Building population models for large-scale neural recordings: opportunities and pitfalls
arXiv:2102.01807
Abstract
Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.
References in corpus (11)
- Weak pairwise correlations imply strongly correlated network states in a neural population
- Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
- Stimulus-dependent maximum entropy models of neural population codes
- Correlations and functional connections in a population of grid cells
- A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements
- Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
- Predicting how and when hidden neurons skew measured synaptic interactions
- A tractable method for describing complex couplings between neurons and population rate
- Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
- Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
- Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships