Network Activity Patterns Research Articles

Event-Based Feature Extraction Using Adaptive Selection Thresholds.

Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.

Ontological Dimensions of Cognitive-Neural Mappings.

The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that 'task paradigm' categories explain more variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, 'study ID', or the study from which each activation map was reported, explained close to 50% of the variance in activation patterns. Using a clustering approach that allows for overlapping clusters, we derived data-driven latent activation states, associated with re-occurring configurations of the canonical frontoparietal, salience, sensory-motor, and default mode network activation patterns. Importantly, with only four data-driven latent dimensions, one can explain greater variance among activation maps than all conventional ontological dimensions combined. These latent dimensions may inform a data-driven cognitive ontology, and suggest that current descriptions of cognitive processes and the tasks used to elicit them do not accurately reflect activation patterns commonly observed in the human brain.

Modeling the coupled dynamics of stream metabolism and microbial biomass

AbstractEstimating and interpreting ecosystem metabolism remains an important challenge in stream ecology. Here, we propose a novel approach to model, estimate, and predict multiseasonal patterns of stream metabolism (gross primary production [GPP] and ecosystem respiration [ER]) at the reach scale leveraging on increasingly available long‐term, high‐frequency measurements of dissolved oxygen (DO). The model uses DO measurements to estimate the parameters of a simple ecosystem model describing the underlying dynamics of stream autotrophic and heterotrophic microbial biomass. The model has been applied to four reaches within the Ybbs river network, Austria. Even if microbial biomasses are not observed, that is, they are treated as latent variables, results show that by accounting for the temporal dynamics of biomass, the model reproduces variability in metabolic fluxes that is not explained by fluctuations of light, temperature, and resources. The model is particularly data‐demanding: to estimate the 11 parameters used in this formulation, it requires sufficiently long, for example, annual, time series, and significant scouring events. On the other hand, the approach has the potential to separate ER into its autotrophic and heterotrophic components, estimate a richer set of ecosystem carbon fluxes (i.e., carbon uptake, loss, and scouring), extrapolate metabolism estimates for periods when DO measurements are unavailable, and predict how ecosystem metabolism would respond to variations of the driving forces. The model is seen as a building block to develop tools to fully appreciate multiseasonal patterns of metabolic activity in river networks and to provide reliable estimations of carbon fluxes from land to ocean.

Network and cellular mechanisms underlying heterogeneous excitatory/inhibitory balanced states.

Recent work has explored spatiotemporal relationships between excitatory (E) and inhibitory (I) signaling within neural networks, and the effect of these relationships on network activity patterns. Data from these studies have indicated that excitation and inhibition are maintained at a similar level across long time periods and that excitatory and inhibitory currents may be tightly synchronized. Disruption of this balance-leading to an aberrant E/I ratio-is implicated in various brain pathologies. However, a thorough characterization of the relationship between E and I currents in experimental settings is largely impossible, due to their tight regulation at multiple cellular and network levels. Here, we use biophysical neural network models to investigate the emergence and properties of balanced states by heterogeneous mechanisms. Our results show that a network can homeostatically regulate the E/I ratio through interactions among multiple cellular and network factors, including average firing rates, synaptic weights and average neural depolarization levels in excitatory/inhibitory populations. Complex and competing interactions between firing rates and depolarization levels allow these factors to alternately dominate network dynamics in different synaptic weight regimes. This leads to the emergence of distinct mechanisms responsible for determining a balanced state and its dynamical correlate. Our analysis provides a comprehensive picture of how E/I ratio changes when manipulating specific network properties, and identifies the mechanisms regulating E/I balance. These results provide a framework to explain the diverse, and in some cases, contradictory experimental observations on the E/I state in different brain states and conditions.

Rethinking Behaviors and Activities of Base Stations in Mobile Cellular Networks Based on Big Data Analysis

This paper uses big data technologies to study base stations’ behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, first we exploit big data platform and technologies to analyze cellular network big data, i.e., Call Detail Records (CDRs). Our CDRs data set, which includes more than 1,000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Second, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations’ behaviors and dissect their predictability. Third, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations’ activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations’ behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.

Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy.

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices.

Differential Effects of Open- and Closed-Loop Intracortical Microstimulation on Firing Patterns of Neurons in Distant Cortical Areas.

Intracortical microstimulation can be used successfully to modulate neuronal activity. Activity-dependent stimulation (ADS), in which action potentials recorded extracellularly from a single neuron are used to trigger stimulation at another cortical location (closed-loop), is an effective treatment for behavioral recovery after brain lesion, but the related neurophysiological changes are still not clear. Here, we investigated the ability of ADS and random stimulation (RS) to alter firing patterns of distant cortical locations. We recorded 591 neuronal units from 23 Long-Evan healthy anesthetized rats. Stimulation was delivered to either forelimb or barrel field somatosensory cortex, using either RS or ADS triggered from spikes recorded in the rostral forelimb area (RFA). Both RS and ADS stimulation protocols rapidly altered spike firing within RFA compared with no stimulation. We observed increase in firing rates and change of spike patterns. ADS was more effective than RS in increasing evoked spikes during the stimulation periods, by producing a reliable, progressive increase in stimulus-related activity over time and an increased coupling of the trigger channel with the network. These results are critical for understanding the efficacy of closed-loop electrical microstimulation protocols in altering activity patterns in interconnected brain networks, thus modulating cortical state and functional connectivity.

MTORC1 and mTORC2 Differentially Regulate Cell Fate Programs to Coordinate Osteoblastic Differentiation in Mesenchymal Stromal Cells

Vascular regeneration depends on intact function of progenitors of vascular smooth muscle cells such as pericytes and their circulating counterparts, mesenchymal stromal cells (MSC). Deregulated MSC differentiation and maladaptive cell fate programs associated with age and metabolic diseases may exacerbate arteriosclerosis due to excessive transformation to osteoblast-like calcifying cells. Targeting mTOR, a central controller of differentiation and cell fates, could offer novel therapeutic perspectives. In a cell culture model for osteoblastic differentiation of pluripotent human MSC we found distinct roles for mTORC1 and mTORC2 in the regulation of differentiation towards calcifying osteoblasts via cell fate programs in a temporally-controlled sequence. Activation of mTORC1 with induction of cellular senescence and apoptosis were hallmarks of transition to a calcifying phenotype. Inhibition of mTORC1 with Rapamycin elicited reciprocal activation of mTORC2, enhanced autophagy and recruited anti-apoptotic signals, conferring protection from calcification. Pharmacologic and genetic negative interference with mTORC2 function or autophagy both abolished regenerative programs but induced cellular senescence, apoptosis, and calcification. Overexpression of the mTORC2 constituent rictor revealed that enhanced mTORC2 signaling without altered mTORC1 function was sufficient to inhibit calcification. Studies in mice reproduced the in vitro effects of mTOR modulation with Rapamycin on cell fates in vascular cells in vivo. Amplification of mTORC2 signaling promotes protective cell fates including autophagy to counteract osteoblast differentiation and calcification of MSC, representing a novel mTORC2 function. Regenerative approaches aimed at modulating mTOR network activation patterns hold promise for delaying age-related vascular diseases and treatment of accelerated arteriosclerosis in chronic metabolic conditions.

Simultaneous electrophysiological and morphological assessment of functional damage to neural networks in vitro after 30\u2013300\u2009g impacts

An enigma of mild traumatic brain injury are observations of substantial behavior and performance deficits in the absence of bleeding or other observable structural damage. Altered behavior and performance reflect changes in action potential (AP) patterns within neuronal networks, which could result from subtle subcellular responses that affect synaptic efficacy and AP production. The aim of this study was to investigate and quantify network activity changes after simulated concussions in vitro and therewith develop a platform for simultaneous and direct observations of morphological and electrophysiological changes in neural networks. We used spontaneously active networks grown on microelectrode arrays (MEAs) to allow long-term multisite monitoring with simultaneous optical observations before and after impacts delivered by a ballistic pendulum (30 to 300 g accelerations). The monitoring of AP waveshape templates for long periods before and after impact provided an internal control for cell death or loss of cell-electrode coupling in the observed set of neurons. Network activity patterns were linked in real-time to high power phase contrast microscopy. There was no overt loss of glial or neuronal adhesion, even at high-g impacts. All recording experiments showed repeatable spike production responses: a loss of activity with recovery to near reference in 1 hr, followed by a slow activity decay to a stable, level plateau approximately 30–40% below reference. The initial recovery occurred in two steps: a rapid return of activity to an average 24% below reference, forming a level plateau lasting from 5 to 20 min, followed by a climb to within 10% of reference where a second plateau was established for 1 to 2 hrs. Cross correlation profiles revealed changes in firing hierarchy as well as in Phase 1 in spontaneous network oscillations that were reduced by as much as 20% 6–8 min post impact with only a partial recovery at 30 min. We also observed that normally stable nuclei developed irregular rotational motion after impact in 27 out of 30 networks. The evolution of network activity deficits and recovery can be linked with microscopically observable changes in the very cells that are generating the activity. The repeatable electrophysiological impact response profiles and oscillation changes can provide a quantitative basis for systematic evaluations of pharmacological intervention strategies. Future expansion to include fluorescent microscopy should allow detailed investigations of damage mechanisms on the subcellular level.

Spontaneous Activity Patterns Are Altered in the Developing Visual Cortex of the Fmr1 Knockout Mouse.

Fragile X syndrome (FXS) is the most prevalent inherited cause of autism and is accompanied by behavioral and sensory deficits. Errors in the wiring of the brain during early development likely contribute to these deficits, but the underlying mechanisms are unclear. Spontaneous activity patterns, which are required for fine-tuning neuronal networks before the senses become active, are perturbed in rodent models of FXS. Here, we investigated spontaneous network activity patterns in the developing visual cortex of the Fmr1 knockout mouse using in vivo calcium imaging during the second postnatal week, before eye opening. We found that while the frequency, mean amplitude and duration of spontaneous network events were unchanged in the knockout mouse, pair-wise correlations between neurons were increased compared to wild type littermate controls. Further analysis revealed that interneuronal correlations were not generally increased, rather that low-synchronization events occurred relatively less frequently than high-synchronization events. Low-, but not high-, synchronization events have been associated with retinal inputs previously. Since we found that spontaneous retinal waves were normal in the knockout, our results suggest that peripherally driven activity is underrepresented in the Fmr1 KO visual cortex. Therefore, we propose that central gating of retinal inputs may be affected in FXS and that peripherally and centrally driven activity patterns are already unbalanced before eye opening in this disorder.

Unique Features of Network Bursts Emerge From the Complex Interplay of Excitatory and Inhibitory Receptors in Rat Neocortical Networks.

Spontaneous network activity plays a fundamental role in the formation of functional networks during early development. The landmark of this activity is the recurrent emergence of intensive time-limited network bursts (NBs) rapidly spreading across the entire dissociated culture in vitro. The main excitatory mediators of NBs are glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and N-Methyl-D-aspartic-acid receptors (NMDARs) that express fast and slow ion channel kinetics, respectively. The fast inhibition of the activity is mediated through gamma-aminobutyric acid type A receptors (GABAARs). Although the AMPAR, NMDAR and GABAAR kinetics have been biophysically characterized in detail at the monosynaptic level in a variety of brain areas, the unique features of NBs emerging from the kinetics and the complex interplay of these receptors are not well understood. The goal of this study is to analyze the contribution of fast GABAARs on AMPAR- and NMDAR- mediated spontaneous NB activity in dissociated neonatal rat cortical cultures at 3 weeks in vitro. The networks were probed by both acute and gradual application of each excitatory receptor antagonist and combinations of acute excitatory and inhibitory receptor antagonists. At the same time, the extracellular network-wide activity was recorded with microelectrode arrays (MEAs). We analyzed the characteristic NB measures extracted from NB rate profiles and the distributions of interspike intervals, interburst intervals, and electrode recruitment time as well as the similarity of spatio-temporal patterns of network activity under different receptor antagonists. We show that NBs were rapidly initiated and recruited as well as diversely propagated by AMPARs and temporally and spatially maintained by NMDARs. GABAARs reduced the spiking frequency in AMPAR-mediated networks and dampened the termination of NBs in NMDAR-mediated networks as well as slowed down the recruitment of activity in all networks. Finally, we show characteristic super bursts composed of slow NBs with highly repetitive spatio-temporal patterns in gradually AMPAR blocked networks. To the best of our knowledge, this study is the first to unravel in detail how the three main mediators of synaptic transmission uniquely shape the NB characteristics, such as the initiation, maintenance, recruitment and termination of NBs in cortical cell cultures in vitro.

A scalable node ordering strategy based on community structure for enhanced temporal network visualization

Temporal networks have been used to map the structural evolution of social, technological, and biological systems, among others. Due to the large amount of information on real-world temporal networks, increasing attention has been given to issues related to the visual scalability of network visualization layouts. However, visual clutter due to edge overlap remains the main challenge calling for efficient methods to improve the visual experience. In this paper, we propose a novel and scalable node reordering approach for temporal network visualization, named Community-based Node Ordering (CNO), combining static community detection with node reordering techniques to enhance the identification of visual patterns. The perception of trends, periodicity, anomalies, and other temporal patterns, is facilitated, resulting in faster decision making. Our method helps not only the study of network activity patterns within communities but also the analysis of relatively large networks by breaking down its structure in smaller parts. Using CNO, we further propose a taxonomy to categorize activity patterns within communities. We performed a number of experiments and quantitative analyses using two real-world networks with distinct characteristics and showed that the proposed layout and taxonomy speed up the identification of patterns that would otherwise be difficult to see.

Interneuron Transplantation Creates New Network States and Rescues Social Behavior Deficits in a Mouse Model of Autism

Abstract INTRODUCTION Inhibitory interneuron transplantation is a prospective treatment for neuropsychiatric disorders, but it is unclear whether transplantation might elicit therapeutic effects by reversing preexisting abnormalities, nonspecifically increasing synaptic inhibition, or engaging mechanisms that vary depending on recipient background and behavioral state. METHODS We transplanted wild-type embryonic interneuron precursors into mice lacking an autism-associated gene, Pten, in inhibitory interneurons. Additionally, we performed transplantation into wild-type recipient mice, and then examined the recipient behavior, cellular physiology in Vitro, and network physiology in Vivo. RESULTS Transplantation rescued social behavior deficits in Pten mutants without normalizing excessive synaptic inhibition in Vitro or reduced baseline gamma oscillatory power in Vivo. However, transplantation altered recipient electroencephalography (EEG) responses observed specifically during periods of social interaction. When transplantation was performed into wild-type recipients, the subtype composition of the transplanted population varied from that observed in Pten mutants, and, moreover, recipients did not exhibit alterations to social behavior or related EEG responses. CONCLUSION Interneuron transplantation elicits recipient- and behavioral state-dependent effects, and normalizes behavior by creating new patterns of network activity, rather than restoring wild-type states. Interneuron transplantation may provide a novel therapeutic approach to the treatment of autism and related neuropsychiatric disorders.

Retrieval of spatial representation on network level in hippocampal CA3 accompanied by overexpression and mixture of stored network patterns

Retrieval of stored network activity pattern has been shown as a competitive transition from one attractor state to another, orchestrated by local theta oscillation. However, the fine nature of this process that is considered as substrate of memory recall is not clear. We found that hippocampal network recall is characterized by hyperactivity in the CA3 place cell population, associated with an “overexpression” of the retrieved network pattern. The overexpression was based on recruitment of cells from the same (recalled) spatial representation with low expected firing probability at the given position. We propose that increased place cell activation during state transitions might facilitate pattern completion towards the retrieved network state and stabilize its expression in the network. Furthermore, we observed frequent mixing of both activity patterns at the temporal level of a single theta cycle. On a sub-theta cycle scale, we found signs of segregation that might correspond to a gamma oscillation patterning, as well as occasional mixing at intervals of less than 5 milliseconds. Such short timescale coactivity might induce plasticity mechanisms, leading to associations across the two originally decorrelated network activity states.

Human cerebrospinal fluid promotes spontaneous gamma oscillations in the hippocampus in vitro

Gamma oscillations (30-80 Hz) are fast network activity patterns frequently linked to cognition. They are commonly studied in hippocampal brain slices in vitro, where they can be evoked via pharmacological activation of various receptor families. One limitation of this approach is that neuronal activity is studied in a highly artificial extracellular fluid environment, as provided by artificial cerebrospinal fluid (aCSF). Here, we examine the influence of human cerebrospinal fluid (hCSF) on kainate-evoked and spontaneous gamma oscillations in mouse hippocampus. We show that hCSF, as compared to aCSF of matched electrolyte and glucose composition, increases the power of kainate-evoked gamma oscillations and induces spontaneous gamma activity in areas CA3 and CA1 that is reversed by washout. Bath application of atropine entirely abolished hCSF-induced gamma oscillations, indicating critical contribution from muscarinic acetylcholine receptor-mediated signaling. In separate whole-cell patch clamp recordings from rat hippocampus, hCSF increased theta resonance frequency and strength in pyramidal cells along with enhancement of h-current (Ih ) amplitude. We found no evidence of intrinsic gamma frequency resonance at baseline (aCSF) among fast-spiking interneurons, and this was not altered by hCSF. However, hCSF increased the excitability of fast-spiking interneurons, which likely contributed to gamma rhythmogenesis. Our findings show that hCSF promotes network gamma oscillations in the hippocampus in vitro and suggest that neuromodulators distributed in CSF could have significant influence on neuronal network activity in vivo.

Context-Dependent Relationships between Locus Coeruleus Activation and Coordinated Neural Activity Patterns in the Anterior Cingulate Cortex

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these modulatory effects on neural activity patterns in vivo is not well understood. Here we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These effects, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations are reduced when tonic (baseline) LC activity increases but are enhanced when external events drive phasic LC responses. Both effects also covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can, at least in part, result from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.

Cathodal tDCS impacts the fear network activity and connectivity patterns during a therapy-like extinction session

Background Transcranial direct current stimulation (tDCS) has been studied as an add-on enhancer of extinction-based treatments for anxiety disorders. Previous studies have showed that although anodal tDCS enhances fear extinction it also leads to decreased stimuli discrimination and consequent generalization of the fear response. Here we aim to understand the impact of cathodal tDCS in the neural activity and connectivity patterns between the tDCS target site (the right dorsolateral prefrontal cortex; rDLPFC) and the extant fear network (the bilateral amygdala, bilateral hippocampus, bilateral insula and the ventro medial prefrontal cortex [vmPFC]). Additionally we aim to see if cathodal tDCS leads to adverse effects similar to the ones observed with anodal stimulation. Methods We used a three-day fear conditioning procedure (day 1–fear acquisition; day 2–20-min 1 mA cathodal tDCS (only tDCS group)+ fear extinction; day 3–fear re-instatement and fear re-extinction). Thirty-four female participants where randomly assigned to either the tDCS group (n = 16) or the control group (n = 18). In day 1 fear acquisition was confirmed by self-reported contingency ratings. In day 2 we observed the between group differences in neural activity and connectivity patterns during extinction. In day 3 we observed the self-reported measures of stimuli contingency and reaction times during an approach-avoidance task to tackle the unconscious, automatic and involuntary avoidance responses. Results Functional magnetic resonance imagining (fMRI) group-level maps showed that cathodal tDCS over rDLPFC effectively reduces the right hippocampus activity during the processing of the conditioned stimuli during extinction. Furthermore, group-level psychophysiological interaction analysis (PPI) showed that cathodal tDCS effectively interferes with the fear response neural network. In particular, during the processing of the conditioned stimuli in late extinction phase, tDCS inverts the positive coupling between the vmPFC and a broad range of prefrontal structures, in particular the right and left DLPFC that participate in the regulation of negative emotions. Finally, day 3 measures showed decreased contingency ratings for the neutral cue and increased stimuli discrimination in the tDCS group, suggesting that cathodal tDCS does not lead to increased generalization. Conclusion Our promising results show that 1 mA cathodal tDCS effectively interferes with a large-scale cortico-subcortical fear network during extinction, and confirms that tDCS is a potential add-on strategy to enhance extinction-based treatments. Future studies should observe the adequate parameters of stimulation for the simultaneous enhancement of extinction learning and increased stimuli discrimination.

Excitation/inhibition imbalance and impaired neurogenesis in neurodevelopmental and neurodegenerative disorders.

The excitation/inhibition (E/I) balance controls the synaptic inputs to prevent the inappropriate responses of neurons to input strength, and is required to restore the initial pattern of network activity. Various neurotransmitters affect synaptic plasticity within neural networks via the modulation of neuronal E/I balance in the developing and adult brain. Less is known about the role of E/I balance in the control of the development of the neural stem and progenitor cells in the course of neurogenesis and gliogenesis. Recent findings suggest that neural stem and progenitor cells appear to be the target for the action of GABA within the neurogenic or oligovascular niches. The same might be true for the role of neuropeptides (i.e. oxytocin) in neurogenic niches. This review covers current understanding of the role of E/I balance in the regulation of neuroplasticity associated with social behavior in normal brain, and in neurodevelopmental and neurodegenerative diseases. Further studies are required to decipher the GABA-mediated regulation of postnatal neurogenesis and synaptic integration of newly-born neurons as a potential target for the treatment of brain diseases.

Cheater's hide and seek: Strategic cognitive network activation during ethical decision making

We consider the dynamic process by which people cognitively activate their social networks during ethical decision making. We compare actors’ goals during anticipatory and ex-post phases of ethical decision making, and propose that they trigger hide-and-seek patterns of network activation. Experiment 1 links cognitively activated network structures with self-report ethical behavior. Consistent with “hiding goals,” actors randomly assigned to anticipate behaving unethically (versus honestly or in the control condition), activated sparser networks that could better hide unethicality (Experiment 2). Consistent with “seeking” goals, participants randomly assigned to unethical (versus honest) conditions mentally activated dense networks, seeking out social support to uphold their sense of self (Experiment 3a). This network activation process is mitigated when participants affirm themselves (Experiment 3b). Experiment 4 replicates these hide and seek patterns of social network activation in a single study that captures both the anticipatory and ex-post phases of ethical decision making.

Dynamic network coding of working-memory domains and working-memory processes

The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machine-learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes (encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity that they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to brain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.