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Found 10 papers, 5 papers with code
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Time series data across scientific domains are often collected under distinct states (e. g., tasks), wherein latent processes (e. g., biological factors) create complex inter- and intra-state variability.
Multi-Lingual DALL-E Storytime
While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations.
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior.
Data Processing of Functional Optical Microscopy for Neuroscience
Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators.
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield
For example, in the Neuropixel probe, 960 electrodes can be addressed by 384 recording channels.
Visualizing the PHATE of Neural Networks
We demonstrate M-PHATE with two vignettes: continual learning and generalization.
Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer
Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations.
Interpreting Deep Learning: The Machine Learning Rorschach Test?
Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities.
An Efficient and Flexible Spike Train Model via Empirical Bayes
Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations.
Short Term Memory Capacity in Networks via the Restricted Isometry Property
Cortical networks are hypothesized to rely on transient network activity to support short term memory (STM).
