Measures of mutual and causal dependence between two time series
IEEE Transactions on Information Theory
Estimation of entropy and mutual information
Neural Computation
Prediction, Learning, and Games
Prediction, Learning, and Games
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Capacity of queues via point-process channels
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Distinguishing Causal Interactions in Neural Populations
Neural Computation
Inferring time-varying network topologies from gene expression data
EURASIP Journal on Bioinformatics and Systems Biology
A Model Reference Adaptive Search Method for Global Optimization
Operations Research
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
The capacity of channels with feedback
IEEE Transactions on Information Theory
Finite state channels with time-invariant deterministic feedback
IEEE Transactions on Information Theory
Directed information and causal estimation in continuous time
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
On directed information theory and Granger causality graphs
Journal of Computational Neuroscience
Universal entropy estimation via block sorting
IEEE Transactions on Information Theory
An Algorithm for Universal Lossless Compression With Side Information
IEEE Transactions on Information Theory
Source Coding With Feed-Forward: Rate-Distortion Theorems and Error Exponents for a General Source
IEEE Transactions on Information Theory
Information theory in neuroscience
Journal of Computational Neuroscience
Information transfer in social media
Proceedings of the 21st international conference on World Wide Web
Brief paper: Modeling the topology of a dynamical network via Wiener filtering approach
Automatica (Journal of IFAC)
Neural Computation
Unsupervised and nonparametric detection of information flows
Signal Processing
Journal of Computational Neuroscience
Causal conditioning and instantaneous coupling in causality graphs
Information Sciences: an International Journal
Hi-index | 0.00 |
Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures--most of which are variants of Granger causality--with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.