New method detects spike-and-wave epileptiform discharges using Kendall's Tau-b.
problem Detecting spike-and-wave epileptiform discharges in EEG signals.
method Proposes a new method based on Kendall's Tau-b coefficient.
result High Specificity and rule in (SpPIn) for spike-and-wave discharge detection.
Bayesian method detects neuron spike activities from noisy fluorescence data.
problem Detecting neuron spike activities from noisy fluorescence data.
method Random finite set (RFS) based Bayesian approach.
result Gains 12% extra detection accuracy compared to MLSpike method.
PCA can detect a low-rank signal in spiked random matrix models, but not always optimally.
problem Understanding when PCA can detect a low-rank signal in the presence of noise.
method Le Cam's notion of contiguity, analysis of spiked Wishart ensemble, and non-spectral tests.
result PCA is sub-optimal for detection in non-Gaussian Wigner ensembles and certain negative spikes in Gaussian Wishart ensemble.
QuPWM detects epileptic spikes in MEG signals with high accuracy.
problem Manual detection of epileptic spikes in MEG signals is time-consuming and subjective.
method QuPWM combines PWM and SVM for feature extraction and classification.
result Average accuracy of 98% achieved on a balanced dataset of 3104 samples.
Paper proposes a k-NN classifier for detecting spike-and-wave seizures in EEG.
problem Early detection of epileptic seizures in EEG signals.
method Uses t-location-scale distribution and k-nearest neighbors classifier.
result Demonstrates improved classification accuracy, sensitivity, and specificity on real data.
Spiking-YOLO improves object detection with low power and fast convergence.
problem Challenging object detection tasks with spiking neural networks.
method Channel-wise normalization and signed neuron with imbalanced threshold.
result Spiking-YOLO achieves comparable results to Tiny YOLO but with significantly less energy consumption.
Novel method detects spike-and-wave patterns in EEG signals.
problem Manual classification of spike-and-wave discharges in EEG signals is time-consuming and error-prone.
method The method divides EEG signals into time segments, applies Morlet 1-D decomposition, extracts scale, variance, and median from wavelet coefficients, and uses a k-NN classifier.
result The proposed method achieved 100% accuracy in detecting spike-and-wave patterns.
Study detects signals in spiked Wigner models using log likelihood ratio.
problem Detecting signals in rank-one spiked Wigner models with non-Gaussian noise.
method Proved asymptotic normality of log likelihood ratio and computed error thresholds.
result Optimal signal-to-noise ratio threshold for reliable detection.
Perfect spike detection method derived and benchmarked.
problem Missing spikes in neuronal network simulations.
method Time-inverted dynamics to test threshold crossing.
result Method can be faster than alternative perfect tests.
Algorithm detects and estimates correlated signals in spiked matrices.
problem Detect and estimate correlated signals in spiked matrices.
method Proposes an efficient algorithm based on counting edge-decorated cycles.
result Algorithm succeeds under certain signal-to-noise ratio conditions.
Study on limits of detecting a rank-one perturbation in Wigner matrices.
problem Detecting an additive rank-one perturbation in Wigner matrices.
method Gaussian interpolation methods and rigorous incarnation of the cavity method.
result Established the maximal region of contiguity between planted and null models, marking a phase transition for both estimation and detection.
Algorithm detects hidden spike-patterns in neural networks with one-shot learning.
problem Detecting hidden spike-patterns in high activity neural networks.
method Constructive algorithm using spike-timing-dependent plasticity (STDP) and lateral inhibition.
result Successful one-shot detection of new spike-patterns after long intervals.
Develops a new point process model for detecting neural spike sequences.
problem Detecting sparse sequences of neural spikes in high-dimensional spike trains.
method A point process model that represents sequence occurrences as marked events in continuous time, with learnable time warping parameters.
result Demonstrates improved detection and modeling of neural spike sequences.
Study on detecting a single spike in high-dimensional data matrices.
problem Detecting a single unknown spike in high-dimensional rectangular data matrices.
method Analysis of likelihood ratio between spiked and null models, using Gaussian fluctuations and Talagrand's interpretation of cavity method.
result Asymptotic Gaussian fluctuations of the likelihood ratio below the BBP threshold, with open maximal parameter region.
Improves signal detection in non-Gaussian noise using transformed data.
problem Signal detection in rank-one signal-plus-noise data matrices.
method Pre-transforming matrix entries and using linear spectral statistics for hypothesis testing.
result Sharp phase transition of largest eigenvalues in spiked rectangular matrices.
Novel NDL framework improves spike detection accuracy and channel localization in EEG/MEG data.
problem Manual spike identification is time-consuming and requires specialized training.
method Nested Deep Learning (NDL) framework that combines signals across all channels.
result Improves prediction accuracy and achieves better channel localization.
Improves detection of low-rank signals from noisy data matrices.
problem Statistical detection of low-rank signals in noisy data matrices.
method Entrywise pre-transforming data matrix for non-Gaussian noise, sharp phase transition thresholds, central limit theorem for linear spectral statistics, hypothesis test.
result Improves detection of low-rank signals from noisy data matrices, generalizing known results.
PCA can detect a low-rank signal but is suboptimal for non-Gaussian matrices and synchronization problems.
problem Understanding the optimal and suboptimal performance of PCA in spiked random matrix models.
method Analysis of spiked random matrix models, including Gaussian and non-Gaussian Wigner ensembles, and synchronization problems.
result PCA achieves optimal detection for Gaussian Wigner ensembles under benign priors but is suboptimal for non-Gaussian matrices and synchronization problems.
This paper compares spike sorting techniques for rat brain neuronal activity.
problem Improving the accuracy of spike sorting for neuronal activity analysis.
method Three-step spike sorting process: detection, feature extraction, and clustering. Various methods are compared.
result Kernel PCA outperforms in feature extraction, leading to better spike sorting results.
Paper trains SNNs for classification using first-to-spike decoding.
problem Training SNNs for classification under GLM model.
method Proposes first-to-spike decoding method for SNNs.
result Improves accuracy and efficiency of SNN classification.
Paper quarantines unreliable Yelp users by detecting review spam.
problem Unreliable and spamming users deceive Yelp's users.
method Used RSD and spam detection techniques on key features.
result More than 80% of Yelp's accounts are unreliable, and highly-rated businesses are often spammed.
A test for weak signal detection in noisy data matrices.
problem Detecting a weak signal in a noisy Wigner matrix when the signal-to-noise ratio is small.
method Utilizes linear spectral statistics and hypothesis testing on the data matrix.
result The proposed test is optimal when the noise is Gaussian and can be improved with known noise density.
Model detects patterns in noisy binary data, explaining neuron activity in terms of cell assemblies.
problem Detecting structure in noisy or approximate repeats of patterns in sparse binary data.
method Probabilistic binary latent variable model based on Noisy-OR model, inferring sparse activity in latent variables.
result Model successfully extracts and explains latent structure in spiking neural data.
New spectral method for community detection in complex networks.
problem Community detection in heterogeneous large networks.
method Spectral methods based on α-parametrized normalized modularity matrix, with regularization of eigenvectors.
result Existence of an optimal value α_opt for best community detection and on-line estimation of it.
Study on signal-plus-noise decomposition in nonlinear spiked random matrices.
problem Nonlinear spiked random matrix models with rank-one signal and noise.
method Signal-plus-noise decomposition and phase transition analysis.
result Identified precise phase transitions in signal components at critical thresholds.
New fatiguing STDP rule helps SNNs learn spike timing from mixed rate codes.
problem STDP's sensitivity to input spike rates hinders learning from fine temporal correlations.
method Proposes a fatiguing STDP rule with short-term synaptic fatigue dynamics.
result FSTDP helps learn spike timing correlations from mixed rate codes.
VOWEL trains WTA-SNNs for multi-valued events, overcoming resource limitations.
problem Training WTA-SNNs for multi-valued events is challenging due to non-differentiability and recurrent behavior.
method Develops a variational online local training rule (VOWEL) for WTA-SNNs using local pre- and post-synaptic information and a common reward signal.
result VOWEL outperforms conventional binary SNNs in real-world neuromorphic datasets with multi-valued events.
This paper explores SNNs for automated driving, promising low-power efficiency.
problem Power consumption and cost in embedded processors for automated driving.
method Overview of SNNs and their potential for automated driving.
result SNNs show potential for automated driving applications with low power consumption.
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 fundamental challenge in calcium imaging has been to infer the timing of action potentials from the measured noisy calcium fluorescence traces. We systematically evaluate a range of spike inference algorithms on a large benchmark dataset recorded from varying neural tissue (V1 and retina) using different calcium indi…
STNMF method uncovers neural circuit components in retinal ganglion cells.
problem Deciphering complex neuronal circuit components in the brain.
method Spike-triggered non-negative matrix factorization (STNMF) method.
result STNMF can detect various properties of upstream bipolar cells and recover synaptic connection strengths.
New estimator for overlapping community detection in graphs.
problem Reconstructing community relations in graphs with overlapping memberships.
method Proposes a new estimator for Mixed-Membership Stochastic Block Model (MMSB) under diverging spiked eigenvalues condition.
result New estimator matches theoretical minimax lower bound on estimation error.
New method trains SNNs with spatio-temporal backpropagation.
problem Training SNNs with spatio-temporal information.
method Iterative LIF model, spatio-temporal backpropagation (STBP).
result Best performance on static and dynamic MNIST datasets.
CRsAE autoencoder learns dictionary for image denoising and brain spike detection.
problem Sparse dictionary learning for image and neural signal processing.
method CRsAE architecture, alternating E-step and M-step, FISTA for sparse coding, EM-inspired regularization.
result CRsAE outperforms conventional methods in image denoising and spike detection.
Hybrid pipeline detects spike-and-wave discharges in long-term EEG recordings.
problem Manual identification of spike-and-wave discharges in long-term EEG recordings is labour-intensive and error-prone.
method A hybrid pipeline that combines analytical features with a shallow ANN for SWD detection.
result The method correctly detected 384 out of 392 annotated SWD events, achieving high sensitivity and specificity.
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
Detects changes in signal covariance matrix using extreme eigenvalues.
problem Detects abrupt changes in signal covariance matrix from identity to low-rank.
method Sequential detection based on extreme eigenvalues of sample covariance matrix.
result Effective detection of behavior changes in swarm data.
Single-spike neurons can approximate as well as multi-spike neurons.
problem Limitation of single-spike neurons in spiking neural networks.
method Comparison of single-spike and multi-spike neural networks.
result Single-spike and multi-spike neural networks are equivalent in approximation capabilities.
Single spiking neuron outperforms ConvNets in counting weakly labeled concepts.
problem Counting weakly labeled concepts in MNIST task.
method Improved gradient-based local learning rule for leaky integrate and fire model.
result Single spiking neuron outperforms conventional ConvNets in MNIST counting task.
Novel connections between Neyman-Scott processes and Bayesian nonparametric mixture models enable scalable inference.
problem Efficiently modeling and detecting clusters in spatiotemporal data.
method Adapting collapsed Gibbs sampling for Neyman-Scott processes via connections to mixture of finite mixture models.
result Demonstrated scalability and effectiveness on neural spike trains and document streams.
Robust detection of transients and onset zones in noisy signals.
problem Detecting transients and onset zones in noisy signals robustly.
method Sparse and invariant representation based on wavelet transforms and scattering network.
result Frequency invariance improves robustness of transient detection.
Develops a monitoring procedure to detect changes in large approximate factor models.
problem Detecting structural changes in large approximate factor models.
method Randomises the test statistic to create a sequence of i.i.d. statistics for monitoring changes.
result Very small probability of false detections and tight detection times of change-points.
Gradient descent optimizes spiking neural networks for dynamic tasks.
problem Lack of efficient supervised learning algorithms for spiking networks.
method Differentiable formulation of spiking networks and exact gradient calculation.
result Optimizes spiking network dynamics on both spike and behavioral time scales.
mmFall detects falls using mmWave radar and a hybrid VRAE, achieving high accuracy.
problem Detecting falls accurately and privately using mmWave radar.
method Uses mmWave radar for body point cloud and centroid, combined with a hybrid VRAE for anomaly detection.
result Achieves 98% fall detection with 2 false alarms out of 50 falls.
Optimal test for detecting signal in noisy matrix model.
problem Signal detection in noisy matrix models with unknown rank.
method Hypothesis test based on linear spectral statistics, optimal under Gaussian noise.
result Optimal test under Gaussian noise, improved with non-Gaussian noise.
Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its…
The paper examines how spike strengths and alignments affect overfitting in linear regression models.
problem The impact of spike strengths and alignments on overfitting in linear regression models.
method Characterization of generalization error through exact expressions and analysis of spike strengths, aspect ratio, and target alignment.
result Increasing spike strength can lead to catastrophic overfitting before benign overfitting, especially in well-specified aligned problems.
SNNs enhance high-frequency price spike forecasting in HFT environments.
problem Conventional financial models fail to capture fine temporal structure in high-frequency price spikes.
method Application of Spiking Neural Networks (SNNs) with hyperparameter tuning via Bayesian Optimization (BO).
result SNN models optimized with PSA achieve significantly higher cumulative returns in backtesting.