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This volume looks at the latest advancements made in the study of neuronal ensembles and their role in brain processes. The chapters in this book cover topics such as approaches to implementing multisensory experiments including the basics of head fixation and analysis of laminar electrophysiology; dimensionality reduction algorithms and network analysis applications for neuronal population recordings; the use of graph theory to detect groups of neurons in optical recordings with high levels of coactivity; tagging and manipulation techniques using optogenetic and chemogenetic approaches to study memory engrams; and methods to characterize molecular signatures of neuronal ensembles using spatially defined transcriptomics. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Authoritative and comprehensive, Identification, Characterization, and Manipulation of Neuronal Ensembles is a valuable resource for all researchers who are interested in learning more about neuronal population dynamics. Chapters "4, 5, and 6" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Single-Neuron and Population Methods to Study the Circuit-Level Cortical Mechanisms of Multisensory Processing.- Detection of Cell Assemblies in High-Density Extracellular Electrophysiological Recordings.- A General and Accurate Method for Neuronal Ensemble Detection in Spiking Neural Networks.- Fast Neuronal Segmentation of Two-Photon Functional Imaging Recordings using CITE-On.- Identification of Neuronal Ensembles from Similarity Maps using Singular Value Decomposition.- Detection of Overlapping Neuronal Assemblies from Activity Recordings of Large Neuronal Populations by Means of Non-Orthogonal Low-Dimensional State Spaces.- Detecting Neural Assemblies through Similarity Graph Clustering.- Identifying Neuronal Ensembles: A Graph Theoretical Approach.- Dissecting Neural Population Dynamics: Elastic Net and Conditional Random Fields for Deciphering Functional Networks and Information Processing in Learning and Memory.- Analyzing Functional Neuronal Ensembles in a Between-Subjects Paradigm.- Tagging and Manipulation of Fear Engrams.- Molecular Analysis of Neural Networks: Methods for Identifying and Characterizing Gene Expression Patterns in Neuronal Ensembles.
