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

Authors:
Christoph Echtermeyer;Tom V. Smulders;V. Anne Smith
Affiliations:
School of Biology, University of St Andrews, St Andrews, UK KY16 9TS;Institute of Neuroscience, The Henry Wellcome Building for Neuroecology, Newcastle University, Newcastle upon Tyne, UK NE2 4HH;School of Biology, University of St Andrews, St Andrews, UK KY16 9TS
Venue:
Journal of Computational Neuroscience
Year:
2010

Citing 15
Cited 0

The C programming language

The C programming language
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Spikes: exploring the neural code

Spikes: exploring the neural code
Causality: models, reasoning, and inference

Causality: models, reasoning, and inference
Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Learning Belief Networks in the Presence of Missing Values and Hidden Variables

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Dynamic Bayesian Networks

Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Dynamic bayesian networks: representation, inference and learning

Dynamic bayesian networks: representation, inference and learning
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data

Bioinformatics
Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

Neural Computation
On the Evaluation of Information Flow in Multivariate Systems by the Directed Transfer Function

Biological Cybernetics
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Evolutionary Computation for Modeling and Optimization

Evolutionary Computation for Modeling and Optimization

Quantified Score

Hi-index	0.00

Visualization

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.