Self-sustained Activity Research Articles

Self-sustained activity in cortical network models

Self-sustained activity in cortical network models

The variability of tidewater-glacier calving: Origin of event-size and interval distributions

AbstractCalving activity at the termini of tidewater glaciers produces a wide range of iceberg sizes at irregular intervals. We present calving-event data obtained from continuous observations of the termini of two tidewater glaciers on Svalbard, and show that the distributions of event sizes and inter-event intervals can be reproduced by a simple calving model, focusing on the mutual interplay between calving and the destabilization of the glacier terminus. The event-size distributions of both the field and the model data extend over several orders of magnitude and resemble power laws. The distributions of inter-event intervals are broad, but have a less pronounced tail. In the model, the width of the size distribution increases with the calving susceptibility of the glacier terminus, a parameter measuring the effect of calving on the stress in the local neighborhood of the calving region. Inter-event interval distributions, in contrast, are insensitive to the calving susceptibility. Above a critical susceptibility, small perturbations of the glacier result in ongoing self-sustained calving activity. The model suggests that the shape of the event-size distribution of a glacier is informative about its proximity to this transition point. Observations of rapid glacier retreats can be explained by supercritical self-sustained calving.

Operator control of interneural computing machines.

A dynamic representation of neural population responses asserts that motor cortex is a flexible pattern generator sending rhythmic, oscillatory signals to generate multiphasic patterns of movement. This raises a question concerning the design and control of new computing machines that mimic the oscillatory patterns and multiphasic patterns seen in neural systems. To address this issue, we design an interneural computing machine (INCM) made of plastic random interneural connections. We develop a mechanical way to measure collective ensemble firing of neurons in INCM. Two sorts of plasticity operators are derived from the measure of synchronous neural activity and the measure of self-sustaining neural activity, respectively. Such plasticity operators conduct activity-dependent operation to modify the network structure of INCM. The activity-dependent operation meets the neurobiological perspective of Hebbian synaptic plasticity and displays the tendency toward circulation breaking aiming to control neural population dynamics. We call such operation operator control of INCM and develop a population analysis of operator control for measuring how well single neurons of INCM can produce rhythmic, oscillatory activity, but at the level of neural ensembles, generate multiphasic patterns of population responses.

Single-neuron criticality optimizes analog dendritic computation

Active dendritic branchlets enable the propagation of dendritic spikes, whose computational functions remain an open question. Here we propose a concrete function to the active channels in large dendritic trees. Modelling the input-output response of large active dendritic arbors subjected to complex spatio-temporal inputs and exhibiting non-stereotyped dendritic spikes, we find that the dendritic arbor can undergo a continuous phase transition from a quiescent to an active state, thereby exhibiting spontaneous and self-sustained localized activity as suggested by experiments. Analogously to the critical brain hypothesis, which states that neuronal networks self-organize near criticality to take advantage of its specific properties, here we propose that neurons with large dendritic arbors optimize their capacity to distinguish incoming stimuli at the critical state. We suggest that “computation at the edge of a phase transition” is more compatible with the view that dendritic arbors perform an analog rather than a digital dendritic computation.

Modeling self-sustained activity cascades in socio-technical networks

The ability to understand and eventually predict the emergence of information and activation cascades in social networks is core to complex socio-technical systems research. However, the complexity of social interactions makes this a challenging enterprise. Previous works on cascade models assume that the emergence of this collective phenomenon is related to the activity observed in the local neighborhood of individuals, but do not consider what determines the willingness to spread information in a time-varying process. Here we present a mechanistic model that accounts for the temporal evolution of the individual state in a simplified setup. We model the activity of the individuals as a complex network of interacting integrate-and-fire oscillators. The model reproduces the statistical characteristics of the cascades in real systems, and provides a framework to study the time evolution of cascades in a state-dependent activity scenario.

Self-Sustained Motor Activity Triggered by Interlimb Reflexes in Chronic Spinal Cord Injury, Evidence of Functional Ascending Propriospinal Pathways

The loss or reduction of supraspinal inputs after spinal cord injury provides a unique opportunity to examine the plasticity of neural pathways within the spinal cord. In a series of nine experiments on a patient, quadriplegic due to spinal cord injury, we investigated interlimb reflexes and self-sustained activity in completely paralyzed and paretic muscles due to a disinhibited propriospinal pathway. Electrical stimuli were delivered over the left common peroneal nerve at the fibular head as single stimuli or in trains at 2–100 Hz lasting 1 s. Single stimuli produced a robust interlimb reflex twitch in the contralateral thumb at a mean latency 69 ms, but no activity in other muscles. With stimulus trains the thumb twitch occurred at variable subharmonics of the stimulus rate, and strong self-sustained activity developed in the contralateral wrist extensors, outlasting both the stimuli and the thumb reflex by up to 20 s. Similar behavior was recorded in the ipsilateral wrist extensors and quadriceps femoris of both legs, but not in the contralateral thenar or peroneal muscles. The patient could not terminate the self-sustained activity voluntarily, but it was abolished on the left by attempted contractions of the paralyzed thumb muscles of the right hand. These responses depend on the functional integrity of an ascending propriospinal pathway, and highlight the plasticity of spinal circuitry following spinal cord injury. They emphasize the potential for pathways below the level of injury to generate movement, and the role of self-sustained reflex activity in the sequelae of spinal cord injury.

Self-sustained activity in neural networks: influence of network topology and cell types

The cerebral cortex exhibits spontaneous and structured collective activity patterns even in the absence of external stimuli [1]. A possible question that can be made given this evidence is: how does the topology of the cortical network together with the individual properties of the neuronal types that populate it affect this self-sustained neural activity? Anatomical evidence suggests that cortical architecture has modular and hierarchical design [2]. Based on their response patterns to intracellular current injection, cortical neurons may be classified into 5 main electrophysiological classes [3]: regular spiking (RS), intrinsically bursting (IB), chattering (CH), fast spiking (FS) and with low threshold spikes (LTS). Cells from the first 3 types are excitatory and cells from latter 2 are inhibitory [3]. In this work we used a hierarchical and modular network model composed of excitatory and inhibitory neurons to study the joint effect of (i) modularity level and (ii) combination of excitatory and inhibitory cell types on self-sustained network activity. Our hierarchical and modular network was constructed using a top-down method [4] starting with a random network of 1024 cells with connection probability of 0.01. The ratio of excitatory to inhibitory neurons was 4:1. Neurons were modeled using Izhikevich's neuron model [5] with parameters adjusted to reproduce the firing behaviors of the 5 cell types. Our model has 2 synaptic conductances (ge, gi), representing excitatory and inhibitory synapses. After a pre-synaptic event, these synaptic conductances are increased by constants Δge, Δgi. Otherwise, they decay according to first-order linear kinetics with characteristic times τe = 5 ms and τi = 6 ms. Using the modularization method of [4] (with rewiring probabilities Ri = 1 and Re = 0.9), we generated networks with 4 modularity levels (0-3). For each level we generated 6 networks given by the possible combinations of the 3 excitatory neuron types with the 2 inhibitory types. Our experimental protocol consisted in stimulating a network by applying white noise to all neurons for 200 ms. After the noise was switched off the simulation was allowed to run for an extra 4800 ms. We performed this experiment for 50 realizations of each network (to calculate averages) and for different combinations of the parameters (Δge, Δgi). From the resulting data we constructed, for each network configuration, a Δge-Δgi diagram plotting the time of last network spike. We defined a threshold for this time (~4500 ms) beyond which we assumed the network as having self-sustained activity. For each diagram, we measured the fraction of its total area for which self-sustained activity existed according to our criterion. This fraction, called FTA, was our measure of self-sustained network activity. Our results show that (1) FTA increases with the modularity level; (2) networks with RS excitatory cells have the highest FTA variability with modularity level; and (3) networks with CH excitatory neurons have the smallest FTA variability with modularity. For these latter networks, FTA reached maximum value for level-1 modularity. Our results show a strong dependency of self-sustained activity on both the modularity level and types of excitatory and inhibitory cells. They suggest that modular architecture favors self-sustained activity and that networks with most of excitatory cells of the RS class exhibit the widest range of self-sustained regimes.

Controlling self-sustained spiking activity by adding or removing one network link

Being able to control the neuronal spiking activity in specific brain regions is central to a treatment scheme in several brain disorders such as epileptic seizures, mental depression, and Parkinson's diseases. Here, we present an approach for controlling self-sustained oscillations by adding or removing one directed network link in coupled neuronal oscillators, in contrast to previous approaches of adding stimuli or noise. We find that such networks can exhibit a variety of activity patterns such as on-off switch, sustained spikes, and short-term spikes. We derive the condition for a specific link to be the controller of the on-off effect. A qualitative analysis is provided to facilitate the understanding of the mechanism for spiking activity by adding one link. Our findings represent the first report on generating spike activity with the addition of only one directed link to a network and provide a deeper understanding of the microscopic roots of self-sustained spiking.

Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

Both external and endogenous electrical fields widely exist in the environment of cortical neurons. The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied. It is found that self-sustained rhythmic firing patterns, which are closely correlated with the cognitive functions, are significantly modified due to the self-organizing of the network in the weak AC field. The activities of the neural networks are affected by the synaptic connection strength, the external stimuli, and so on. In the presence of learning rules, the synaptic connections can be modulated by the external stimuli, which will further enhance the sensitivity of the network to the external signal. The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network.

Changes in FDB and soleus muscle activity after a train of stimuli during upright stance

Evidence of self-sustained muscle activation following a brief electrical stimulation has been reported in the literature for certain muscles. This report shows that the foot muscle (Flexor Digitorum Brevis - FDB) shows a self-sustained increase in muscle activity during upright stance in some subjects following a train of stimuli to the tibial nerve. Healthy subjects were requested to stand upright and surface EMG electrodes were placed on the FDB, Soleus and Tibialis Anterior muscles. After background muscle activity (BGA) acquisition, a 50 Hz train of stimuli was applied to the tibial nerve at the popliteal fossa. The root mean square values (RMS) of the BGA and the post-stimulus muscle activation were computed. There was a 13.8% average increase in the FDB muscle EMG amplitude with respect to BGA after the stimulation was turned off. The corresponding post-stimulus Soleus EMG activity decreased by an average of 9.2%. We hypothesize that the sustained contraction observed in the FDB following stimulus may be evidence of persistent inward currents (PIC) generated in FDB spinal motoneurons. The post-stimulus decrease in soleus activity may have occurred due to the action of inhibitory interneurons caused by the PICs, which were triggered by the stimulus train. These sustained post-stimulation changes in postural muscle activity, found in different levels in different subjects, may be part of a set of possible responses that contribute to overall postural control.

Emergence of Spiral Wave Activity in a Mechanically Heterogeneous Reaction-Diffusion-Mechanics System

We perform a numerical study of emergent spiral wave activity in a two-dimensional reaction-diffusion-mechanics medium with a regional inhomogeneity in active and passive mechanical properties. We find that self-sustaining spiral wave activity emerges for a wide range of mechanical parameters of the inhomogeneity via five mechanisms. We classify these mechanisms, relate them to parameters of the inhomogeneity, and discuss how these results can be applied to understand the onset of cardiac arrhythmias due to regional mechanical heterogeneity.

Columnar Interactions Determine Horizontal Propagation of Recurrent Network Activity in Neocortex

The cortex is organized in vertical and horizontal circuits that determine the spatiotemporal properties of distributed cortical activity. Despite detailed knowledge of synaptic interactions among individual cells in the neocortex, little is known about the rules governing interactions among local populations. Here, we used self-sustained recurrent activity generated in cortex, also known as up-states, in rat thalamocortical slices in vitro to understand interactions among laminar and horizontal circuits. By means of intracellular recordings and fast optical imaging with voltage-sensitive dyes, we show that single thalamic inputs activate the cortical column in a preferential layer 4 (L4) → layer 2/3 (L2/3) → layer 5 (L5) sequence, followed by horizontal propagation with a leading front in supragranular and infragranular layers. To understand the laminar and columnar interactions, we used focal injections of TTX to block activity in small local populations, while preserving functional connectivity in the rest of the network. We show that L2/3 alone, without underlying L5, does not generate self-sustained activity and is inefficient propagating activity horizontally. In contrast, L5 sustains activity in the absence of L2/3 and is necessary and sufficient to propagate activity horizontally. However, loss of L2/3 delays horizontal propagation via L5. Finally, L5 amplifies activity in L2/3. Our results show for the first time that columnar interactions between supragranular and infragranular layers are required for the normal propagation of activity in the neocortex. Our data suggest that supragranular and infragranular circuits, with their specific and complex set of inputs and outputs, work in tandem to determine the patterns of cortical activation observed in vivo.

Civic Agriculture: Towards a Local Food Web for Sustainable Urban Development

The past decades have seen an accumulating body of studies assessing the benefits of smaller-scale enterprises on the level of civic and community welfare in the United States and elsewhere. In the context of producing subsistence, agriculture practice is now under a transition from large scale commodity enterprises to small scale self-sustaining civic activities. In practice, recent urban development projects emphasized the production of local food within the urban development site to promote a healthy, ecologically and economically sustainable lifestyle. This is of particular importance to the United States where more than 20 per cent of petroleum consumption goes to food processing and transportation due to the contemporary industrialized agriculture practice. This paper uses a housing development project in the United States to exemplify that civic agriculture can be integrated into urban development in the forms of community supported agriculture (CSA), kitchen gardens, farmer's market, and so on, to create a local food web, which will provide a working farm as a civic amenity on the site, allowing community members the opportunity to interact with each other and with nature, and become actively engaged with the new sustainable ways of living. The study suggests that neighborhoods that embrace a community-based environmental capitalism model of housing development can enhance the level of civic engagement among their residents, contribute to rising levels of civic welfare and socioeconomic well-being, maintain the landscape authenticity, enhance local and regional ecosystem services, reduce carbon footprint, and ultimately, promote long-term sustainability by reconnecting farm, food, and community.

Building Blocks of Self-Sustained Activity in a Simple Deterministic Model of Excitable Neural Networks

Understanding the interplay of topology and dynamics of excitable neural networks is one of the major challenges in computational neuroscience. Here we employ a simple deterministic excitable model to explore how network-wide activation patterns are shaped by network architecture. Our observables are co-activation patterns, together with the average activity of the network and the periodicities in the excitation density. Our main results are: (1) the dependence of the correlation between the adjacency matrix and the instantaneous (zero time delay) co-activation matrix on global network features (clustering, modularity, scale-free degree distribution), (2) a correlation between the average activity and the amount of small cycles in the graph, and (3) a microscopic understanding of the contributions by 3-node and 4-node cycles to sustained activity.

Computational models of neurophysiological correlates of tinnitus

The understanding of tinnitus has progressed considerably in the past decade, but the details of the mechanisms that give rise to this phantom perception of sound without a corresponding acoustic stimulus have not yet been pinpointed. It is now clear that tinnitus is generated in the brain, not in the ear, and that it is correlated with pathologically altered spontaneous activity of neurons in the central auditory system. Both increased spontaneous firing rates and increased neuronal synchrony have been identified as putative neuronal correlates of phantom sounds in animal models, and both phenomena can be triggered by damage to the cochlea. Various mechanisms could underlie the generation of such aberrant activity. At the cellular level, decreased synaptic inhibition and increased neuronal excitability, which may be related to homeostatic plasticity, could lead to an over-amplification of natural spontaneous activity. At the network level, lateral inhibition could amplify differences in spontaneous activity, and structural changes such as reorganization of tonotopic maps could lead to self-sustained activity in recurrently connected neurons. However, it is difficult to disentangle the contributions of different mechanisms in experiments, especially since not all changes observed in animal models of tinnitus are necessarily related to tinnitus. Computational modeling presents an opportunity of evaluating these mechanisms and their relation to tinnitus. Here we review the computational models for the generation of neurophysiological correlates of tinnitus that have been proposed so far, and evaluate predictions and compare them to available data. We also assess the limits of their explanatory power, thus demonstrating where an understanding is still lacking and where further research may be needed. Identifying appropriate models is important for finding therapies, and we therefore, also summarize the implications of the models for approaches to treat tinnitus.

Maladaptive plasticity for motor recovery after stroke: mechanisms and approaches.

Many studies in human and animal models have shown that neural plasticity compensates for the loss of motor function after stroke. However, neural plasticity concerning compensatory movement, activated ipsilateral motor projections and competitive interaction after stroke contributes to maladaptive plasticity, which negatively affects motor recovery. Compensatory movement on the less-affected side helps to perform self-sustaining activity but also creates an inappropriate movement pattern and ultimately limits the normal motor pattern. The activated ipsilateral motor projections after stroke are unable to sufficiently support the disruption of the corticospinal motor projections and induce the abnormal movement linked to poor motor ability. The competitive interaction between both hemispheres induces abnormal interhemispheric inhibition that weakens motor function in stroke patients. Moreover, widespread disinhibition increases the risk of competitive interaction between the hand and the proximal arm, which results in an incomplete motor recovery. To minimize this maladaptive plasticity, rehabilitation programs should be selected according to the motor impairment of stroke patients. Noninvasive brain stimulation might also be useful for correcting maladaptive plasticity after stroke. Here, we review the underlying mechanisms of maladaptive plasticity after stroke and propose rehabilitation approaches for appropriate cortical reorganization.

Student experiences of creating and sharing material in online learning

Background: User-generated content in an online environment has significant implications in both education and health. Instead of a model of knowledge being something transferred from the expert to the student or to the patient, Web 2.0 technologies offer the hope of making learning, and healthcare delivery, a more collaborative and participative process. However, some evidence suggests that this supposed democratisation of production is not entirely democratic and, in an educational context, many students are uncomfortable about sharing material they create with their peers.Aim: To understand students’ affective and other experiences of generating content to be shared online with peers (e.g. in online discussions).Methods: Online interviews, face-to-face focus groups and further methods with two student groups in medical education, one undergraduate and one postgraduate.Results: Students were broadly positive about creating and sharing material online, but were also quite anxious about doing so. Many practical issues, including around course design, student workload and assessment pressures, were barriers to students creating and sharing material. Group size is also important, with self-sustaining activity less likely in small groups.Conclusion: Successfully introducing user-generated content into medical education requires attention to practical details and an awareness of the student anxiety that can arise.

The influence of connectivity on the firing rate in a neuronal network with electrical and chemical synapses

We study the firing rate properties of a cellular automaton model for a neuronal network with chemical synapses. We propose a simple mechanism in which the nonlocal connections are included, through electrical and chemical synapses. In the latter case, we introduce a time delay which produces self-sustained activity. Nonlocal connections, or shortcuts, are randomly introduced according to a specified connection probability. There is a range of connection probabilities for which neuron firing occurs, as well as a critical probability for which the firing ceases in the absence of time delay. The critical probability for nonlocal shortcuts depends on the network size according to a power-law. We also compute the firing rate amplification factor by varying both the connection probability and the time delay for different network sizes.

Event-driven simulations of a plastic, spiking neural network

We consider a fully connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low- and high-activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.

The emergence of connectivity in neuronal networks: From bootstrap percolation to auto-associative memory

We consider a random synaptic pruning in an initially highly interconnected network. It is proved that a random network can maintain a self-sustained activity level for some parameters. For such a set of parameters a pruning is constructed so that in the resulting network each neuron/node has almost equal numbers of in- and out-connections. It is also shown that the set of parameters which admits a self-sustained activity level is rather small within the whole space of possible parameters. It is pointed out here that the threshold of connectivity for an auto-associative memory in a Hopfield model on a random graph coincides with the threshold for the bootstrap percolation on the same random graph. It is argued that this coincidence reflects the relations between the auto-associative memory mechanism and the properties of the underlying random network structure.This article is part of a Special Issue entitled “Neural Coding".