Causal pattern recovery from neural spike train data using the Snap Shot Score

Abstract

We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.

A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

“…In recent years, there has been considerable interest in learning the structure of gene regulatory networks from transcriptomic time series with dynamic Bayesian networks (DBNs) (Ong et al (2002); Husmeier (2003); Smith et al (2002)), which is motivated by related applications to the inference of neural networks from electrochemical spike train data ; Echtermeyer et al (2009)) and follows up on the successful reconstruction of protein signalling pathways with static Bayesian networks (Sachs et al (2005)). …”

Identification of Functionally Interconnected Neurons Using Factor Analysis

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