannot be used…
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.
Flexible model captures commodity skews with maturity effects.
problem Capturing market skew in commodity futures with maturity effects.
method Non-parametric extension with leverage functions, calibrated using Monte Carlo simulation.
result Model accurately captures market smile and implied variance accumulation.
The paper analyzes short maturity Asian options using large deviations theory.
problem Efficiency of existing methods for small maturities and volatilities.
method Large deviations theory and a local volatility model with a jump term.
result Asymptotics for Asian options are derived, showing rare event behavior for out-of-the-money options and more complex behavior for at-the-money options.
The paper analyzes Asian options in local volatility models at short maturity.
problem Short-maturity pricing and hedging of Asian options in local volatility models.
method Approximation of local volatility model by Gaussian process at short maturity, combined with Malliavin calculus.
result Short-maturity Asian option prices and delta values approximate European counterparts with a specific volatility function.
Study short maturity Asian options in jump-diffusion models with local volatility.
problem Analyzing Asian options pricing in models with jumps and local volatility.
method Asymptotic analysis for short maturity, considering fixed and floating strike options.
result Explicit results for Asian option prices in several models, including Merton, double-exponential, and Variance Gamma models.
This paper proposes a Monte Carlo technique for pricing the forward yield to maturity, when the volatility of the zero-coupon bond is known. We make the assumption of deterministic default intensity (Hazard Rate Function). We make no assumption on the volatility of the yield. We actually calculate the initial value of …
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.
We correct a mistake in the published version of our paper. Our new conclusion is that the "implied leverage effect" for single stocks is underestimated by option markets for short maturities and overestimated for long maturities, while it is always overestimated for OEX options, except for the shortest maturities wher…
Study short-maturity VIX and European option prices with jumps.
problem Analyzing VIX and European options with jumps in short-maturity models.
method Local-stochastic volatility models with compound Poisson jumps, leading-order asymptotics in closed-form.
result Closed-form solutions for VIX and European option prices in short-maturity models.
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.
Causal inference concerns the identification of cause-effect relationships between variables, e.g. establishing whether a stimulus affects activity in a certain brain region. The observed variables themselves often do not constitute meaningful causal variables, however, and linear combinations need to be considered. In…
Improved Heston model produces steeper smile for short maturities.
problem Implied volatility surface does not produce a steep enough smile for short maturities.
method Introduced Stationary Heston model with invariant measure and used Product Recursive Quantization for numerical solution.
result Stationary Heston model produces a steeper smile for short maturities.
Asymptotic analysis of short-maturity options on realized variance in local-stochastic volatility models.
problem Analyzing the behavior of short-maturity options on realized variance in local-stochastic volatility models.
method Large deviations theory and variational problems to solve rate functions for different cases.
result Explicit solutions for the rate function in the uncorrelated case and upper/lower bounds and expansions for the correlated case.
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 …
We present a rigorous study of the short maturity asymptotics for Asian options with continuous-time averaging, under the assumption that the underlying asset follows the Constant Elasticity of Variance (CEV) model. We present an analytical approximation for the Asian options prices which has the appropriate short matu…