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Non-stationary dynamic mode decomposition
Many physical processes display complex high-dimensional time-varying behavior, from
global weather patterns to brain activity. An outstanding challenge is to express high …
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
Abstract Latent Variable Models (LVMs) propose to model the dynamics of neural
populations by capturing low-dimensional structures that represent features involved in …
populations by capturing low-dimensional structures that represent features involved in …
Reducing power requirements for high-accuracy decoding in iBCIs
Objective. Current intracortical brain-computer interfaces (iBCIs) rely predominantly on
threshold crossings ('spikes') for decoding neural activity into a control signal for an external …
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 …
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 …
neural activities. Sleep spindle, a rare brain signal, is considered to be associated with …