Large-Scale Neural Ensembles in Mice: Methods for Recording and Data Analysis

One of the fundamental goals in neuroscience is to uncover, in real-time, the formation and retrieval of the brain’s associative memory traces. Here, we describe methodology we have developed to permit large-scale recording and analysis of neuronal activity from ensembles of neurons. We have constructed a lightweight multi-channel recording microdrive that permits long-term recording from multiple neurons, or several brain regions simultaneously, from freely behaving mice. Our device is capable of acquiring up to 128 channels of neuronal activity data simultaneously from freely moving mice. The recording and decoding of such high-density signals can be combined with the acquisition of behavioral responses of mice in elegant paradigms in order that one might define the firing patterns of multiple neurons and their relationships with behavioral performances as memory traces are formed or recalled. It is well known that startling events are often encoded as episodic memories that are remembered well for years. We have recorded hundreds of individual CA1 units using our high-density recording technique in mice while subjecting them to repetitions of particular startling stimuli. By decoding simultaneously acquired hippocampal network activity our analyses have revealed functional coding units, that we have termed neural cliques. Our data indicate that any episodic event is represented and encoded by the activation of a set of neural clique assemblies that are organized in a categorical and hierarchical manner. The neural clique assemblies’ organization represents a network-level mechanism capable of vast storage capacity, and permits identification of common patterns from individual behavioral episodes and their application to abstract concepts necessary for intelligence and adaptive behaviors. The decoding and deciphering of these real-time ensemble-recording technologies offer great promise for application to multiple brain regions and will significantly impact the development of brain–machine interface technology.