Network Response Research Articles

A unifying perspective on neuromodulatory effects on signal transmission and plasticity in D1-dominant MSN neurons

How could phasic variations in dopamine level affect the learning outcome of a spiking neural network? How may neuromodulation affect the network's instantaneous response to simultaneously arriving glutamatergic inputs? How may this depend on the brain regions involved? In our spiking phenomenological model for signal transmission across the synapse and along the dendritic tree, we propose a new approach for the influence of dopamine-like neuromodulators on the ascribed aspects, which unifies diverging views on its role in (reinforcement) learning and (attentional) contrast. We call into question the common practice of simulating dopaminergic influence on an STDP rule as a third factor, and instead show how an instantaneous effect of a dopamine-like neuromodulator on postsynaptic activity can also lead to reinforced learning outcomes. As the phasic change of neuromodulator needs to be present during glutamatergic transmission in our model, we do not account for delayed reward as stated in the distal reward problem. Instead, we assume an involvement of hippocampus and cortical working memory for long delays of reward. However, as our transmission-based model does not interfere with the standard two-factor STDP rule, it may be freely combined with existing extensions to STDP if needed.

Network Identification via Node Knockout

This technical note examines the problem of identifying the interaction geometry among a known number of agents, adopting a (weighted) consensus-type algorithm for their coordination. Inspired by how biologists use gene knockouts for experimentally identifying genetic interaction networks in cellular organisms, we propose a node-knockout procedure for the complete characterization of the interaction geometry in consensus-type networks. In our context, the node knockout is essentially a grounding procedure- where the node broadcasts a zero state to its neighbors without being removed from the network. The proposed centralized identification process is also facilitated by introducing “ports” for stimulating a subset of network vertices via an appropriately defined interface and observing the network's response at another set of vertices. We then provide an example for the utility of such a network identification process in the context of fault detection for networked systems.

S4-1: Motion Detection Based on Recurrent Network Dynamics

The detection of a sequence of events requires memory. The detection of visual motion is a well-studied example; there the memory allows the comparison of current with earlier visual input. This comparison results in an estimate of direction and speed of motion. The dominant model of motion detection in primates—the motion energy model—assumes that this memory resides in subclasses of cells with slower temporal dynamics. It is not clear, however, how such slow dynamics could arise. We used extracellularly recorded responses of neurons in the macaque middle temporal area to train an artificial neural network with recurrent connectivity. The trained network successfully reproduced the population response, and had many properties also found in the visual cortex (e.g., Gabor-like receptive fields, a hierarchy of simple and complex cells, motion opponency). When probed with reverse-correlation methods, the network's response was very similar to that of a feed-forward motion energy model, even though recurrent ...

The phase response of the cortical slow oscillation.

Cortical slow oscillations occur in the mammalian brain during deep sleep and have been shown to contribute to memory consolidation, an effect that can be enhanced by electrical stimulation. As the precise underlying working mechanisms are not known it is desired to develop and analyze computational models of slow oscillations and to study the response to electrical stimuli. In this paper we employ the conductance based model of Compte etal. (J Neurophysiol 89:2707-2725, 2003) to study the effect of electrical stimulation. The population response to electrical stimulation depends on the timing of the stimulus with respect to the state of the slow oscillation. First, we reproduce the experimental results of electrical stimulation in ferret brain slices by Shu etal. (Nature 423:288-293, 2003) from the conductance based model. We then numerically obtain the phase response curve for the conductance based network model to quantify the network's response to weak stimuli. Our results agree with experiments in vivo and in vitro that show that sensitivity to stimulation is weaker in the up than in the down state. However, we also find that within the up state stimulation leads to a shortening of the up state, or phase advance, whereas during the up-down transition a prolongation of up states is possible, resulting in a phase delay. Finally, we compute the phase response curve for the simple mean-field model by Ngo etal. (EPL Europhys Lett 89:68002, 2010) and find that the qualitative shape of the PRC is preserved, despite its different mechanism for the generation of slow oscillations.

A chemokine receptor CXCR7 non-cell-autonomously controls migration and nuclei formation of pontine neurons from the migratory environment

Mammary gland development is coupled to reproductive events by hormonal cues of ovarian and pituitary origin, which activate a genomic regulatory network. Identification of the components and regulatory links that comprise this network will provide the basis for defining the network's dynamic response during normal development and its perturbation during breast carcinogenesis. In this study KIBRA was identified as a transcript showing decreased expression associated with failed mammary gland development in Prlr knockout mammary epithelium. It is strongly up-regulated during pregnancy, falls during lactation and is again up-regulated during involution of the gland at weaning. A bioinformatic approach was undertaken to identify potential binding partners which interact with the WW domains of KIBRA. We show that KIBRA binds to a WW domain binding motif, PPxY, in the tyrosine kinase receptor DDR1, and dissociates upon treatment with the DDR1 ligands collagen type I or IV. In addition we show that KIBRA and DDR1 also interact with PKCz to form a trimeric complex. Finally, overexpression and knockdown studies demonstrate that KIBRA promotes the collagen-stimulated activation of the MAPK cascade. Thus KIBRA may play a role in how the reproductive state influences the mammary epithelial cell to respond to changing cell-context information, such as experienced during the tissue remodeling events of mammary gland development.

Load Modulation of BOLD Response and Connectivity Predicts Working Memory Performance in Younger and Older Adults

Individual differences in working memory (WM) performance have rarely been related to individual differences in the functional responsivity of the WM brain network. By neglecting person-to-person variation, comparisons of network activity between younger and older adults using functional imaging techniques often confound differences in activity with age trends in WM performance. Using functional magnetic resonance imaging, we investigated the relations among WM performance, neural activity in the WM network, and adult age using a parametric letter n-back task in 30 younger adults (21-31 years) and 30 older adults (60-71 years). Individual differences in the WM network's responsivity to increasing task difficulty were related to WM performance, with a more responsive BOLD signal predicting greater WM proficiency. Furthermore, individuals with higher WM performance showed greater change in connectivity between left dorsolateral prefrontal cortex and left premotor cortex across load. We conclude that a more responsive WM network contributes to higher WM performance, regardless of adult age. Our results support the notion that individual differences in WM performance are important to consider when studying the WM network, particularly in age-comparative studies.

Plasticity and stability in recurrent neural networks

Over the past 30 years, Bienenstock-Cooper-Munro (BCM) [1] type learning rules have shaped our understanding of synaptic plasticity. While they excel at explaining the emergence of receptive fields and stimulus selectivity in networks with feed-forward architecture, their impact on recurrent scenarios is less distinctive. Here, we analyze general BCM-type synaptic plasticity rules with a homeostatic sliding threshold in the framework of recurrent networks of rate-based and spiking neurons. We begin by considering the effects of learning rate and homeostatic timescales on network stability in a non-linear firing rate model. We show how a sensible choice of timescales leads to stable weight dynamics, but other seemingly sensible parameter choices will inevitably lead to catastrophic run-away potentiation. We discuss under which conditions a stable fixed-point in a regime of Hebbian learning exists. We then study the network's response to perturbations and quantify the critical point whereupon network stability is compromised. By viewing perturbation as a consequence of pattern storage in synaptic connections, we quantify the number of such patterns that can be learned safely in a given time. Our model could provide simple explanations as to why memory intake capacity is limited and why learning becomes increasingly inefficient during intensive learning periods. We confirm these findings in numerical simulations of spiking neural networks and show that our analytical results apply to synapses subject to additive triplet-STDP [2].

Considerations for using integral feedback control to construct a perfectly adapting synthetic gene network

It has long been known to control theorists and engineers that integral feedback control leads to, and is necessary for, “perfect” adaptation to step input perturbations in most systems. Consequently, implementation of this robust control strategy in a synthetic gene network is an attractive prospect. However, the nature of genetic regulatory networks (density-dependent kinetics and molecular signals that easily reach saturation) implies that the design and construction of such a device is not straightforward. In this study, we propose a generic two-promoter genetic regulatory network for the purpose of exhibiting perfect adaptation; our treatment highlights the challenges inherent in the implementation of a genetic integral controller. We also present a numerical case study for a specific realization of this two-promoter network, “constructed” using commonly available parts from the bacterium Escherichia coli. We illustrate the possibility of optimizing this network's transient response via analogy to a linear, free-damped harmonic oscillator. Finally, we discuss extensions of this two-promoter network to a proportional-integral controller and to a three-promoter network capable of perfect adaptation under conditions where first-order protein removal effects would otherwise disrupt the adaptation.

Homeostasis and evolution together dealing with novelties and managing disruptions

PurposeThe purpose of this paper is to present an artificial homeostatic system whose parameters are defined by means of an evolutionary process. The objective is to design a more biologically plausible system inspired by homeostatic regulations observed in nature, which is capable of exploring key issues in the context of robot behaviour adaptation and coordination.Design/methodology/approachThe proposed system consists of an artificial endocrine system that coordinates two spatially unconstrained GasNet artificial neural network models, called non‐spatial GasNets. Both systems are dedicated to the definition of control actions in autonomous navigation tasks via the use of an artificial hormone and a hormone receptor. A series of experiments are performed in a real and simulated scenario in order to investigate the performance of the system and its robustness to novel environmental conditions and internal sensory disruptions.FindingsThe designed system shows to be robust enough to self‐adapt to a wider variety of disruptions and novel environments by making full use of its in‐built homeostatic mechanisms. The system is also successfully tested on a real robot, indicating the viability of the proposed method for coping with the reality gap, a well‐known issue for the evolutionary robotics community.Originality/valueThe proposed framework is inspired by the homeostatic regulations and gaseous neuro‐modulation that are intrinsic to the human body. The incorporation of an artificial hormone receptor stands for the novelty of this paper. This hormone receptor proves to be vital to control the network's response to the signalling promoted by the presence of the artificial hormone. It is envisaged that the proposed framework is a step forward in the design of a generic model for coordinating many and more complex behaviours in simulated and real robots, employing multiple hormones and potentially coping with further severe disruptions.

Spontaneous Local Gamma Oscillation Selectively Enhances Neural Network Responsiveness

Synchronized oscillation is very commonly observed in many neuronal systems and might play an important role in the response properties of the system. We have studied how the spontaneous oscillatory activity affects the responsiveness of a neuronal network, using a neural network model of the visual cortex built from Hodgkin-Huxley type excitatory (E-) and inhibitory (I-) neurons. When the isotropic local E-I and I-E synaptic connections were sufficiently strong, the network commonly generated gamma frequency oscillatory firing patterns in response to random feed-forward (FF) input spikes. This spontaneous oscillatory network activity injects a periodic local current that could amplify a weak synaptic input and enhance the network's responsiveness. When E-E connections were added, we found that the strength of oscillation can be modulated by varying the FF input strength without any changes in single neuron properties or interneuron connectivity. The response modulation is proportional to the oscillation strength, which leads to self-regulation such that the cortical network selectively amplifies various FF inputs according to its strength, without requiring any adaptation mechanism. We show that this selective cortical amplification is controlled by E-E cell interactions. We also found that this response amplification is spatially localized, which suggests that the responsiveness modulation may also be spatially selective. This suggests a generalized mechanism by which neural oscillatory activity can enhance the selectivity of a neural network to FF inputs.

Dynamics and Design Principles of a Basic Regulatory Architecture Controlling Metabolic Pathways

The dynamic features of a genetic network's response to environmental fluctuations represent essential functional specifications and thus may constrain the possible choices of network architecture and kinetic parameters. To explore the connection between dynamics and network design, we have analyzed a general regulatory architecture that is commonly found in many metabolic pathways. Such architecture is characterized by a dual control mechanism, with end product feedback inhibition and transcriptional regulation mediated by an intermediate metabolite. As a case study, we measured with high temporal resolution the induction profiles of the enzymes in the leucine biosynthetic pathway in response to leucine depletion, using an automated system for monitoring protein expression levels in single cells. All the genes in the pathway are known to be coregulated by the same transcription factors, but we observed drastically different dynamic responses for enzymes upstream and immediately downstream of the key control point—the intermediate metabolite α-isopropylmalate (αIPM), which couples metabolic activity to transcriptional regulation. Analysis based on genetic perturbations suggests that the observed dynamics are due to differential regulation by the leucine branch-specific transcription factor Leu3, and that the downstream enzymes are strictly controlled and highly expressed only when αIPM is available. These observations allow us to build a simplified mathematical model that accounts for the observed dynamics and can correctly predict the pathway's response to new perturbations. Our model also suggests that transient dynamics and steady state can be separately tuned and that the high induction levels of the downstream enzymes are necessary for fast leucine recovery. It is likely that principles emerging from this work can reveal how gene regulation has evolved to optimize performance in other metabolic pathways with similar architecture.

KIBRA interacts with discoidin domain receptor 1 to modulate collagen-induced signalling

Mammary gland development is coupled to reproductive events by hormonal cues of ovarian and pituitary origin, which activate a genomic regulatory network. Identification of the components and regulatory links that comprise this network will provide the basis for defining the network's dynamic response during normal development and its perturbation during breast carcinogenesis. In this study KIBRA was identified as a transcript showing decreased expression associated with failed mammary gland development in Prlr knockout mammary epithelium. It is strongly up-regulated during pregnancy, falls during lactation and is again up-regulated during involution of the gland at weaning. A bioinformatic approach was undertaken to identify potential binding partners which interact with the WW domains of KIBRA. We show that KIBRA binds to a WW domain binding motif, PPxY, in the tyrosine kinase receptor DDR1, and dissociates upon treatment with the DDR1 ligands collagen type I or IV. In addition we show that KIBRA and DDR1 also interact with PKCz to form a trimeric complex. Finally, overexpression and knockdown studies demonstrate that KIBRA promotes the collagen-stimulated activation of the MAPK cascade. Thus KIBRA may play a role in how the reproductive state influences the mammary epithelial cell to respond to changing cell-context information, such as experienced during the tissue remodeling events of mammary gland development.

Individual Differences in Sentence Comprehension: A Functional Magnetic Resonance Imaging Investigation of Syntactic and Lexical Processing Demands

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network's properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network's response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance.

Response times in M/M/s fork-join networks

We study a fork-join processing network in which jobs arrive according to a Poisson process and each job splits into m tasks, which are simultaneously assigned to m nodes that operate like M/M/s queueing systems. When all of its tasks are finished, the job is completed. The main result is a closed-form formula for approximating the distribution of the network's response time (the time to complete a job) in equilibrium. We also present an analogous approximation for the distribution of the equilibrium queue length (the number of jobs in the system), when each node has one server. Kolmogorov-Smirnov statistical tests show that these formulae are good fits for the distributions obtained from simulations.

Response times in M/M/s fork-join networks

We study a fork-join processing network in which jobs arrive according to a Poisson process and each job splits into m tasks, which are simultaneously assigned to m nodes that operate like M/M/s queueing systems. When all of its tasks are finished, the job is completed. The main result is a closed-form formula for approximating the distribution of the network's response time (the time to complete a job) in equilibrium. We also present an analogous approximation for the distribution of the equilibrium queue length (the number of jobs in the system), when each node has one server. Kolmogorov-Smirnov statistical tests show that these formulae are good fits for the distributions obtained from simulations.

Linking ecological science to decision-making: delivering environmental monitoring information as societal feedback.

The paper describes the Ecological Monitoring and Assessment Network's (EMAN) operational and program response to certain challenges of environmental monitoring in Canada, in particular, efforts to improve the ability of the network to deliver relevant information to decision makers. In addition to its familiar roles, environmental monitoring should deliver feedback to society on environmental changes associated with development patterns, trends, processes and interventions. In order for such feedback to be effective, it must be relevant, timely, useful and accessible: all characteristics that are defined by the user, not the provider. Demand driven environmental monitoring is explored through EMAN's experiences with Canada's Biosphere Reserves, the NatureWatch Program and the Canadian Community Monitoring Network.

Performance evaluation of QoS-routing methods for IP-based multiservice networks

This paper provides a performance analysis of lost/delayed traffic and control load for various quality of service (QoS)-routing methods, which control a network's response to traffic demands and other stimuli, such as traffic overloads, link failures, or node failures. Essentially all of the methods analyzed are already widely applied in operational networks worldwide, particularly in PSTN networks employing TDM-based technology. However, the methods are shown to be extensible to packet-based technologies, in particular, to Internet protocol (IP)-based technologies. Results of performance analysis models are presented which illustrate the tradeoffs between various approaches. Based on the results of these studies as well as established practice and experience, methods for dynamic QoS routing and admission control are proposed for consideration in network evolution to IP-based technologies. In particular, we find that aggregated per-virtual-network bandwidth allocation compares favorably with per-flow allocation. We also find that event-dependent routing (EDR) methods for management of label switched paths perform just as well or better than the state-dependent routing methods with flooding, which means that EDR path selection has potential to significantly enhance network scalability.

Spurious dynamics in somatosensory cortex

Cortical networks are dynamical systems whose task is to process information. However, in addition to ‘intended’ dynamical behaviors, the sheer complexity of a cortical network's structure—regardless of its precise details—should generate additional ‘unintended’ dynamical behaviors. Dynamics observed in cortical network models and in the somatosensory cortex suggest that such spurious dynamical behaviors are likely to be pervasive but relatively simple, contributing to—rather than dominating—a network's response to stimuli. Spurious dynamics may be responsible for a variety of experimentally observed intriguing features of cortical dynamics. Because of their distributed origins and emergent nature, such dynamical features, while clearly identifiable, will resist attempts at identifying specific mechanisms to explain them. We describe some of the spurious dynamical phenomena associated with somatosensory cortical response to brushing stimulation, to illustrate how spurious dynamics can affect neurons’ functional properties, cortical stimulus representation and, ultimately, perception.

Nonlinear dynamics of ice-wedge networks and resulting sensitivity to severe cooling events.

Patterns of subsurface wedges of ice that form along cooling-induced tension fractures, expressed at the ground surface by ridges or troughs spaced 10 30 m apart, are ubiquitous in polar lowlands. Fossilized ice wedges, which are widespread at lower latitudes, have been used to infer the duration and mean temperature of cold periods within Proterozoic and Quaternary climates, and recent climate trends have been inferred from fracture frequency in active ice wedges. Here we present simulations from a numerical model for the evolution of ice-wedge networks over a range of climate scenarios, based on the interactions between thermal tensile stress, fracture and ice wedges. We find that short-lived periods of severe cooling permanently alter the spacing between ice wedges as well as their fracture frequency. This affects the rate at which the widths of ice wedges increase as well as the network's response to subsequent climate change. We conclude that wedge spacing and width in ice-wedge networks mainly reflect infrequent episodes of rapidly falling ground temperatures rather than mean conditions.

Competency program development across a merged healthcare network.

Competency validation from initial entry through continued employment is essential to ensure nursing personnel provide safe professional practice. With hospital mergers it becomes quickly apparent that varying methodologies for competency verification exist. Nursing staff educators need a system-wide competency program to ensure a single high standard for the entire healthcare network. This article addresses one healthcare network's response to the challenge of developing and implementing a standardized competency-based program across a wide variety of settings.