Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus

Abstract

To examine the network-level organizing principles by which the brain achieves its real-time encoding of episodic information, we have developed a 96-channel array to simultaneously record the activity patterns of as many as 260 individual neurons in the mouse hippocampus during various startling episodes. We find that the mnemonic startling episodes triggered firing changes in a set of CA1 neurons in both startle-type and environment-dependent manners. Pattern classification methods reveal that these firing changes form distinct ensemble representations in a low-dimensional encoding subspace. Application of a sliding window technique further enabled us to reliably capture not only the temporal dynamics of real-time network encoding but also postevent processing of newly formed ensemble traces. Our analyses revealed that the network-encoding power is derived from a set of functional coding units, termed neural cliques, in the CA1 network. The individual neurons within neural cliques exhibit ''collective cospiking'' dynamics that allow the neural clique to overcome the response variability of its members and to achieve real-time encoding robustness. Conversion of activation patterns of these coding unit assemblies into a set of real-time digital codes permits concise and universal representation and categorization of discrete behavioral episodes across different individual brains.episodic memory ͉ neural clique ͉ neural code ͉ startle ͉ cell assembly

On the Natural Hierarchical Composition of Cliques in Cell Assemblies

“…Direct evidence has recently been provided for cliquecodes in multiple-unit electrode recordings from rat hippocampus [21,32,33]. These experiments confirmed earlier indirect evidence for assemblies [19,36].…”

“…These experiments confirmed earlier indirect evidence for assemblies [19,36]. Lin et al [32,33], for instance, studied startle-triggered ensemble responses in large sets of 260 simultaneously recorded CA1 neurons of rats in response to different experiences like 'freefall inside an elevator', 'sudden gush of air to the back', or 'earthquake-like shake'. They found that the stimuli evoked diverse changes in some hippocampal neurons but could be projected by means of a multiple discriminant analysis to distinct patterns in a low-dimensional coding space.…”

“…Lin et al [33], based on multiple-electrode recordings in behaving rats [21,32], argued for categorical representations of stimulus conditions related to behavioural responses in hippocampal neurons which comprise nested sets of neurons that get smaller the more specific the stimulus conditions are. These results to some degree parallel the expectations from concept lattices but consider only the mapping of features to one output aspect, the reflexive response.…”

Neural encoding of the concept of nest in the mouse brain

“…Indeed, our recent large-scale recording suggests that some of the memory-coding units in the hippocampus seem to be intrinsically involved in extracting commonality and abstract general features from various episodic events (17,18). Therefore, we set out to investigate the neural encoding of conceptual knowledge of nests or beds in the mouse hippocampus and studied the correlation between the neural activity and behavioral encounters of various types of nests.…”

Causal entropies—A measure for determining changes in the temporal organization of neural systems

“…Others operate on the entire space of interaction. These include dimensional reduction methods, such as principle component analysis and multiple discriminant analysis (Lin et al, 2005), optimal linear estimation (Salinas and Abbot, 1994), and Bayes methods (Zhang et al, 1998). Another class of methods stems from bivariate autoregressive calculations.…”