Synaptic propagation in neuronal networks with finite-support space-dependent coupling.

Traveling waves of electrical activity are ubiquitous in biological neuronal networks. Traveling waves in the brain are associated with sensory processing, phase coding, and sleep. The neuron and network parameters that determine traveling waves' evolution are the synaptic space constant, synaptic conductance, membrane time constant, and synaptic decay time constant. We used an abstract neuron model in a one-dimensional network to investigate the propagation characteristics of traveling wave activity. We formulate a set of evolution equations based on the network connectivity parameters. Using a combination of numerical and analytical approaches, we show that these traveling waves are stable to a series of perturbations with biological relevance.

Efficacy based ginger fingerprinting reveals potential antiproliferative analytes for triple negative breast cancer.

Ginger (Zingiber officinale) is one of the most widely consumed dietary supplements worldwide. Its anticancer potential has been demonstrated in various studies. However, ginger roots obtained from different geographical locations showed extensive variability in their activities, mainly due to differences in the levels of bioactive compounds. Here we evaluated the effect of these differences on the anticancer activity of ginger by performing efficacy-based fingerprinting. We characterized the fingerprint profiles of 22 ginger samples using liquid chromatography-mass spectroscopy, followed by a principal component analysis (PCA) and pearson correlation analysis. We also evaluated the anti-proliferative effects (IC50) of these samples on triple-negative breast cancer cells using the MTT assays. The supervised PCA identified a subset of analytes whose abundance strongly associated with the IC50 values of the ginger extracts, providing a link between ginger extract composition and in vitro anticancer efficacy. This study demonstrated that variation in the ginger fingerprint profiles resulting from differences in their chemical composition could have a significant impact on efficacy and bioactivity of ginger extracts. Also, this first-of-a-kind efficacy-based fingerprinting approach proposed here can identify potent anticancer candidates from the ginger fingerprint without the need for isolating individual components from the extracts.

Hypoxia-Induced Centrosome Amplification Underlies Aggressive Disease Course in HPV-Negative Oropharyngeal Squamous Cell Carcinomas.

Human papillomavirus-negative (HPV-neg) oropharyngeal squamous cell carcinomas (OPSCCs) are associated with poorer overall survival (OS) compared with HPV-positive (HPV-pos) OPSCCs. The major obstacle in improving outcomes of HPV-neg patients is the lack of robust biomarkers and therapeutic targets. Herein, we investigated the role of centrosome amplification (CA) as a prognostic biomarker in HPV-neg OPSCCs. A quantitative evaluation of CA in clinical specimens of OPSCC revealed that (a) HPV-neg OPSCCs exhibit higher CA compared with HPV-pos OPSCCs, and (b) CA was associated with poor OS, even after adjusting for potentially confounding clinicopathologic variables. Contrastingly, CA was higher in HPV-pos cultured cell lines compared to HPV-neg ones. This divergence in CA phenotypes between clinical specimens and cultured cells can therefore be attributed to an inaccurate recapitulation of the in vivo tumor microenvironment in the cultured cell lines, namely a hypoxic environment. The exposure of HPV-neg OPSCC cultured cells to hypoxia or stabilizing HIF-1α genetically increased CA. Both the 26-gene hypoxia signature as well as the overexpression of HIF-1α positively correlated with increased CA in HPV-neg OPSCCs. In addition, we showed that HIF-1α upregulation is associated with the downregulation of miR-34a, increase in CA and expression of cyclin- D1. Our findings demonstrate that the evaluation of CA may aid in therapeutic decision-making, and CA can serve as a promising therapeutic target for HPV-neg OPSCC patients.

A Quantitative Centrosomal Amplification Score Predicts Local Recurrence of Ductal Carcinoma In Situ.

PURPOSE: The purpose of this study is to predict risk of local recurrence (LR) in ductal carcinoma in situ (DCIS) with a new visualization and quantification approach using centrosome amplification (CA), a cancer cell-specific trait widely associated with aggressiveness. EXPERIMENTAL DESIGN: This first-of-its-kind methodology evaluates the severity and frequency of numerical and structural CA present within DCIS and assigns a quantitative centrosomal amplification score (CAS) to each sample. Analyses were performed in a discovery cohort (DC, n = 133) and a validation cohort (VC, n = 119). RESULTS: DCIS cases with LR exhibited significantly higher CAS than recurrence-free cases. Higher CAS was associated with a greater risk of developing LR (HR, 6.3 and 4.8 for DC and VC, respectively; P < 0.001). CAS remained an independent predictor of relapse-free survival (HR, 7.4 and 4.5 for DC and VC, respectively; P < 0.001) even after accounting for potentially confounding factors [grade, age, comedo necrosis, and radiotherapy (RT)]. Patient stratification using CAS (P < 0.0001) was superior to that by Van Nuys Prognostic Index (VNPI; HR for CAS = 6.2 vs. HR for VNPI = 1.1). Among patients treated with breast-conserving surgery alone, CAS identified patients likely to benefit from adjuvant RT. CONCLUSIONS: CAS predicted 10-year LR risk for patients who underwent surgical management alone and identified patients who may be at low risk of recurrence, and for whom adjuvant RT may not be required. CAS demonstrated the highest concordance among the known prognostic models such as VNPI and clinicopathologic variables such as grade, age, and comedo necrosis.

Machine learning-based prediction of breast cancer growth rate in vivo.

BACKGROUND: Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen. METHODS: A serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort. RESULTS: SM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours. CONCLUSION: Our Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications.

Effects of gut-derived endotoxin on anxiety-like and repetitive behaviors in male and female mice.

BACKGROUND: Gut dysbiosis is observed in several neuropsychiatric disorders exhibiting increases in anxiety behavior, and recent work suggests links between gut inflammation and such disorders. One source of this inflammation may be lipopolysaccharide (LPS), a toxic component of gram-negative bacteria. Here, we (1) determine whether oral gavage of LPS, as a model of gut-derived endotoxemia, affects anxiety-like and/or repetitive behaviors; (2) test whether these changes depend on TLR4 signaling; and (3) test the extent to which gut-derived endotoxin and TLR4 antagonism affects males and females differently. METHODS: In experiment 1, male wild-type (WT) and Tlr4-/- mice were tested for locomotor, anxiety-like, and repetitive behaviors in an automated open field test apparatus, 2 h after oral gavage of LPS or saline. In experiment 2, male and female WT mice received an oral gavage of LPS and an injection of one or two TLR4 antagonists that target different TLR4 signaling pathways ((+)-naloxone and LPS derived from R. sphaeroides (LPS-RS)). Univariate and multivariate analyses were used to identify effects of treatment, sex, and genotype and their interaction. RESULTS: In experiment 1, oral gavage of LPS increased anxiety-like behavior in male WT mice but not in Tlr4-/- mice. In experiment 2, oral gavage of LPS increased anxiety-like and decreased repetitive behaviors in WT mice of both sexes. Neither antagonist directly blocked the effects of orally administered LPS. However, treatment with (+)-naloxone, which blocks the TRIF pathway of TLR4, had opposing behavioral effects in males and females (independent of LPS treatment). We also identified sex differences in the expression of interleukin-6, a pro-inflammatory cytokine, in the gut both in basal conditions and in response to LPS. CONCLUSION: In spite of the ubiquitous nature of LPS in the gut lumen, this is the first study to demonstrate that intestinally derived LPS can initiate behavioral aspects of the sickness response. While an increased enteric load of LPS increases anxiety-like behavior in both sexes, it likely does so via sex-specific mechanisms. Similarly, TLR4 signaling may promote baseline expression of repetitive behavior differently in males and females. This study lays the groundwork for future interrogations into connections between gut-derived endotoxin and behavioral pathology in males and females.

Multi-institutional study of nuclear KIFC1 as a biomarker of poor prognosis in African American women with triple-negative breast cancer.

Nuclear KIFC1 (nKIFC1) predicts worse outcomes in breast cancer, but its prognostic value within racially distinct triple-negative breast cancer (TNBC) patients is unknown. Thus, nKIFC1 expression was assessed by immunohistochemistry in 163 African American (AA) and 144 White TNBC tissue microarrays (TMAs) pooled from four hospitals. nKIFC1 correlated significantly with Ki67 in White TNBCs but not in AA TNBCs, suggesting that nKIFC1 is not merely a surrogate for proliferation in AA TNBCs. High nKIFC1 weighted index (WI) was associated with significantly worse overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) (Hazard Ratios [HRs] = 3.5, 3.1, and 3.8, respectively; P = 0.01, 0.009, and 0.007, respectively) in multivariable Cox models in AA TNBCs but not White TNBCs. Furthermore, KIFC1 knockdown more severely impaired migration in AA TNBC cells than White TNBC cells. Collectively, these data suggest that nKIFC1 WI an independent biomarker of poor prognosis in AA TNBC patients, potentially due to the necessity of KIFC1 for migration in AA TNBC cells.

Distinctions in Breast Tumor Recurrence Patterns Post-Therapy among Racially Distinct Populations.

BACKGROUND: Clinical studies have revealed a higher risk of breast tumor recurrence in African-American (AA) patients compared to European-American (EA) patients, contributing to the alarming inequality in clinical outcomes among the ethnic groups. However, distinctions in recurrence patterns upon receiving hormone, radiation, and/or chemotherapy between the races remain poorly characterized. METHODS: We compared patterns and rates (per 1000 cancer patients per 1 year) of recurrence following each form of treatment between AA (n = 1850) and EA breast cancer patients (n = 7931) from a cohort of patients (n = 10504) treated between 2005-2015 at Northside Hospital in Atlanta, GA. RESULTS: Among patients who received any combination of adjuvant therapy, AA displayed higher overall rates of recurrence than EA (p = 0.015; HR: 1.699; CI: 1.108-2.606). Furthermore, recurrence rates were higher in AA than EA among stage I (p = 0.031; HR: 1.736; CI: 1.052-2.864) and T1 classified patients (p = 0.003; HR: 2.009; CI: 1.263-3.197). Interestingly, among patients who received neoadjuvant chemotherapy, AA displayed higher rates of local recurrence than EA (p = 0.024; HR: 7.134; CI: 1.295-39.313). CONCLUSION: Our analysis revealed higher incidence rates of recurrence in AA compared to EA among patients that received any combination of adjuvant therapy. Moreover, our data demonstrates an increased risk of tumor recurrence in AA than EA among patients diagnosed with minimally invasive disease. This is the first clinical study to suggest that neoadjuvant chemotherapy improves breast cancer recurrence rates and patterns in AA.

Analytically tractable studies of traveling waves of activity in integrate-and-fire neural networks.

In contrast to other large-scale network models for propagation of electrical activity in neural tissue that have no analytical solutions for their dynamics, we show that for a specific class of integrate and fire neural networks the acceleration depends quadratically on the instantaneous speed of the activity propagation. We use this property to analytically compute the network spike dynamics and to highlight the emergence of a natural time scale for the evolution of the traveling waves. These results allow us to examine other applications of this model such as the effect that a nonconductive gap of tissue has on further activity propagation. Furthermore we show that activity propagation also depends on local conditions for other more general connectivity functions, by converting the evolution equations for network dynamics into a low-dimensional system of ordinary differential equations. This approach greatly enhances our intuition into the mechanisms of the traveling waves evolution and significantly reduces the simulation time for this class of models.

Rampant centrosome amplification underlies more aggressive disease course of triple negative breast cancers.

Centrosome amplification (CA), a cell-biological trait, characterizes pre-neoplastic and pre-invasive lesions and is associated with tumor aggressiveness. Recent studies suggest that CA leads to malignant transformation and promotes invasion in mammary epithelial cells. Triple negative breast cancer (TNBC), a histologically-aggressive subtype shows high recurrence, metastases, and mortality rates. Since TNBC and non-TNBC follow variable kinetics of metastatic progression, they constitute a novel test bed to explore if severity and nature of CA can distinguish them apart. We quantitatively assessed structural and numerical centrosomal aberrations for each patient sample in a large-cohort of grade-matched TNBC (n = 30) and non-TNBC (n = 98) cases employing multi-color confocal imaging. Our data establish differences in incidence and severity of CA between TNBC and non-TNBC cell lines and clinical specimens. We found strong correlation between CA and aggressiveness markers associated with metastasis in 20 pairs of grade-matched TNBC and non-TNBC specimens (p < 0.02). Time-lapse imaging of MDA-MB-231 cells harboring amplified centrosomes demonstrated enhanced migratory ability. Our study bridges a vital knowledge gap by pinpointing that CA underlies breast cancer aggressiveness. This previously unrecognized organellar inequality at the centrosome level may allow early-risk prediction and explain higher tumor aggressiveness and mortality rates in TNBC patients.

KIFCI, a novel putative prognostic biomarker for ovarian adenocarcinomas: delineating protein interaction networks and signaling circuitries.

BACKGROUND: Amplified centrosomes in cancers are recently garnering a lot of attention as an emerging hub of diagnostic, prognostic and therapeutic targets. Ovarian adenocarcinomas commonly harbor supernumerary centrosomes that drive chromosomal instability. A centrosome clustering molecule, KIFC1, is indispensable for the viability of extra centrosome-bearing cancer cells, and may underlie progression of ovarian cancers. METHODS: Centrosome amplification in low- and high- grade serous ovarian adenocarcinomas was quantitated employing confocal imaging. KIFC1 expression was analyzed in ovarian tumors using publically-available databases. Associated grade, stage and clinical information from these databases were plotted for KIFC1 gene expression values. Furthermore, interactions and functional annotation of KIFC1 and its highly correlated genes were studied using DAVID and STRING 9.1. RESULTS: Clinical specimens of ovarian cancers display robust centrosome amplification and deploy centrosome clustering to execute an error-prone mitosis to enable karyotypic heterogeneity that fosters tumor progression and aggressiveness. Our in silico analyses showed KIFC1 overexpression in human ovarian tumors (n = 1090) and its upregulation associated with tumor aggressiveness utilizing publically-available gene expression databases. KIFC1 expression correlated with advanced tumor grade and stage. Dichotomization of KIFC1 levels revealed a significantly lower overall survival time for patients in high KIFC1 group. Intriguingly, in a matched-cohort of primary (n = 7) and metastatic (n = 7) ovarian samples, no significant differences in KIFC1 expression were detectable, suggesting that high KIFC1 expression may serve as a marker of metastases onset. Nonetheless, KIFC1 levels in both primary and matched metastatic sites were significantly higher compared to normal tissue . Ingenuity based network prediction algorithms combined with pre-established protein interaction networks uncovered several novel cell-cycle related partner genes on the basis of interconnectivity, illuminating the centrosome clustering independent agenda of KIFC1 in ovarian tumor progression. CONCLUSIONS: Ovarian cancers display amplified centrosomes, a feature of aggressive tumors. To cope up with the abnormal centrosomal load, ovarian cancer cells upregulate genes like KIFC1 that are known to induce centrosome clustering. Our data underscore KIFC1 as a putative biomarker that predicts worse prognosis, poor overall survival and may serve as a potential marker of onset of metastatic dissemination in ovarian cancer patients.

On brain activity mapping: insights and lessons from Brain Decoding Project to map memory patterns in the hippocampus.

The BRAIN project recently announced by the president Obama is the reflection of unrelenting human quest for cracking the brain code, the patterns of neuronal activity that define who we are and what we are. While the Brain Activity Mapping proposal has rightly emphasized on the need to develop new technologies for measuring every spike from every neuron, it might be helpful to consider both the theoretical and experimental aspects that would accelerate our search for the organizing principles of the brain code. Here we share several insights and lessons from the similar proposal, namely, Brain Decoding Project that we initiated since 2007. We provide a specific example in our initial mapping of real-time memory traces from one part of the memory circuit, namely, the CA1 region of the mouse hippocampus. We show how innovative behavioral tasks and appropriate mathematical analyses of large datasets can play equally, if not more, important roles in uncovering the specific-to-general feature-coding cell assembly mechanism by which episodic memory, semantic knowledge, and imagination are generated and organized. Our own experiences suggest that the bottleneck of the Brain Project is not only at merely developing additional new technologies, but also the lack of efficient avenues to disseminate cutting edge platforms and decoding expertise to neuroscience community. Therefore, we propose that in order to harness unique insights and extensive knowledge from various investigators working in diverse neuroscience subfields, ranging from perception and emotion to memory and social behaviors, the BRAIN project should create a set of International and National Brain Decoding Centers at which cutting-edge recording technologies and expertise on analyzing large datasets analyses can be made readily available to the entire community of neuroscientists who can apply and schedule to perform cutting-edge research.

On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus.

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination.

The interplay between branching and pruning on neuronal target search during developmental growth: functional role and implications.

Regenerative strategies that facilitate the regrowth and reconnection of neurons are some of the most promising methods in spinal cord injury research. An essential part of these strategies is an increased understanding of the mechanisms by which growing neurites seek out and synapse with viable targets. In this paper, we use computational and theoretical tools to examine the targeting efficiency of growing neurites subject to limited resources, such as maximum total neural tree length. We find that in order to efficiently reach a particular target, growing neurites must achieve balance between pruning and branching: rapidly growing neurites that do not prune will exhaust their resources, and frequently pruning neurites will fail to explore space effectively. We also find that the optimal branching/pruning balance must shift as the target distance changes: different strategies are called for to reach nearby vs. distant targets. This suggests the existence of a currently unidentified higher-level regulatory factor to control arborization dynamics. We propose that these findings may be useful in future therapies seeking to improve targeting rates through manipulation of arborization behaviors.

A mismatch-based model for memory reconsolidation and extinction in attractor networks.

The processes of memory reconsolidation and extinction have received increasing attention in recent experimental research, as their potential clinical applications begin to be uncovered. A number of studies suggest that amnestic drugs injected after reexposure to a learning context can disrupt either of the two processes, depending on the behavioral protocol employed. Hypothesizing that reconsolidation represents updating of a memory trace in the hippocampus, while extinction represents formation of a new trace, we have built a neural network model in which either simple retrieval, reconsolidation or extinction of a stored attractor can occur upon contextual reexposure, depending on the similarity between the representations of the original learning and reexposure sessions. This is achieved by assuming that independent mechanisms mediate Hebbian-like synaptic strengthening and mismatch-driven labilization of synaptic changes, with protein synthesis inhibition preferentially affecting the former. Our framework provides a unified mechanistic explanation for experimental data showing (a) the effect of reexposure duration on the occurrence of reconsolidation or extinction and (b) the requirement of memory updating during reexposure to drive reconsolidation.

Differential consolidation and pattern reverberations within episodic cell assemblies in the mouse hippocampus.

One hallmark feature of consolidation of episodic memory is that only a fraction of original information, which is usually in a more abstract form, is selected for long-term memory storage. How does the brain perform these differential memory consolidations? To investigate the neural network mechanism that governs this selective consolidation process, we use a set of distinct fearful events to study if and how hippocampal CA1 cells engage in selective memory encoding and consolidation. We show that these distinct episodes activate a unique assembly of CA1 episodic cells, or neural cliques, whose response-selectivity ranges from general-to-specific features. A series of parametric analyses further reveal that post-learning CA1 episodic pattern replays or reverberations are mostly mediated by cells exhibiting event intensity-invariant responses, not by the intensity-sensitive cells. More importantly, reactivation cross-correlations displayed by intensity-invariant cells encoding general episodic features during immediate post-learning period tend to be stronger than those displayed by invariant cells encoding specific features. These differential reactivations within the CA1 episodic cell populations can thus provide the hippocampus with a selection mechanism to consolidate preferentially more generalized knowledge for long-term memory storage.

A synaptic reinforcement-based model for transient amnesia following disruptions of memory consolidation and reconsolidation.

The observation of memory recovery following post-training amnestic interventions has historically caused controversy over the meaning of this finding, leading some authors to question the paradigm of a consolidation period for memories. Similarly, recent demonstrations of transient amnesia caused by interventions following memory reactivation have been used to question the existence of a retrieval-driven reconsolidation process. The present work aims to approach the phenomenon of transient amnesia following disruptions of consolidation and reconsolidation, discussing how memory recovery might be explained within a framework of systems consolidation, persistent synaptic reinforcement, and multiple memory traces. With these concepts in mind, we propose that long-term consolidation processes can underlie recovery from amnesia, demonstrating the feasibility of such a hypothesis in a two-structure computational model of learning in which consolidation is dependent upon synaptic reentry reinforcement. On the basis of this, we suggest that prolonged consolidation can account for experimental findings of transient amnesia, in a way that explains differences between disruptions of consolidation and reconsolidation without the need to dwell into the discussion between storage- and retrieval-based explanations for memory impairment.

Subspace projection approaches to classification and visualization of neural network-level encoding patterns.

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD.

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.