The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility.
Keywords: neural ensemble data, spikes, local field potential, data analysis, KL-divergence
Citation: Long II JD and Carmena JM (2011) A statistical description of neural ensemble dynamics. Front. Comput. Neurosci. 5:52. doi: 10.3389/fncom.2011.00052
Received: 01 February 2011;
Accepted: 01 November 2011;
Published online: 28 November 2011.
Edited by:Nicolas Brunel, Centre National de la Recherche Scientifique, France
Reviewed by:Maneesh Sahani, University College London, UK
Copyright: © 2011 Long II and Carmena. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
*Correspondence: John D. Long II, Helen Wills Neuroscience Institute, University of California Berkeley, 754 Sutardja Dai Hall, MC #1764, Berkeley, CA 94720, USA. e-mail: email@example.com