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A multivariate extension of mutual information for growing neural networks.
Citation:
Ball, K., C. Grant, W. Mundy, AND Tim Shafer. A multivariate extension of mutual information for growing neural networks. Neural Networks. Elsevier B.V., Amsterdam, Netherlands, 95:29-43, (2017). https://doi.org/10.1016/j.neunet.2017.07.009
Impact/Purpose:
This manuscript reports on a novel analysis of spontaneous activity in neural networks, called normalized mutual information (NMI), that assesses the information contained in the network. The results demonstrate that NMI is a sensitive measure for compound effects on neural network function, and is more sensitive that similar measures such as correlation co-efficient between electrodes. As such, it improves our ability to detect compound effects in developing neural networks.
Description:
Recordings of neural network activity in vitro are increasingly being used to assess the development of neural network activity and the effects of drugs, chemicals and disease states on neural network function. The high-content nature of the data derived from such recordings can be used to infer effects of compounds or disease states on a variety of important neural functions, including network synchrony. Historically, synchrony of networks in vitro has been assessed either by determination of correlation coefficients (e.g. Pearson's correlation), by statistics estimated from cross-correlation histograms between pairs of active electrodes, and/or by pairwise mutual information and related measures. The present study examines the application of Normalized Multiinformation (NMI) as a scalar measure of shared information content in a multivariate network that is robust with respect to changes in network size. Theoretical simulations are designed to investigate NMI as a measure of complexity and synchrony in a developing network relative to several alternative approaches. The NMI approach is applied to these simulations and also to data collected during exposure of in vitro neural networks to neuroactive compounds during the first 12 days in vitro, and compared to other common measures, including correlation coefficients and mean firing rates of neurons. NMI is shown to be more sensitive to developmental effects than first order synchronous and nonsynchronous measures of network complexity. Finally, NMI is a scalar measure of global (rather than pairwise) mutual information in a multivariate network, and hence relies on less assumptions for cross-network comparisons than historical approaches.
