Novel low-rank neural decoder improves -ECoG neural decoding.
problem Challenging neural decoding from high-dimensional -ECoG data.
method Low-rank structure in neural network decoder.
result Low-rank decoder outperforms standard PCA.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,291 papers · 148 categories
Trend · papers per month
48 results for “neural cortex”
Tiny Eats GRU detects eating episodes on a microcontroller.
problem Automatic dietary monitoring on low-power devices.
method Shallow gated recurrent unit (GRU) architecture on Arm Cortex M0+.
result Tiny Eats GRU achieves 95.15% accuracy with 4% memory usage and 6 ms latency.
Decodes neural activity to detect context effects in natural settings.
problem Detecting context-dependent changes in neural encoding.
method Decoding-based approach controlling for confounding factors.
result Demonstrates context-dependent changes in neural encoding.
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.
FANN-on-MCU enables efficient neural network inference on IoT devices.
problem Energy-efficient neural network inference on resource-constrained IoT devices.
method Open-source toolkit for ARM Cortex-M and RISC-V microcontrollers.
result Efficient neural network execution with low latency and power consumption.
DCNs mimic neuronal networks for improved neural classification.
problem Lack of topological similarity between DNNs and biological neural networks.
method Developed DCNs with topologies inspired by real-world neuronal networks.
result High classification accuracy achieved by DCNs.
Machine learning improves neural decoding performance.
problem Traditional neural decoding methods are inefficient.
method Apply modern machine learning algorithms (neural networks, gradient boosting) for neural decoding.
result Modern methods significantly outperform traditional approaches.
Deep learning models outperform classical methods in forecasting neural activity.
problem Improving forecasting of neural activity using deep learning models.
method Systematic evaluation of eight probabilistic deep learning models against classical statistical models and baseline methods.
result Several deep learning models consistently outperform classical approaches in forecasting neural activity.
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.
New model for visual cortex border completion using bicycle wheel motions.
problem Understanding border completion in the visual cortex V1.
method Sub-Riemannian Hamiltonian formalism and bicycle wheel analogy.
result Analogies between visual cortex border completion and bicycle wheel motions.
Fault-tolerant neural networks inspired by biological error correction codes.
problem Achieving reliable computation with unreliable neurons.
method Using biological error correction codes from grid cells in the mammalian cortex to develop a fault-tolerant neural network.
result Noisy biological neurons operate below a fault-tolerance threshold, suggesting a mechanism for reliable computation in the brain.
A new geometric model for V1 hypercolumns combines symplectic and spherical models.
problem Understanding the structure of V1 hypercolumns in the visual cortex.
method A differential geometric model based on conformal geometry.
result Combines features of symplectic and spherical models of hypercolumns.
A new model of V1 using orientation, frequency, and phase.
problem Understanding the complex behavior of V1 simple cells.
method Developed a sub-Riemannian model based on Gabor functions.
result The model enhances images using orientation, frequency, and phase.
Learning with hidden variablesq-bio.NC
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…
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.
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.
TMNs model brain memory systems for continual learning.
problem Catastrophic forgetting in neural networks.
method Triple Network architecture of GANs, incorporating brain-inspired algorithms.
result New state-of-the-art performance on class-incremental learning benchmarks.
This paper tackles quantum machine learning by embedding nonlinear functions in topographic representations.
problem Challenges of nonlinear processes in quantum machine learning.
method Topographic representation of information for quantum machine learning.
result Nonlinear functions can be embedded in unitary processes.
Improved cardiac arrhythmia detection in wearable devices with neural networks.
problem Resource constraints in low-power wearable devices for accurate arrhythmia detection.
method Adapted a convolutional-recurrent neural network to a low-power microcontroller, optimizing for precision and memory usage.
result Reduced score from 0.8 to 0.784 in fixed-point precision, with a 195.6KB memory footprint and 33.98MOps/s throughput.
Modeling curvature-sensitive cells in visual cortex with geometric structures.
problem Understanding the functional architecture of curvature-sensitive cells in the visual cortex.
method Geometric model based on Engel structure and SIM(2) symmetry.
result Identified SIM(2) as the natural symmetry group for curvature-sensitive cells.
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.
New CNN architecture separates 'what' and 'where' in neural data.
problem Estimating individual receptive field locations in neural data.
method Sparse readout layer factorizing spatial and feature dimensions.
result Our network outperforms current models in system identification.
High-contrast "gaudy" images improve the training of deep neural network models of visual cortexcs.CV
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.
Modeling curvature-sensitive cells in visual cortex using manifold geometry.
problem Understanding how curvature influences cell function in the visual cortex.
method Developed a 4D manifold with canonical Engel structure to represent orientation, position, curvature, and scale.
result Characterized curvature-sensitive receptive profiles using left-invariant generators of the Engel structure.
The paper extends the functional geometry of the visual cortex to more complex architectures using contactization and symplectization.
problem Understanding the functional architecture of the visual cortex.
method Contactization and symplectization processes to extend the dimension of the space.
result Extension of the functional geometry of the visual cortex to more complex architectures.
The apparent stochasticity of in-vivo neural circuits has long been hypothesized to represent a signature of ongoing stochastic inference in the brain. More recently, a theoretical framework for neural sampling has been proposed, which explains how sample-based inference can be performed by networks of spiking neurons.…
Automated framework optimizes DNN deployment on Arm CPUs.
problem Lack of globally optimised DNN deployment across software levels.
method Reinforcement Learning search for automated design space exploration.
result Up to 4x improvement in performance and 2x reduction in memory.
New eGRU unit improves keyword spotting on ultra-low-power devices.
problem Resource constraints of edge devices for neural network deployment.
method Optimized recurrent unit architecture for ultra-low power.
result eGRU is 60x faster and 10x smaller than GRU, maintaining accuracy.
BNNs enhance reservoir computing by acting as generalization filters.
problem Understanding how BNNs integrate with reservoir computing.
method Optogenetics and calcium imaging to record BNNs, reservoir computing framework.
result BNNs improve reservoir computing performance through generalization.
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 …
New method learns complex brain signal patterns from EEG/MEG data.
problem Complex waveforms in brain signals not captured by linear filters.
method Multivariate convolutional sparse coding (CSC) algorithm.
result Reveals non-sinusoidal mu-shaped patterns in brain signals.
Spiking neural networks perform similarly to deep networks on occluded images.
problem Robust object recognition in partially occluded images.
method Developed a two-layer spiking neural network trained on natural scenes with a biologically plausible learning rule, compared to deep convolutional networks.
result Spiking neural networks achieve good accuracy and robustness on stepwise pixel erasement tasks.
EAST compresses deep ConvNets for tiny memory nodes.
problem Memory constraints in tiny devices for deep ConvNets.
method Encoding-Aware Sparse Training (EAST) with adaptive group pruning and LZ4 weight encoding.
result EAST achieves deep memory compression with lower sparsity and higher accuracy.
3D good continuation model explains stereo vision using neurogeometry.
problem Understanding how the brain processes 3D visual correspondence.
method Developed a neurogeometric model involving spatial and orientation disparities.
result Provides insight into neural organization and correspondence problem.
A deep neural network learns to balance memory formation and network activity with context sensitivity.
problem Improving neural network performance with limited resources.
method Integrates biological hippocampal pathways into a deep convolutional network with context-sensitive bias.
result Demonstrates a significant performance increase in a constrained network architecture.
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.
Neural population activity often exhibits rich variability and temporal structure. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as "non-stationarity". To better …
New method visualizes brain activity changes over time.
problem Understanding representational dynamics in neural responses.
method Procrustes-aligned Multidimensional Scaling (pMDS) on RDM movies.
result Multidimensional scaling alignment captures representational dynamics.
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaceseess.SP
DyEnsemble improves BCI accuracy by adapting to nonstationary neural signals.
problem Nonstationary neural signals in BCI cause decoding errors.
method Dynamic ensemble modeling that learns and combines diverse models online.
result DyEnsemble outperforms Kalman filters, especially with noisy signals.
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detectioneess.IV
Graph embedding improves fMRI classification and reveals brain region differences in ASD.
problem Difficult to embed informative brain fMRI representations due to high dimensionality and low SNR.
method Modelled fMRI as a graph, used GNN to learn from graph data, incorporated mutual information loss (Infomax).
result Infomax graph embedding improves classification performance and reveals separable nodal representations of ASD and HC groups.
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.
Develops visual explanations for Alzheimer's disease classification using 3D-CNNs.
problem Improving understanding of Alzheimer's disease classification using 3D-CNNs.
method Three approaches: sensitivity analysis and two activation visualization methods.
result Visual explanations identify important brain parts for Alzheimer's disease diagnosis.
Adaptive lateral connections improve visual action recognition.
problem Feedforward neural models lack feedback and lateral connections like the primate visual cortex.
method Dynamic weights in recurrent lateral connections, iteratively reintroduced input.
result Significant performance gains in visual action recognition without pretraining.
Meta-Dynamic models learn shared neural dynamics across tasks.
problem Learning latent dynamics from neural recordings across different tasks.
method Captures variabilities on a low-dimensional manifold to meta-learn dynamics.
result Meta-Dynamic models can rapidly learn latent dynamics from new recordings.
Introduces basic facts on early vision's functional architecture.
problem Accessibility of early vision's functional architecture for non-specialists.
method Survey of three neurogeometric models and discussion of the conformal model.
result Synthesis of symplectic and spherical models into the conformal model.
Unified framework models neural decision-making, improving accuracy.
problem Limitations in modeling neural activity during decision-making.
method Unifying framework based on state-space models with scalable inference.
result Two-dimensional accumulator better captures neural responses.
Unsupervised neural models predict brain activity better than supervised methods.
problem Understanding how the brain represents visual information without direct supervision.
method Built upon PredNet, used RSA to compare PredNet representations to fMRI and MEG data.
result Unsupervised models trained to predict video frames outperform supervised image classification models in predicting brain activity.
Smooth kernel regularizer improves deep neural networks' performance with less data.
problem Deep neural networks need large datasets for effective learning.
method Proposes a smooth kernel regularizer that encourages spatial correlations in convolution kernel weights, learned from previous experience.
result The smooth kernel regularizer improves visual recognition models over an L2 regularization baseline.