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However, the older version of spDCM showed a positive influence 0. Stability depended profoundly on the size of the network: parameter estimates showed higher stability in two-region networks than in larger networks for both versions. Comparison of posterior parameter estimates within the auditory network.

B and C. NeuroImage , — Neuron , 87 3 : — The main goal of this study was to investigate changes in effective connectivity associated with reward and punishment. More specifically, changes in connectivity between the ventral striatum VS , anterior insula aI , anterior cingulate cortex ACC and occipital cortex OCC that are related to win- and loss- feedback were studied. Here, fMRI data from the human connectome project [1] was used for our study purposes.

Data from unrelated subjects performing a gambling task was analyzed. In short, participants played a card game where they had to guess whether the upcoming card would be higher or less than 5 range was between 1 and 9. After the gamble, feedback was provided indicating a reward, punishment or neutral trial. The minimally preprocessed data was used and extra spatially smoothed with a 5-mm FWHM Gaussian kernel. We specified a fully connected model i. The fully connected model was estimated for every subject and then used in the recently proposed parametric empirical Bayesian PEB, [3] framework for estimating DCM parameters at the group level.

Finally, we used Bayesian model reduction to obtain the best nested models. Conclusion: Overall, both win- and loss- feedback have a general increasing effect on effective connectivity. The main difference between win and loss can be observed for the connection from aI and OCC with loss-feedback having a decreased effect. In addition, only win-feedback increases the connection from VS to aI. Overall, the VS appears as a key region in conveying loss and win information across the network. BMA modulatory parameters at the group level are shown for A.

Van Essen, D. NeuroImage, , 62— Friston, Karl J. Dynamic causal modelling. Neuroimage , , 19 4 : — Bayesian model reduction and empirical Bayes for group DCM studies. Increasingly, computational models of brain activity are applied to investigate the relation between structure and function. In addition, biologically interpretable dynamical models may be used as unique predictive tools to investigate the impact of structural connectivity damage on brain dynamics.

In this study, we compared global biophysical model parameters between brain tumor patients and healthy controls. Data were preprocessed and converted to a subject-specific structural and functional connectivity matrix using a modified version of the TVB preprocessing pipeline [3].

In order to simulate brain dynamics, the reduced Wong-Wang model [4] was used. This is a dynamical mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network. To this end, values of the global scaling factor G and the local feedback inhibitory synaptic coupling J i were varied. Values of G and J i yielding optimal correspondence were then compared between the brain tumor patient groups and healthy controls.

Visually, no clear group differences are apparent. In future studies, larger sample sizes will be utilized, as data collection is still ongoing and more efforts to data sharing across labs are undertaken. In addition, local model parameter alterations in the vicinity of the lesion will be examined, since global model parameters might not be sufficiently sensitive to capture local lesion effects. Global scaling factor G ; B. Local feedback inhibitory synaptic coupling J i. The Virtual Brain: A simulator of primate brain network dynamics. Frontiers in Neuroinformatics , 7 :1— UK Data Archive.

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. How local excitation-inhibition ratio impacts the whole brain dynamics. The Journal of Neuroscience , — The shape of neuronal arborizations defines amongst other aspects their physical connectivity and functionality. Yet an efficient method for quantitatively analyzing the spatial structure of such trees has been difficult to establish. The wide diversity of neuronal morphologies in the brain, even for cells identified by experts as of the same type, renders an objective classification scheme a challenging task.

We propose a Topological Morphology Descriptor [1], inspired by Topological Data Analysis, to quantitatively analyze the branching shapes of neurons, which overcomes the limitations of existing techniques. The TMD encodes the morphology of the tree into a simplified topological representation that preserves sufficient information to be useful for the comparison and the distinction of different branching patterns.

Topological morphology descriptor. The neuronal tree is mapped into a barcode. Each bar represents the lifetime of a branch; its start and end distance from the soma. This method is applicable to any tree-like structure, and we demonstrate its generality by applying it to groups of mathematical random trees and neuronal morphologies. Our results show that the TMD of tree shapes reliably and efficiently distinguishes different shapes of trees and neurons. Therefore, the TMD provides an objective benchmark test of the quality of any grouping of branching trees into discrete morphological classes.

Our results demonstrate that the TMD can enhance our understanding of the anatomy of neuronal morphologies. Ascoli G. Markram H. Muller E. Mohan H. Heterogeneity of neural attributes is recognized as a crucial feature in neural processing. Thus, we have developed theoretical methods based on [1] to characterize the firing rate distribution of spiking neural networks with intrinsic and network heterogeneity [2], both of which have been widely reported in experiments. This relationship intrinsic and network can lead to various levels of firing rate heterogeneity, depending on regime.

Next we adapt our theory to a delayed feedforward spiking network model of the electrosensory system of the weakly electric fish. Experimental recordings indicate that feedforward network input can mediate response heterogeneity of pyramidal cells [3]. We demonstrate that structured connectivity rules, derived from our theory, can lead to qualitatively similar statistics as the experimental data.

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Thus, the model demonstrates that intrinsic and network attributes do not interact in a linear manner but rather in a complex stimulus-dependent fashion to increase or decrease neural heterogeneity and thus shape population codes. As evidence for heterogeneity shaping population codes, we also present some preliminary work using recordings from electric fish subject to noisy stimuli.

We use a GLM model for each neuron, fit the parameters to the data using standard maximum likelihood methods, and perform Bayesian estimation of the stimuli. We find that firing rate heterogeneity is a signature of optimal Bayesian stimulus estimation of noisy stimuli. Interestingly, the firing rate correlation is not an indicator of decoding performance for a given population of neurons.

Nicola, C. Ly, S. Journal of Computational Neuroscience , — Marsat, G. Hupe, K. Allen: Heterogeneous response properties in a population of sensory neurons are structured to efficiently code naturalistic stimuli. Program KnowledgeSpace [1] is a community encyclopedia platform currently under development where neuroscience data and knowledge are synthesized. KnowledgeSpace aims to provide a global interface between current brain research concepts and the data, models and literature about them.

It is an open project that welcomes participation and contributions from members of the global research community. KnowledgeSpace version 1. During the pre-launch phase, work focused on linking concepts to data, models, and literature from existing community resources. Initial content included in KnowledgeSpace covers ion channels, neuron types, and microcircuitry. For each content type, physiology, gene expression, anatomy, models, and morphology data sources are available.

Going forward we will enhance atlas representations of the mouse brain linking concepts to data, models, and literature, and an atlas representation of the human brain that links to available data, models, and literature will be implemented. Links to analysis tools will also be integrated into the KnowledgeSpace data section.

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The project will also develop protocols, standards, and mechanisms that allow the community to add data, analysis tools, and model content to KnowledgeSpace. The KnowledgeSpace also represents an important component of the Neuroinformatics Platform being deployed in the Human Brain Project web portal. The cerebellum plays an essential role in tasks ranging from motor control to higher cognitive functions such as language processing and receives input from many brain areas. A general framework for understanding cerebellar function is to view it as an adaptive-filter [1].

Within this framework, understanding, from computational and experimental studies, how the cerebellum processes information and what kind of computations it performs is a complex task, yet to be fully accomplished. In the case of computational studies, this reflects a need for new systematic methods to characterize the computational capacities of cerebellum models. In the present work, to fulfill this need, we apply a method borrowed from the field of machine learning to evaluate the computational capacity of a prototypical model of the cerebellum cortical network.

Using this method, we find that the model can perform both linear operations on input signals —which is expected from previous work-, and —more surprisingly- highly nonlinear operations on input signals. The model that we study is a simple rate model of the cerebellar granular layer in which granule cells inhibit each other via a single-exponential synaptic connection.

The resulting recurrent inhibition is an abstraction of the inhibitory feedback circuit composed of granule and Golgi cells. Purkinje cells are modelled as linear trainable readout neurons. The model was originally introduced in [2, 3] to demonstrate that models of the cerebellum that include recurrence in the granular layer are suited for timing-related tasks. Further studies carried out in [4] showed how the recurrent dynamics of the network can provide the basis for constructing temporal filters.

The method, described in detail in [5], and developed in the context of the artificial intelligence algorithm known as reservoir computing [6], consists in feeding the network model with a random time dependent input signal and then quantifying how well a complete set of functions each function representing a different type of computation of the input signal can be reconstructed by taking a linear combination of the neuronal activations. The result is a quantitative estimate of the number of different computations that can be carried out by the model.

We conducted simulations with granule cells. Our results show that the cerebellum prototypical model has the capability to compute both linear and highly nonlinear functions of its input. Specifically, the model is able to reconstruct Legendre polynomial functions up to the 10th degree. Despite their abstract nature, these two properties are essential to perform typical cerebellar functions, such as learning the timing of conditioned reflexes or fine-tuning nonlinear motor control tasks or, we believe, even higher cognitive functions. In future work, we hope to confirm these abstract results by applying our cerebellum model to typical cerebellar tasks.

Additionally, we will compare our results with a very recent work which studied how a model of the cerebellum could solve several machine learning tasks [7]. Dean P, Porril J: The cerebellar microcircuit as an adaptive filter: experimental and computational evidence. Nat Rev Neurosci , 11 1 : 30— Neural Comput , 17 5 : — Yamazaki T, Tanaka S: The cerebellum as a liquid state machine.

Neural Netw , 20 3 : — Sci Rep , 2: Computer Science Review , 3: — In this work, we investigated the role of connectivity in driving spontaneous activity towards critical, sub-critical or super-critical regimes, by combining both experimental and computational investigations. We estimated the functional connectivity of cortical networks by using cross-correlation based methods, collected in the software T ool C onnect [4].

In particular, our cross-correlation algorithm is able to reliably and accurately infer functional and effective excitatory and inhibitory links in ex vivo neuronal networks, while guaranteeing high computational performances necessary to process large-scale population recordings. To support our experimental investigations, we also developed a computational model of neuronal network, made up of Izhikevich neurons [5] structurally connected by following well defined topologies of connectivity e. Simulations of the model demonstrated that the presence of hubs, the physiological balance between excitation and inhibition, and the concurrent presence of scale-free and small-world features are necessary to induce critical dynamics.

J Neurosci , 23 35 — Neuroscience , 4 — Front Neuroinform , 10 Izhikevich EM: Simple model of spiking neurons. Understanding how such transition takes place might shade light on the emergence of the rich repertoire of neuronal dynamics underlying brain computation. Sleep-wake transition is a widely-studied phenomenon ranging in experimental, computational and theoretical frameworks [3—5], however it is still debated how brain state changes occur.

We also shown how this phase of activity pattern bistability is captured by a mean-field rate-based model of a cortical column. Guided by this mean-field model, spiking neuron networks are devised to reproduce the electrophysiological changes displayed during the transition.

We extended our previous findings by performing a stability analysis of the competing attractors, observing a modulation of their stability, that affect the dynamics of the Down-to-AL transition and the residence dynamics within the AL state. Moreover, we found that the mean-field model remarkably matches the stability modulation observed in experiments. This match between theory and experiments further strengthens our claim that cortical assemblies of neurons display a Hopf bifurcation when anesthesia fades out.

Such observation gives important information on intrinsic dynamical properties of the system, suggesting that it does not respond in a passive way but rather it is a strongly nonlinear component, capable to drastically change its dynamics under small changes of relevant parameters. This can provide a computational advantage in terms of the capability of producing a rich repertoire of network states during wakefulness. Arch Ital Biol , — Capone Cristiano, Mattia Maurizio: Speed hysteresis and noise shaping of traveling fronts in neural fields: role of local circuitry and nonlocal connectivity.

Scientific Reports , 7: doi: Neuroimage , — Deco, P. Hagmann, A. Hudetz, and G. Tononi: Modeling resting-stat state functional networks when the cortex falls asleep: local and global changes. Cortex , vol. X , vol. Bernstein Conference Numerous studies, across different sensory modalities, suggest that the neural code employed in early stages of the cortical hierarchy can be explained in terms of Efficient Coding. This principle states that information is represented in a neural population so as to minimize redundancy.

This is achieved when the features to which neurons are tuned occur in a statistically independent fashion in the sensory environment. Several studies using these methods have shown a qualitative similarity between predicted RFs and those found in primary visual cortex, for simple and complex cells with linear and non-linear RF structures, respectively. Recent methods allow direct experimental estimation of RFs. Using these methods, we report on the first quantitative evaluation of the Efficient Coding Hypothesis at the level of RF structures, including both simple and complex cells.

Experimental RF structures were estimated from recordings of single-units in the primary visual cortex of anaesthetized cats in response to presentation of Gaussian white noise. RFs were estimated from recordings assuming a General Quadratic Model for spike rate and performing maximum likelihood estimation on the response given the stimulus. Theoretical Efficient Coding RF structures were inferred by performing unsupervised learning on a set of natural images, under the assumption of Efficient Coding that evoked spike rates were statistically independent and sparsely distributed, and using the same General Quadratic Model as for the experimental RFs.

We recovered spatial RF structures from 94 well isolated single-units in 3 cats, of which 26 were classified as simple cells, 38 as complex cells and 30 as a mixed cell class. The results confirmed the qualitatively similarity of theoretical RF structures from Efficient Coding with those estimated experimentally. However, quantitatively a number of discrepancies were observed as well as similarities.

The quantitative discrepancies we found were robust to changes in meta-parameters, such as the degree of image compression in pre-processing or the source of natural images. The results suggest that the experimental RFs are sub-optimal in terms of coding efficiency. However, it is important to note that we used a deterministic model of spike rate in response to an image stimulus: a stochastic model is more realistic and may limit the coding efficiency of the theoretical result, bringing it in closer quantitative agreement with experiment.

Dentate gyrus granule cells provide powerful feedforward excitatory drive onto a local circuit of CA3 pyramidal cells and inhibitory interneurons, and is believed to selectively activate subsets of pyramidal cells in the CA3 recurrent network for encoding and recall of memories. Cholinergic receptors provide a key means to modulate this circuit, increasing cellular excitability and altering synaptic release, but the combined action of these changes on information processing between the dentate gyrus and CA3 remains unknown.

The short-term plasticity dynamics of these responses were used to constrain a computational model of mossy fibre driven transmission across a range of stimulation patterns. This model was then used to analyse how aceytlcholine influences encoding and recall in a spiking neural network model of CA3 to study encoding and recall of neuronal ensembles driven by mossy fibre input. We found that acetylcholine lowers the requirements for encoding neuronal ensembles and increases memory storage in CA3.

Several brain areas involved in decision making, impulsivity and reward valuation, such as the prefrontal cortex and striatum, are interconnected to the STN, and activity in these areas might be modulated by STN DBS. Understanding the relationship between STN functioning and ICD would help developing better therapies for PD while shedding light on the mechanisms of human decision making. All patients were under dopamine replacement therapy, and half of them were affected by GD.

In the task patients were asked to decide between a high risk HR and low risk LR option, the first being associated to a negative expected value, but to a high reward in case of win. Reaction times were strongly affected by trial type, with GD patients and non-GD patients quicker in taking HR and LR decisions respectively, suggesting that decision is actually determined before options presentation. Analyzing low frequency STN LFP we found that amplitude of fluctuations, recorded during specific intervals preceding option presentation, carried significant information about future choices on single trials in patients affected by GD but not in those not affected.

These results complement previous studies about the role of inhibiting impulsive behavior displayed by the STN activity. Beta-range STN fluctuations were found to be modulated by the level of conflict in decisions [3], while our results suggest that the lower frequencies, which are functionally correlated with different cortical areas [4], play instead a role to prevent pathological risk attraction.

Science : — Distinct mechanisms mediate speed-accuracy adjustments in cortico-subthalamic networks. We present and discuss data-driven models of biophysically detailed hippocampal CA1 pyramidal cells and interneurons of a rat. They have been integrated into the BSP in an intuitive graphical user interface guiding the user through all steps, from selecting experimental data to constrain the model, to run the optimization generating a model template and, finally, to explore the model with in silico experiments.

Electrophysiological features were extracted from somatic traces obtained from intracellular paired recordings performed using sharp electrodes on CA1 principal cells and interneurons with classical accommodating cAC , bursting accommodating bAC and classical non-accommodating cNAC firing patterns. The resulting optimized ensembles of peak conductances for the ionic currents, were used to explore and validate the model behavior during interactive in silico experiments carried out within the HBP Collaboratory.

Such a modelling effort has been undertaken in the context of the Human Brain Project and constitutes one of the major steps in the workflow that is being used to build a cellular level model of a rodent hippocampus. It is widely accepted that the cerebellum and basal ganglia BG and play key roles in motor adaptation in error based and non-error based one, respectively [1].

However, despite considerable number of studies, the interactions between BG and cerebellum are not completely understood [1]. In particular, in the experiments it is difficult to dissociate the adaptation performed by cerebellum and by BG. To our knowledge no mathematical model exists that explains the conditions in which visual perturbations make reinforcement learning in the BG the main mechanism of motor adaptation. We have developed a model that integrates a phenomenological representation of the cerebellum and a previously published firing rate-based description of BG network [3], and mimics the trial-to-trial motor adaptation in 2D reaching arm movements.

Cerebellum is implemented as an artificial neural network performing corrections of the motor program, descending from motor cortex to spinal cord, via supervised learning. Cerebellum output represents a correction, which adds to the motor command descending from the MC to the spinal cord. This correction is calculated as a linear transformation of the motor command. The transformation matrix is updated by the supervised learning algorithm, accounting for the vector error provided by the visual feedback.

The corrected signal goes to the spinal cord neuron network that controls a two-joint arm to perform center-out reaching movements. Our model simulations suggest that when the perception of the vector error provided to the cerebellum is significantly perturbed, the faulty cerebellar corrections adversely affect or even completely destroy motor adaptation.

We speculate and show via simulations that error-based learning in cerebellum has an adaptive critic component which effectively suppresses error-based mechanisms to enable reinforcement-based motor adaptation. Izawa J, Shadmehr R. Learning from sensory and reward prediction errors during motor adaptation. PLoS Comput Biol. European Journal of Neuroscience ; 38 6 : — Reward based motor adaptation mediated by basal ganglia. Frontiers in Computational Neuroscience. Bridging descriptions of brain activity across different scales is a major challenge for theoretical neuroscience.

Numerous experimental techniques are available to measure brain activity, ranging from single cells recordings to population measurements of the average activity of large ensembles of neurons. It is often in these population-level recordings e. A large body of experimental and computational works indicates that the interplay between synaptic processing and recurrent inhibition is the key ingredient to generate such oscillations, in a mechanism commonly referred to as Interneuronal Gamma oscillations ING [1, 2].

Here, we analyse the dynamics of a network of quadratic integrate-and-fire neurons with time-delayed synaptic interactions, both in their excitable and self-oscillatory regime. Time delays have been indeed shown to approximate the effect of synaptic kinetics [3]. Using the so-called Lorentzian ansatz [4, 5], we derive a set of two delayed firing rate equations FREs. Due to their analytical tractability, the FREs allow us to find exact boundaries of stability for the parameters regions of oscillatory collective synchrony-CS and asynchronous dynamics. Moreover, for inhibitory coupling, we observe a more complex oscillatory state, the so-called quasiperiodic partially synchronized state QPS.

Interestingly, macroscopically this state strongly resembles the sparsely synchronized state observed in networks of leaky integrate-and-fire neurons subjected to strong recurrent inhibition and noise [6]. However, microscopically, these two states have qualitatively different dynamics, suggesting a dichotomy between microscopic and macroscopic dynamics. Moreover, sufficiently increasing inhibition, the QPS undergoes a series of period doubling bifurcation that eventually leads to chaos.

Notably, only the collective dynamics is chaotic, while microscopically neurons are non-chaotic. Finally, we find that while excitation always leads to collective synchronous oscillations, inhibition fails to synchronize neural activity when a precise degree of heterogeneity is exceeded, consistently with previous numerical studies of heterogeneous, inhibitory spiking neural networks [7].

Int J Psychophysiol , 38 — Physica D , — Phys Rev X , 5: Phys Rev Lett , Brunel N, Hakim V: Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput , 11 J Neurosci , 16 20 — Anterior cingulate cortex ACC plays regulatory and cognitive roles. Its functions are associated with conflict and performance monitoring, regulation of strategy and response selection, all of which depend on reward monitoring and its anticipation [1]. Concurrently, the firing rate of ACC neurons gradually increased or decreased along with reward expectancy.

It happened when the reward was certain and correct decisions could only bring animal closer to the reward. However, when certainty about outcome was removed and no notion of reward proximity was provided the progressive modulation of behavior and ACC activity disappeared. Here we tested whether such motivation signal can be also found in the circumstances when the reward is no longer certain and the animal choices brings reward closer or further away but the information about reward closeness reminds - the situation more common in the economic decisions of everyday life.

We recorded single unit activity from dorsal ACC while monkey performed token gambling task. On each trial, monkeys gambled to gain certain number of tokens, but they could also lose tokens. The collection of six tokens resulted in a jackpot reward delivery. The number of collected tokens was displayed on the monitor and was known to the animal. The animal learnt the task and exhibited risk seeking behavior as previously reported [3]. The analysis of behavioral data revealed that animal performance percent of correct responses depended on the number of previously collected tokens.

The relation was not monotonic with the drop of performance after reward administration. At the same time, the significant fraction of recorded neurons exhibited tuning towards the number of previously collected tokens. Our preliminary results suggest that ACC monitors rewards in risky conditions, and that neuronal signals could be directly related to the motivation of the animal.

Annu Rev Neurosci , 39 : — Science , : — Azab H, Hayden BY: Shared roles of dorsal and subgenual anterior cingulate cortices in economic decisions. Bipolar disorder BPD is characterized by oscillations alternating between manic and depressive episodes causing swings in moods. Some medications popularly used for stabilizing mood include selective serotonin reuptake inhibitors and lithium therapy. This computational study focuses on the serotonergic system dysfunction, and particularly, understanding their contribution to cortico-basal ganglia network CGBN dynamics for stability and recurrence of moods.

To this end, we try to model the disorder in a decision-making framework that tries to choose between actions of positive or negative affects. We propose a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of CBGN and relate this impairment to the manic and depressive episodes of BPD. The proposed model of BPD is derived from an earlier model, that describes the roles of dopamine and serotonin in the action selection dynamics of CBGN.

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  7. In that model, rewarding actions are selected based on the Utility function, which combines Value and Risk functions as follows eqn. The lumped model was later extended to a detailed network model of BG. There exists clinical and experimental evidence supporting abnormality in serotonin levels and reward sensitivity in case of BPD. This preliminary model can be extended to a detailed network model. Future work will include expanding CBGN with neural models of limbic system, and predicting plausible treatment strategies for effectively dealing with the onset and progression of BPD symptoms.

    Activity of posterior parietal cortex PPC neurons exhibits self-motion tuning to both ongoing and impeding movements, which may reflect behavioral planning [1]. A major input to PPC originates from the frontal medial agranular cortex AGm , which is believed to be involved in complex motor planning.

    In the monkey, Pesaran and colleagues [2] showed that fronto-parietal coherence is stronger in free-choice tasks than in instructed trials, probably activating different decision-related circuits in these areas. Therefore, we hypothesize that in the rat the interaction between AGm and PPC may be instrumental in coordinating decision making and motor planning.

    Conclusions: Our results indicate a complex regulation of oscillatory behavior in PPC and AGm during free behavior in rats. AGm leads PPC and both the frequency of the oscillation and the time delay between the two areas change as a function of behavioral events. A and C. B and D. Withlock J. Sutherland, Menno P. Moser: Navigating from hippocampus to parietal cortex. PNAS vol. Nature — Schizophrenia has long been described as a syndrome of disordered connectivity in the brain. While originally based on clinical symptomatology, neurophysiological evidence for this concept has been found in imaging studies in humans with schizophrenia.

    It has also been found that cortical pyramidal neurons have a reduced density of the synaptic spines necessary for cellular communication in postmortem brain tissue recovered from people with schizophrenia. However, functional evidence for disconnectivity at the level of local neuronal circuits is limited. To address this question, we characterized neuronal dynamics between groups of simultaneously recorded cortical neurons in data obtained from both primate and mouse models of schizophrenia. Neural data were obtained from multielectrode recording arrays inserted into the parietal and prefrontal cortices of macaque monkeys while the animals performed a cognitive control task that measures a specific cognitive impairment in human patients with schizophrenia.

    Phencyclidine, an NMDA receptor NMDAR antagonist that has long been used as a pharmacological model of psychosis, was administered systemically on alternating days with injections of saline. In the mouse experiments, analogous data were obtained from medial prefrontal cortex in awake head-fixed mice during locomotion. Cross-correlation analysis was performed on spike trains from pairs of simultaneously recorded neurons to characterize changes in synchrony between conditions.

    In the phencyclidine condition, there was a reduction in synchronous firing between pairs of cells. A similar rate-independent reduction in precise synchrony was also found in medial prefrontal cortical neuronal ensemble recordings obtained from Dgcr8 mice as compared to controls, suggesting that this is may be a consistent finding related to the root pathophysiology of schizophrenic processes. To characterize deficits in synaptic communication between neurons in the disease state, we employed higher-order transfer entropy TE metrics to identify pairs of cells that exhibited effective connectivity Ito et al, , PLOS One.

    Consistent with the disconnection hypothesis of schizophrenia, we found that acute administration of PCP resulted in a reduction in the percent of cell pairs identified as significantly functionally connected by TE analysis, as well as a reduction in the overall distribution of population shared information.

    This result suggests a cellular basis for the reduced information-processing capabilities seen in schizophrenics performing prefrontal cortex-dependent tasks, as well as synaptic disconnection. In summary, these results display a reduction in both zero-lag synchrony and cellular-level functional connectivity in two very distinct animal models of schizophrenia.

    It is well known that coincident firing of action potentials facilitates connectivity between neurons, and asynchrony results in disconnection. Thus, the results presented here support the notion that alterations in precise spike timing may be an underlying driving factor towards reduced functional connectivity in schizophrenia, providing a new mechanistic model for disease pathophysiology.

    Current neuromodulation techniques for seizure suppression, such as vagus nerve or deep brain stimulation, have shown some clinical efficacy. Yet their application is complicated by the large parameter space of electrical stimulation settings inherent to these systems. A physician must skillfully choose stimulation parameters such as frequency, amplitude, and pulse width for each individual patient in order to effectively reduce their incidence of seizures. We demonstrate an algorithm capable of automatically generating a continuous stimulation waveform to suppress neural activity and minimize total stimulation energy.

    We treat the suppression of neural activity as a linear-quadratic-Gaussian LQG control problem. The resulting optimal controller consists of a Kalman filter and a linear-quadratic regulator LQR. The effectiveness of the LQG controller in suppressing seizure biomarkers was first verified in a computational model of epilepsy called Epileptor [1], which simulates local field potential LFP recordings within a seizure focus. We built a model of the generated LFPs using the Ho-Kalman algorithm [2] for subspace system identification. The Kalman filter estimated the state of the system and a feedback control signal provided by the LQR successfully prevented seizures during stimulation, even while varying the Epileptor model parameters.

    We then implemented the LQG controller in an in vivo rodent model. We stimulated the ventral hippocampal commissure while recording in the hippocampus. The Ho-Kalman algorithm was again used to build a dynamical systems model of the LFP activity based on the evoked response to Gaussian white noise stimulation. Our results show a significant decrease in LFP power during closed-loop stimulation. Open-loop stimulation produced negligible change in LFP power. The LQG controller was confirmed to be an effective tool for minimizing LFP activity within a selected frequency band.

    The mathematical models of neural dynamics it uses are subject specific and determine stimulation waveforms based on state to suppress neural activity. Brain , pt. Deep brain stimulation DBS is an effective therapy for motor symptoms of PD, and is often used as a complement to medication in patients who have progressed to severe stages of PD.

    However, programming these devices is difficult and time consuming, and DBS therapy is limited by side effects and partial efficacy [1]. Current cDBS strategies are incapable of adapting to the needs of patients: once the clinician sets the parameters, they do not change until the next programming visit. In this study, we have created a reinforcement learning RL algorithm capable of learning online how best to stimulate to reduce pathological oscillations in silico. The RL-DBS algorithm decides when to deliver stimulus pulses based upon the real-time amplitude and phase of the pathological oscillation in order to reduce the amplitude of that oscillation.

    The algorithm learns which actions lead to the highest cumulative reward i. After training on the model, the RL-DBS algorithm is able to learn both phase and amplitude selectivity to optimally reduce the pathological oscillation. The algorithm then decides which action to execute based upon the action difference Figure. Additionally, the algorithm learns to deliver bursts of stimulation phase-locked to the oscillation.

    We created an adaptive RL-DBS algorithm capable of learning on-line how to reduce the power of a pathological oscillation in a computation model of PD. The algorithm has the potential to deliver individualized, adaptive DBS therapy that can improve the quality of life for PD patients. Learned reward maps A, B and action difference C as a function of the phase and amplitude of the oscillation. A and B show the learned reward for no stimulation and stimulation respectively, while C shows the action difference.

    The algorithm selects the action that with the highest expected reward. The action difference reveals that the algorithm learns both phase- and amplitude-selective stimulation. Deuschl, S. Paschen, and K. Lanciego, J. Artieda, N. Gonzalo, and C. Trends Neurosci. S8—S19, Mean-field modeling of the basal ganglia-thalamocortical system. I Firing rates in healthy and parkinsonian states. Detecting delayed coupling in dynamical systems remains a challenging frontier in Neuroscience. Frequently used tools such as cross-correlation have been shown to be robust against measurement noise but fail to identify coupling direction.

    With widespread use of these tools, it is important to have a complete understanding of the limitations of each metric and the circumstances of optimal use in experimental design. To test these metrics over a salient parameter space, a linear, delayed vector autoregressive model was created with probabilistic and complex coupling over probabilistic time delays.

    The model was run with various measurement noise strengths, numbers of nodes, and number of available data points. Correlation, cross-correlation, mutual information, multivariate granger causality MVGC , and transfer entropy TE were computed and compared to true coupling adjacency matrices using an L-2 metric. Significant differences were found between reconstruction results between metrics. MVGC was found to outperform all other metrics when the signal to noise ratio exceeded 0.

    Similarly, MVGC and TE required a minimum number of samples to converge, and the required number of points was found to be a function of the number of nodes. Reconstruction error of time-lagged coupling as a function of measurement noise with standard deviations. Conclusions: Based on this work, significant disparity exists between the performance of existing methods to detect delayed coupling.

    Many common tools fail to detect delayed coupling. However, even with a minimal density of time points to number of nodes, MVGC efficiently recovers complex and delayed coupling. Careful consideration should be given to metrics used in experiments where coupling may be delayed or spread out over time. Measurement noise and data sample density requirements may affect experimental design. Barnett L, Seth AK. Elsevier; ; — BMC Neurosci. BioMed Central Ltd; ; Granger causality and transfer entropy Are equivalent for Gaussian variables.

    A common property of developing neuronal systems is their intrinsic ability to generate spatiotemporally propagating spiking activity involving a large number of highly synchronously firing neurons. Primary neuronal cultures are among the experimental preparations that allow the investigation of the principles underlying the generation of such spontaneous coordinated spiking activity: cell cultures self-organize during development up to the stage where they elicit stereotyped network-wide spiking activity, called network bursts.

    The high spatial resolution of the high-density CMOS multi-electrode arrays revealed that network bursts correspond to a coordinated propagation of action potentials throughout the network [1]. Specifically, these propagations could be well clustered into few groups differing for their ignition sites i.

    This finding suggests the presence of regions in charge of triggering such spontaneous events. Following this direction, we investigated what were the main determinants underlying the generation of network bursts in cell cultures at the mature stage. With minimal topological constraints on the coupling between neuronal pairs i. The results obtained in this study, by combining experimental datasets with our neural network computational model, shows that while the synaptic contribution is mainly involved in shaping the network burst, the key player in the generation of network bursts could be found in the local properties of the neuronal network.

    In particular, in the model, the neurons of to the hot spots were much more responsive than any other region to mild stimulations delivered to these regions. Lab Chip , 9 18 : — J Neural Eng , 7 5 : Over the last decade graphics processing units GPUs have evolved into powerful, massively parallel co-processors that are increasingly used for scientific computing and machine learning.

    As a consequence, frameworks are being developed for making GPU acceleration available for specific applications without complex parallel code design. Brian2GeNN supports all common features of Brian 2 with few exceptions such as multi-compartment models, multiple networks or heterogeneous delays. On this poster, we present the basic principles of how Brian2GeNN works and benchmark examples of its performance with a number of different benchmark models and using a number of diverse GPU accelerators.

    We can demonstrate that depending on the model and the accelerator, achieved speedups can vary considerably. Yavuz, J. Turner and T. Nowotny GeNN: a code generation framework for accelerated brain simulations. Scientific Reports , 6 In visual discrimination tasks, the subject collects information about sensory stimuli and makes behavioral decisions accordingly. In this study, we are searching for coding strategies in visual cortices of the macaque macaca mulatta that relate to both stimuli and behavior.

    Multi-units within a single cortical column are recorded in V1 and V4 areas simultaneously while the subject is performing a change detection task with matching and non-matching stimuli. We assess systematic differences in distribution of spike counts for matching vs. In addition, we estimate pair-wise correlations of spike counts.

    ISBN 13: 9781908419293

    The spiking signal is weakly but significantly predictive on the type of stimulus matching vs. In both areas, the effect is limited to the superficial layers of the cortical column. In incorrect trials, neural activity in V1 is in addition characterized by a systematic bias in spike counts already at the beginning of the trial. The bias is consistent with the future behavioral choice and is only present in the deep cortical layers. Comparing the distribution of correlation coefficients across pairs of neurons with matching and non-matching stimuli, distribution of coefficients in V4 is less variable with matching stimuli, in particular for short This effect could be interpreted as a fast adaptation of neural responses to two consecutive presentations of the same stimuli [2].

    In V1, we did not observe any systematic changes in spike-count correlations with different stimuli. However, correlations are significantly more variable in trials with incorrect compared to correct behavioral performance. This effect is once again limited to deep cortical layers. Higher variability of correlations in V1 might be a signature of spontaneously generated network state that is more likely leading to incorrect behavioral performance. Finally, we test the interactions between choice probabilities and spike-count correlations.

    Choice probabilities and correlations do not interact in V1, but weakly interact in the V4 area, where cells with similar choice probabilities tend to be more strongly correlated. In summary, we observe various differences in the first and second order statistics of spike counts in both V1 and V4 areas. The first order statistics is related to coding of both stimuli and behavioral choices while correlations would rather modulate the efficacy of encoded signals. Visual Neurosci , 13 1 : 87— Nature , : — Neuron , 76 3 : — Nienborg H, Cumming BG: Decision-related activity in sensory neurons may depend on the columnar architecture of cerebral cortex.

    The interplay between structural connectivity SC and neural dynamics is still not yet fully understood. Applying topological analysis, the connectome approach links this anatomical network to brain function. Here we adopt a computational approach to find topology features related to the stability on global neural dynamics. A previous study of a mean field model based on the human cortex network, shows at least 2 global neural states, with either a low or high firing rate pattern [1, 3].

    Also, at this bistable range, this model achieves the highest correlations with empirical resting state fMRI data. How the network connectivity pattern shapes the critical G values has not been yet investigated. Our aim is to identify local or global topology features related to the critical G values. For each of the analyzed networks, values in their critical G points have small or null variability. Then, we selectively prune the edges of the networks and calculate their critical G values to show the effect of structure pattern in maintaining the bistable dynamics.

    The edges were pruned selectively based on either the degree or the k core decomposition measure; interpreted as a local or global topology feature, respectively.

    Saturday 21 April 2018: Screening Room 1, The Soho Hotel, 4 Richmond Mews, London, W1D 3DH

    The highest shifts in critical G values are achieved when the edges of high degree or k core nodes are pruned. In contrast, when we prune those edges belong to low degree or no k core nodes, the shifts in the critical G points are irrelevant. We interpret this as that the model can use either local or global connectivity pattern in order to stabilize the critical G points. Furthermore, our study show that shifts in the critical G points are statistically equivalent when the degree distribution but not k core structure is shared, such as in the binary human SC compared to the RN.

    Therefore, in our simulation the degree distribution, interpreted as a local connectivity feature, determines the critical G points for bistability, capturing the essential structural pattern of the network. We also show that it is possible to obtain bistability in other types of networks, suggesting that structure dynamic relationships may obey a topological principle.

    SC is recipient of a Ph. J Neurosci. PLoS Biol. Chaotic dynamics of neural oscillations has been shown at the single neuron and network levels, both in experimental data and numerical simulations. Theoretical works suggest that chaotic dynamics enrich the behavior of neural systems, by providing multiple attractors in a system.

    However, the contribution of chaotic neural oscillators to relevant network behavior has not been systematically studied yet. We investigated the synchronization of neural networks composed of conductance-based neural models that display subthreshold oscillations with regular and burst firing [1]. In this model, oscillations are driven by a combination of persistent Sodium current, a hyperpolarization-activated current Ih and a calcium-activated potassium current, very common currents in the CNS.

    By small changes in conductance densities, the model can be turned into either chaotic or non-chaotic modes [2]. We study synchronization of heterogeneous networks where conductance densities are drawn from either chaotic or non-chaotic regions of the parameter space. Measuring mean phase synchronization in a small-world network with electrical synapses, we characterize the transition from unsynchronized to synchronized state as the connectivity strength is increased.

    First, we draw densities from fixed-size regions of the parameter space and find the transition to synchronized oscillations is always smooth for chaotic oscillators but not always smooth for the nonchaotic ones. However, non-smooth transitions were found to be associated to a change in firing pattern from tonic to bursting. Nevertheless, we noticed that chaotic oscillators display a wider distribution of firing frequencies than non-chaotic oscillators, thus making more heterogeneous networks.

    Next, we draw the conductance densities from the parameter space in a way that maintained the same distribution of firing frequencies hence the heterogeneity of the network for both chaotic and non-chaotic. In this case, synchronization curves are very similar, being second order phase transition for both cases. However, we cannot discard that non-chaotic oscillators become chaotic or vice versa when in a network, because of the extra parameter associated to the electrical synapse.

    During the late s and early s, Medalla made a series of 'participation works' where he encouraged audience involvement in the production of pieces to challenge notions of creative hierarchy. In , the piece will be exhibited at the Venice Biennale. Tompkins is a groundbreaking, feminist artist who has, over the course of her career, repeatedly bucked conventional norms, eschewing 'safe' art in favor of creating difficult and impactful paintings, drawings, photographs, and videos.

    Taking as her starting point heterosexual pornography, Tompkins reclaims the medium, taking a genre typically conceived as a vehicle for subverting women, and uses it to create direct, powerful, and empowering works that are distinctly feminist. Appropriating pornographic imagery, Tompkins recasts it through an artistic lens and creates masterfully crafted paintings that assume control of and reframe the subject matter.

    Variant | issue 9

    The centerpiece of the exhibition at Gavlak will be WOMEN Words, Phrases, and Stories an installation of 1, paintings of words that Tompkins received in response to an email request for words that describe women. Tompkins received more than 3, responses to her initial query, with the most common words sent in response being cunt, bitch, slut and mother.

    Beginning in , Tompkins selected 1, of the words and began to paint them on canvas, depicting some simply as text, while incorporating imagery from her earlier works into the background, or gestural references to what Tompkins describes as "old-boy painters," like de Kooning and Rothko, into others.

    Tompkins then installed the paintings in a confined yet fluid network, creating conversations between them, some humorous, some disturbing. In one arrangement, Tompkins placed the phrase "Liberated women," and underneath hung "Talking, talking, talking," followed by "Will she ever shut up?

    In other less lighthearted areas of the installation, more overtly misogynistic words form groupings: "The only thing that could make her more beautiful is my dick in her mouth," "Three hole wonder," or "Prick pit. The works span from the early s to , highlighting the way in which she has continued to approach similar subject matter anew, though with an ever-evolving formal approach. Taken together the works offer a bold look at the state of feminism today, a reminder that while women's rights have advanced, there is still a long road ahead towards equality.

    Born in in Washington, D. Tompkins' work is currently part of the permanent collection at the Centre Pompidou, Paris, France. For more information concerning the exhibition, please contact Tabor Story at tabor gavlakgallery. The Wall Street Journal U. Heiss's upstart enterprise since its merger with the Museum of Modern Art-a 40th-anniversary show assembles work by artists associated with the outpost's founding in The inaugural exhibition "Rooms" transformed an abandoned school building in Long Island City into a series of unusual gallery spaces, with artwork in former classrooms, hallways, playgrounds and a boiler room down below.

    Now, the retrospective "Forty" presents artists from that original show or otherwise affiliated with Ms. Heiss's early organizational experiments. In the early s, Ms. Heiss had emerged as a kind of renegade leader of New York's burgeoning "alternative space" movement, founding an organization called the Institute of Art and Urban Resources to help find studio, exhibition and performance space for the city's artists. In addition to the abandoned Queens school, Ms.

    Heiss reimagined many unused and overlooked city spaces, launching the Clocktower Gallery, the Idea Warehouse and the Coney Island Sculpture Factory, among others. It had no collection, no gift shop and no allegiance to art of historically sanctified or market-sanctioned kinds.

    Morning visitation was avoided in favor of more amenable times at night-"artist's hours," Ms. Heiss said. Richard "Dickie" Landry, who has four minimalist drawings in the "Forty" show, knew Ms. Heiss at the start of her career. Heiss recalled a performance at her Clocktower Gallery featuring the musician and artistic muse Charlotte Moorman, who played the cello while topless and covered in molten chocolate. Heiss seeded early exhibits like "Rooms" with artists who were then barely known.

    Among the 42 artists in the show, on view through Aug. Others represent a historical milieu devoted to questioning the art world's formalities and precious airs. Heiss made] was getting rid of the pedestal and those aspects that made art important by giving it prestige," said Richard Nonas, an year-old artist included in "Forty.

    Nonas's large steel sculpture "Alligator" lies on the floor of a second-floor gallery, in the same spot it occupied in "Rooms," though the setting has evolved. In , the walls of the derelict building were raw and falling down. Now, they are museum-grade white and cooled by air conditioning. Up a creaky set of wooden stairs, an immersive installation by the artist Colette transforms a spacious attic into a sort of stage set for a modern costume drama.

    Friday 20 April 2018: Courthouse Cinema, 19-21 Great Marlborough Street, London, W1F 7HL

    Heiss said of mirrored glass and fabric draped all around. Heiss stepped down from her directorship in Biesenbach said.

    Heiss, now 73 years old and busy with roaming exhibitions and projects supported by her nonprofit organization Clocktower Productions, said her return to the institution she founded was welcome for the chance to reacquaint herself with artists from a generation she helped shape. Landmarks, the public art program of The University of Texas at Austin, acquires major works by Marc Quinn and Ann Hamilton Landmarks' collection expands with the acquisition of a monumental sculpture by Marc Quinn and a newly commissioned project by Ann Hamilton.

    Both works will be installed at the university's Dell Medical School and are funded through a percent-for-art allocation that sets aside one-to-two percent of capital improvement projects for the acquisition of public art. Both wrestle with bigger ideas about the human form and healing, making them ideally suited for the new Dell Medical School. Marc Quinn's artistic practice is preoccupied with the mutability of the body and the dualisms that define human life: spiritual and physical, surface and depth, cerebral and sexual. Spiral of the Galaxy, Quinn's seven-ton bronze sculpture, was first shown at an exhibition of the artist's work in at the Giorgio Cini Foundation in Venice, Italy.

    Landmarks' acquisition was cast as the artist's proof alongside an edition of three, and it will be the only example of the piece in the United States. Placed at the gateway to the Dell Medical School, the monumental sculpture depicts an elegant conch shell.

    The conch carries cultural and religious significance, and among many interpretations can be construed here as a complex structure that protects delicate organisms. Ann Hamilton engaged in a three-part residency for Landmarks to create portraits of local community members. Her images evoke the human form, touch, and the care and attention of healers. During each residency, she photographed volunteers through a semi-transparent membrane that renders in focus only what touches the surface and softly blurs the gestures and outlines of the sitters.

    The optical quality of the material renders touch-something felt more than seen-visible. The life of every citizen will intersect with the health care system and these portraits include caregivers, faculty, students, staff, community partners, civic leaders and patients themselves. Hamilton will select around two dozen portraits to install in the new Center for Health Learning and Center for Health Discovery buildings in early Her library of approximately subjects may be used in future buildings of the Dell Medical School as well as in other graphic applications, including a book that contains images of each participant.

    Public art that starts conversations and inspires creativity and community connections is vital to the environment we envision. An ongoing percent-for-art allocation ensures the collection will develop in tandem with the rapid expansion of the campus. With these upcoming additions, Landmarks continues to advance its mission to present iconic works of art that convey the university's ideals. Beyond its aesthetic value, the group demonstrates significant art historical trends from the second half of the 20th century.

    The collection also fosters learning through its conservation efforts. Landmarks provides technical training for student volunteers who preserve the sculptures, the only known program of its kind in the United States. Marc Quinn was born in London in He graduated from Cambridge University with a degree in history and history of art, and subsequently worked as an assistant to the sculptor Barry Flanagan.

    He is one of the leading artists of his generation, creating sculptures, paintings, and drawings that explore the dynamic between art and science, and the human body relative to perceptions of beauty. Other key subjects include cycles of growth and evolution through topics such as genetics and the manipulation of DNA, as well as issues of life, death, and identity.

    Quinn's work uses a broad range of materials, both traditional and unorthodox. The materiality of the object, in both its elemental composition and surface appearance, is at the heart of Quinn's work. Ann Hamilton was born in Lima, Ohio, in Ann Hamilton is a visual artist internationally recognized for the sensory surrounds of her large-scale multimedia installations.

    Using time as process and material, her methods serve as an invocation of place, of collective voice, of communities past and of labor present. Her ephemeral environments create immersive experiences that poetically respond to the architectural presence and social history of their sites. Get out the heat We are so excited and what to share our happiness Join us for an evening of laughter! Garvey Simon announces new lenticular collages by Karen Shaw Shaw's Atmospheric Disturbances series rhymes with glitch art, a movement in contemporary art that aestheticizes glitches, or errors, in digital technologies.

    Karen's images shine with jewel-like colors, while defying their two-dimensional medium by revealing two or more views as the observer moves. The collages call to mind a once-imagined cyberpunk future that is becoming increasingly real today. We just received new works at the gallery and they are available for private viewings. For inquiries contact us: liz garveysimon. We'd love to see you soon at the Gallery! Project Anywhere v. I invite you to help me explore this very basic act which unites us: breathing. Hope to see you at this exploration of the art of the everyday at a wonderfully dynamic set of creative community-engaged projects in Jamaica, Queens.

    Best wishes, Priscilla Overview For my project I have a hard time embracing the word performance I want to explore a basic act that unites us all: breathing. And to use the act of breathing with someone else as a subtle invitation for us to slow down a bit and to connect. I also want to encourage people to remember to pause and take a deep breath as they live their often stressful lives. Background An interaction has evolved at my job that I would like to bring to the people of Jamaica. It is very simple. Taking a deep breath with someone.

    Whenever we see each other around the college, we stop what we are doing, pause, and both of us take a deep breath. Just by seeing each other, we remind each other to take a deep breath. And we try to encourage others to do this. In Jamaica I would like to invite people to take a deep breath with me. I think it may be an opportunity for connection.

    More About the Project I like the idea of working near the exhibition space to give more context and have it related to the show. Also I may make some related objects such as simple talismans I can give people to carry with them as a reminder to breath in their everyday life, and a sign or something visible to communicate something about the action and invite people to consider participating.

    Phrases for a sign are rolling around in my mind: breathe with me, take a nice slow breath, help yourself to some oxygen, etc. Still thinking about that. I may also offer to give those who participate a small talisman to help them remember to pause and breathe during their daily lives in the future. Inspired by proposing this project, I will consciously remind myself to slow down and take a deep breath throughout the planning and implementation process.

    I consider this awareness to be part of the project and am very grateful for Best wishes,. The umbrella is a symbol of protection and resistance. This performance seeks an intersection where aesthetics and politics ignite each other, exploring how symbolic and situational behaviors impact on our perception in regards to specific social movements and activism.

    It is relevant to open conversations about how to transform social and political landscapes through embodied gestures, examining relationships between the citizens and the place they live, between what they have lost and what they have gained in social political transformations. Chun Hua Catherine Dong, born in China, is a visual artist working with performance, photography, and video. She received a M. Among many other awards and grants, she is the recipient of Franklin Furnace Award for contemporary avant-garde art in New York in Dong now lives in Montreal.

    Dorian Grey Gallery with Martino Auto Concepts are excited to announce the second family friendly gathering of exotic cars in the Hamptons. Please RSVP to join us as parking will be limited. We are in the middle of a tremendous amount of grief and anger over the rash of violence, police killing of black people, sniper killing police in Dallas, killings of LGBT community in Orlando. All of the hate, revenge, murder and dismissal of the value of human life is something we cannot be silent about.

    For the past 15 years, we have been working on multimedia projects that seek to build bridges across race, culture, ethnicity, class, gender and age and defy stereotypes. We use our projects to open up dialogue, entertain, and bring moments of humanity, solace, grief, joy and celebration to the stage, books, radio, and web.

    We invite you to join us at whatever events you can attend, participate in post-show discussions, and continue to work toward a more peaceful world. In the tradition of the playwright Anna Deavere Smith, Sloan performs "Crossing the BLVD" adopting the personae and respectfully mimicking the accents of the varied immigrants.

    That's quite a rep to live up to. Don't worry about it. Worry, instead, about the possibility of missing his 2 programs, he's not planning to live forever no matter how many attractive offers he gets along those lines. Don't expect 'beautiful' hi-def movies lacking content but abounding in budgets. Expect movies so packed with conceptual loudness that you'll be hi-def-ened without having a single voice raised. One could say that these are a bit off - as in he bit off more than he could eschew.

    He also died This will explain. He has served as Abrons's director since , during which time much of the center's adventurous, increasingly international programming has been credited to him. But after 10 years, Mr. Wegman said, it was time to move on. His successor will be chosen in the fall.

    The coming season at Abrons, which begins on Sept. It's a wonderful season to exit with. Wegman is looking forward to the drag performer Dickie Beau's "Blackouts," which has its American premiere on Oct. The show, which blends the spectacle of drag with the sadness of clowns, explores Dickie Beau's fascination with female icons like Marilyn Monroe and Judy Garland. Wegman also singled out "Good Samaritans" Feb. At Skirball, Mr. Wegman said, he plans to build out the theater's international programming by taking advantage of connections N. He also wanted to see what he could do to "embrace Broadway.

    It looks like something you might pick up in any well-stocked toy store.