Non-stationary dynamic mode decomposition

J Ferré, A Rokem, EA Buffalo, JN Kutz, A Fairhall - IEEE Access, 2023‏ - ieeexplore.ieee.org
Many physical processes display complex high-dimensional time-varying behavior, from
global weather patterns to brain activity. An outstanding challenge is to express high …

AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity

J Li, L Scholl, T Le, P Rajeswaran… - Advances in …, 2023‏ - proceedings.neurips.cc
Abstract Latent Variable Models (LVMs) propose to model the dynamics of neural
populations by capturing low-dimensional structures that represent features involved in …

Reducing power requirements for high-accuracy decoding in iBCIs

BM Karpowicz, B Bhaduri… - Journal of Neural …, 2024‏ - iopscience.iop.org
Objective. Current intracortical brain-computer interfaces (iBCIs) rely predominantly on
threshold crossings ('spikes') for decoding neural activity into a control signal for an external …

Constructing functional graphs and recurrent neural network models of neural dynamics using calcium imaging data in Python

V Porubsky - 2023‏ - search.proquest.com
Leveraging the power of computational analyses and modeling could assist with the study of
neural dynamics. Complex analyses like graph theory could enable biomarker discovery in …

FPGA Deployment of LFADS for Real-time Neuroscience Experiments

X Liu - 2023‏ - search.proquest.com
Neural networks have been widely used in neuroscience experiments to model and analyze
neural activities. Sleep spindle, a rare brain signal, is considered to be associated with …