arXiv research

Study recovers spike order in noisy tensor estimation without SNR assumptions.

Study reveals efficient recovery of multi-modal signals via Bayesian methods and sequential learning.

SGD recovers multiple signal vectors in noisy tensor PCA.

Study of Langevin dynamics for tensor PCA recovery in high dimensions.

SMM improves signal recovery from noisy data.

QAOA matches classical tensor power iteration in spiked tensor model recovery.

Study shows generative priors improve rank-one matrix recovery with optimal sample complexity.

In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, a…

Spike sorting is a fundamental preprocessing step in neuroscience that is central to access simultaneous but distinct neuronal activities and therefore to better understand the animal or even human brain. But numerical complexity limits studies that require processing large scale datasets in terms of number of electrod…

RBM learns in high dimensions via AMP and GD, reaching optimal weak recovery.

Paper optimizes tensor deflation for non-orthogonal signals.

Study on signal-plus-noise decomposition in nonlinear spiked random matrices.

PLS-SVD struggles with missing data in multimodal datasets, showing a phase transition in performance.

Study reveals limits of PLS in multi-modal learning with correlated signals.

Estimating the leading principal components of data, assuming they are sparse, is a central task in modern high-dimensional statistics. Many algorithms were developed for this sparse PCA problem, from simple diagonal thresholding to sophisticated semidefinite programming (SDP) methods. A key theoretical question is und…

Estimation of low-rank matrices is of significant interest in a range of contemporary applications. In this paper, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is…

Paper proposes a new method for PCA using generative models.

This study optimizes multi-modal learning thresholds and algorithms in high dimensions.

New method recovers signals from compressed measurements using generative networks with contractive layers.

Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification. It is well-known that solving for the sparse coefficient vector to ma…

AMP algorithm for matrix tensor product model provides recovery conditions.

SMPI recovers tensor spikes from noisy data with improved performance.

In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events. We perform parameter and state estimation using a dynamic compressed sensing framework and develop an efficient solution consisting of two nes…

Single-spike neurons can approximate as well as multi-spike neurons.

Much of studies on neural computation are based on network models of static neurons that produce analog output, despite the fact that information processing in the brain is predominantly carried out by dynamic neurons that produce discrete pulses called spikes. Research in spike-based computation has been impeded by th…

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.

SNNs enhance high-frequency price spike forecasting in HFT environments.

SNNs can represent complex functions efficiently.

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Ge…

New method identifies important features and interactions in RF models.

Affine spiking neural networks learn efficiently and generalize well.

Proposes a Bayesian approach for automatic node selection in sparse neural networks.

Developing electrophysiological recordings of brain neuronal activity and their analysis provide a basis for exploring the structure of brain function and nervous system investigation. The recorded signals are typically a combination of spikes and noise. High amounts of background noise and possibility of electric sign…

Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work. The main challenge lies in that the combination of the ambient noise with dynamic baseline fluctuation, often contaminates the observations, thereby deteriorating the reliability o…

New method improves neural spike train models by minimizing divergence directly, leading to better performance.

New method detects spike-and-wave epileptiform discharges using Kendall's Tau-b.

Develops generic spike-and-slab priors for high-dimensional linear regression.

T2FSNN improves deep SNNs by reducing spikes and latency.

A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced by Johnstone, in which a prominent eigenvector (or "spike") is planted into a random matrix. These distributions form natural statistical models for principal component analysis (PCA) problems throughou…

Improved SNNs with quantized activations outperform traditional networks.

A learning rule for first-spike times in neural networks reduces energy consumption and reaction times.

New method uses surrogate gradients to train efficient spiking networks on neuromorphic hardware.

This research bridges binary and spiking neural networks for efficient on-chip AI.

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in-vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in-silico.…

PCA++ improves robustness to background noise in contrastive learning.

This paper analyzes generalization for linear models with spiked covariance structures.

This paper suggests a learning-theoretic perspective on how synaptic plasticity benefits global brain functioning. We introduce a model, the selectron, that (i) arises as the fast time constant limit of leaky integrate-and-fire neurons equipped with spiking timing dependent plasticity (STDP) and (ii) is amenable to the…