Paper Info
Title
Scalable inference of neural dynamical systems.
Abstract
Fluorescent calcium imaging provides a potentially powerful tool for inferring connectivity in large neural circuits. However, a key challenge in using calcium imaging for connectivity detection is that current systems often have a temporal response and frame rates that can be orders of magnitude slower than the underlying neural spiking process. Bayesian inference methods based on expectation-maximization (EM) have been proposed to overcome these limitations, but are often computationally demanding since the E-step in the EM procedure typically involves state estimation for a high-dimensional nonlinear dynamical system. In this work, we propose a computationally scalable method based on a hybrid of loopy belief propagation and approximate message passing (AMP). The key insight is that a neural system as viewed through calcium imaging can be factorized into simple scalar dynamical systems for each neuron with linear interconnections between the neurons. Using the structure, the updates in the proposed hybrid AMP methodology can be computed by a set of one-dimensional state estimation procedures and linear transforms with the connectivity matrix. The method extends earlier works by incorporating more general nonlinear dynamics and responses to stimuli.
Year
Venue
Field
2015
2015 53RD ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON)
Mathematical optimization,Nonlinear system,Bayesian inference,Computer science,Inference,Theoretical computer science,Dynamical systems theory,Biological neural network,Message passing,Scalability,Belief propagation
DocType
ISSN
Citations 
Conference
2474-0195
0
PageRank 
References 
Authors
0.34
4
1
Name
Order
Citations
PageRank
Alyson K. Fletcher155241.10