Longnian Lin

Organizing principles of real-time memory encoding: neural clique assemblies and universal neural codes

Recent identification of network-level coding units, termed neural cliques, in the hippocampus has enabled real-time patterns of memory traces to be mathematically described, directly visualized, and dynamically deciphered. These memory coding units are functionally organized in a categorical and hierarchical manner, suggesting that internal representations of external events in the brain is achieved not by recording exact details of those events, but rather by recreating its own selective pictures based on cognitive importance. This neural-clique-based hierarchical-extraction and parallel-binding process enables the brain to acquire not only large storage capacity but also abstraction and generalization capability. In addition, activation patterns of the neural clique assemblies can be converted to strings of binary codes that would permit universal categorizations of internal brain representations across individuals and species.

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

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.

Large-scale neural ensemble recording in the brains of freely behaving mice.

With the availability of sophisticated genetic techniques, the mouse is a valuable mammalian model to study the molecular and cellular basis of cognitive behaviors. However, the small size of mice makes it difficult for a systematic investigation of activity patterns of neural networks in vivo. Here we report the development and construction of a high-density ensemble recording array with up to 128-recording channels that can be formatted as single electrodes, stereotrodes, or tetrodes. This high-density recording array is capable of recording from hundreds of individual neurons simultaneously in the hippocampus of the freely behaving mice. This large-scale in vivo ensemble recording techniques, once coupled with mouse genetics, should be valuable to the study of complex relationship between the genes, neural network, and cognitive behaviors.

Neural encoding of the concept of nest in the mouse brain

As important as memory is to our daily functions, the ability to extract fundamental features and commonalities from various episodic experiences and to then generalize them into abstract concepts is even more crucial for both humans and animals to adapt to novel and complex situations. Here, we report the neural correlates of the abstract concept of nests or beds in mice. Specifically, we find hippocampal neurons that selectively fire or cease to fire when the mouse perceives nests or beds, regardless of their locations and environments. Parametric analyses show that responses of nest cells remain invariant over changes in the nests physical shape, style, color, odor, or construction materials; rather, their responses are driven by conscious awareness and physical determination of the categorical features that would functionally define nests. Such functionality-based abstraction and generalization of conceptual knowledge, emerging from episodic experiences, suggests that the hippocampus is an intrinsic part of the hierarchical structure for generating concepts and knowledge in the brain.

Neurons in the Amygdala with Response-Selectivity for Anxiety in Two Ethologically Based Tests

The amygdala is a key area in the brain for detecting potential threats or dangers, and further mediating anxiety. However, the neuronal mechanisms of anxiety in the amygdala have not been well characterized. Here we report that in freely-behaving mice, a group of neurons in the basolateral amygdala (BLA) fires tonically under anxiety conditions in both open-field and elevated plus-maze tests. The firing patterns of these neurons displayed a characteristic slow onset and progressively increased firing rates. Specifically, these firing patterns were correlated to a gradual development of anxiety-like behaviors in the open-field test. Moreover, these neurons could be activated by any impoverished environment similar to an open-field; and introduction of both comfortable and uncomfortable stimuli temporarily suppressed the activity of these BLA neurons. Importantly, the excitability of these BLA neurons correlated well with levels of anxiety. These results demonstrate that this type of BLA neuron is likely to represent anxiety and/or emotional values of anxiety elicited by anxiogenic environmental stressors.

History of winning remodels thalamo-PFC circuit to reinforce social dominance

The brain circuits of a winner Social dominance in mice depends on their history of winning in social contests. Zhou et al. found that this effect is mediated by neuronal projections from the thalamus to a brain region called the dorsomedial prefrontal cortex. Selective manipulation of synapses driven by this input revealed a causal relationship between circuit activity and mental effort–based dominance behavior. Thus, synapses in this pathway store the memory of previous winning or losing history. Science, this issue p. 162 Synaptic plasticity of a thalamo–prefrontal cortex circuit underlies the winner effect and transfer of social dominance in mice. Mental strength and history of winning play an important role in the determination of social dominance. However, the neural circuits mediating these intrinsic and extrinsic factors have remained unclear. Working in mice, we identified a dorsomedial prefrontal cortex (dmPFC) neural population showing “effort”-related firing during moment-to-moment competition in the dominance tube test. Activation or inhibition of the dmPFC induces instant winning or losing, respectively. In vivo optogenetic-based long-term potentiation and depression experiments establish that the mediodorsal thalamic input to the dmPFC mediates long-lasting changes in the social dominance status that are affected by history of winning. The same neural circuit also underlies transfer of dominance between different social contests. These results provide a framework for understanding the circuit basis of adaptive and pathological social behaviors.

A distinct entorhinal cortex to hippocampal CA1 direct circuit for olfactory associative learning

Lateral and medial parts of entorhinal cortex (EC) convey nonspatial what and spatial where information, respectively, into hippocampal CA1, via both the indirect EC layer 2→ hippocampal dentate gyrus→CA3→CA1 and the direct EC layer 3→CA1 paths. However, it remains elusive how the direct path transfers distinct information and contributes to hippocampal learning functions. Here we report that lateral EC projection neurons selectively form direct excitatory synapses onto a subpopulation of morphologically complex, calbindin-expressing pyramidal cells (PCs) in the dorsal CA1 (dCA1), while medial EC neurons uniformly innervate all dCA1 PCs. Optogenetically inactivating the distinct lateral EC–dCA1 connections or the postsynaptic dCA1 calbindin-expressing PC activity slows olfactory associative learning. Moreover, optetrode recordings reveal that dCA1 calbindin-expressing PCs develop more selective spiking responses to odor cues during learning. Thus, our results identify a direct lateral EC→dCA1 circuit that is required for olfactory associative learning.

Hippocampal theta-driving cells revealed by Granger causality

The two‐dipole model of theta generation in hippocampal CA1 suggests that the inhibitory perisomatic theta dipole is generated by local GABAergic interneurons. Various CA1 interneurons fire preferentially at different theta phases, raising the question of how these theta‐locked interneurons contribute to the generation of theta oscillations. We here recorded interneurons in the hippocampal CA1 area of freely behaving mice, and identified a unique subset of theta‐locked interneurons by using the Granger causality approach. These cells fired in an extremely reliable theta‐burst pattern at high firing rates (∼90 Hz) during exploration and always locked to ascending phases of the theta waves. Among theta‐locked interneurons we recorded, only these cells generated strong Granger causal influences on local field potential (LFP) signals within the theta band (4–12 Hz), and the influences were persistent across behavioral states. Our results suggest that this unique type of theta‐locked interneurons serve as the local inhibitory theta dipole control cells in shaping hippocampal theta oscillations.

Temporal Dynamics of Distinct CA1 Cell Populations during Unconscious State Induced by Ketamine

Ketamine is a widely used dissociative anesthetic which can induce some psychotic-like symptoms and memory deficits in some patients during the post-operative period. To understand its effects on neural population dynamics in the brain, we employed large-scale in vivo ensemble recording techniques to monitor the activity patterns of simultaneously recorded hippocampal CA1 pyramidal cells and various interneurons during several conscious and unconscious states such as awake rest, running, slow wave sleep, and ketamine-induced anesthesia. Our analyses reveal that ketamine induces distinct oscillatory dynamics not only in pyramidal cells but also in at least seven different types of CA1 interneurons including putative basket cells, chandelier cells, bistratified cells, and O-LM cells. These emergent unique oscillatory dynamics may very well reflect the intrinsic temporal relationships within the CA1 circuit. It is conceivable that systematic characterization of network dynamics may eventually lead to better understanding of how ketamine induces unconsciousness and consequently alters the conscious mind.

Prediction of rat behavior outcomes in memory tasks using functional connections among neurons.

Background Analyzing the neuronal organizational structures and studying the changes in the behavior of the organism is key to understanding cognitive functions of the brain. Although some studies have indicated that spatiotemporal firing patterns of neuronal populations have a certain relationship with the behavioral responses, the issues of whether there are any relationships between the functional networks comprised of these cortical neurons and behavioral tasks and whether it is possible to take advantage of these networks to predict correct and incorrect outcomes of single trials of animals are still unresolved. Methodology/Principal Findings This paper presents a new method of analyzing the structures of whole-recorded neuronal functional networks (WNFNs) and local neuronal circuit groups (LNCGs). The activity of these neurons was recorded in several rats. The rats performed two different behavioral tasks, the Y-maze task and the U-maze task. Using the results of the assessment of the WNFNs and LNCGs, this paper describes a realization procedure for predicting the behavioral outcomes of single trials. The methodology consists of four main parts: construction of WNFNs from recorded neuronal spike trains, partitioning the WNFNs into the optimal LNCGs using social community analysis, unsupervised clustering of all trials from each dataset into two different clusters, and predicting the behavioral outcomes of single trials. The results show that WNFNs and LNCGs correlate with the behavior of the animal. The U-maze datasets show higher accuracy for unsupervised clustering results than those from the Y-maze task, and these datasets can be used to predict behavioral responses effectively. Conclusions/Significance The results of the present study suggest that a methodology proposed in this paper is suitable for analysis of the characteristics of neuronal functional networks and the prediction of rat behavior. These types of structures in cortical ensemble activity may be critical to information representation during the execution of behavior.