Shared Input Research Articles

Mechanisms underlying desynchronization of cholinergic-evoked thalamic network activity.

Synchronous neuronal activity in the thalamocortical system is critical for a number of behaviorally relevant computations, but hypersynchrony can limit information coding and lead to epileptiform responses. In the somatosensory thalamus, afferent inputs are transformed by networks of reciprocally connected thalamocortical neurons in the ventrobasal nucleus (VB) and GABAergic neurons in the thalamic reticular nucleus (TRN). These networks can generate oscillatory activity, and studies in vivo and in vitro have suggested that thalamic oscillations are often accompanied by synchronous neuronal activity, in part mediated by widespread divergence and convergence of both reticulothalamic and thalamoreticular pathways, as well as by electrical synapses interconnecting TRN neurons. However, the functional organization of thalamic circuits and its role in shaping input-evoked activity patterns remain poorly understood. Here we show that optogenetic activation of cholinergic synaptic afferents evokes near-synchronous firing in mouse TRN neurons that is rapidly desynchronized in thalamic networks. We identify several mechanisms responsible for desynchronization: (1) shared inhibitory inputs in local VB neurons leading to asynchronous and imprecise rebound bursting; (2) TRN-mediated lateral inhibition that further desynchronizes firing in the VB; and (3) powerful yet sparse thalamoreticular connectivity that mediates re-excitation of the TRN but preserves asynchronous firing. Our findings reveal how distinct local circuit features interact to desynchronize thalamic network activity.

A joint measurement of efficiency and effectiveness using network data envelopment analysis approach in the presence of shared input

This paper proposes a novel network data envelopment analysis (NDEA) approach to measure both technical efficiency and service effectiveness of railway transportation services. In particular, railway transportation systems consume varying amounts of shared inputs to produce outputs. The proposed NDEA model represents both the non-storable feature of transportation service and production technologies in a unified framework in the presence of shared inputs. Hence, the proposed model not only measures the overall efficiency of the railways, but also estimates passenger and freight technical efficiency, service effectiveness, and technical effectiveness, simultaneously. A case study validates the proposed model.

Distinct Lineage-Dependent Structural and Functional Organization of the Hippocampus

The hippocampus, as part of the cerebral cortex, is essential for memory formation and spatial navigation. Although it has been extensively studied, especially as a model system for neurophysiology, the cellular processes involved in constructing and organizing the hippocampus remain largely unclear. Here, we show that clonally related excitatory neurons in the developing hippocampus are progressively organized into discrete horizontal, but not vertical, clusters in the stratum pyramidale, as revealed by both cell-type-specific retroviral labeling and mosaic analysis with double markers (MADM). Moreover, distinct from those in the neocortex, sister excitatory neurons in the cornu ammonis 1 region of the hippocampus rarely develop electrical or chemical synapses with each other. Instead, they preferentially receive common synaptic input from nearby fast-spiking (FS), but not non-FS, interneurons and exhibit synchronous synaptic activity. These results suggest that shared inhibitory input may specify horizontally clustered sister excitatory neurons as functional units in the hippocampus.

Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input

The operational performance of airline includes production and consumption technologies for transporting passengers and cargoes. By determining the performance of airlines' operations one can get further insights from the obtained results and can develop appropriate policy for improving the operational performance. This paper, for the first time, proposes a novel slacks-based measure network data envelopment analysis (SBM-NDEA) approach to measure both technical efficiency and service effectiveness of airlines. The proposed model represents both the non-storable feature of transportation service and production technologies in a unified framework in the presence of shared input. Also, the proposed model estimates both technical efficiency and service effectiveness. A case study is presented.

Gap Junctions Are Essential for Generating the Correlated Spike Activity of Neighboring Retinal Ganglion Cells

Neurons throughout the brain show spike activity that is temporally correlated to that expressed by their neighbors, yet the generating mechanism(s) remains unclear. In the retina, ganglion cells (GCs) show robust, concerted spiking that shapes the information transmitted to central targets. Here we report the synaptic circuits responsible for generating the different types of concerted spiking of GC neighbors in the mouse retina. The most precise concerted spiking was generated by reciprocal electrical coupling of GC neighbors via gap junctions, whereas indirect electrical coupling to a common cohort of amacrine cells generated the correlated activity with medium precision. In contrast, the correlated spiking with the lowest temporal precision was produced by shared synaptic inputs carrying photoreceptor noise. Overall, our results demonstrate that different synaptic circuits generate the discrete types of GC correlated activity. Moreover, our findings expand our understanding of the roles of gap junctions in the retina, showing that they are essential for generating all forms of concerted GC activity transmitted to central brain targets.

Going beyond Poisson processes: a new statistical framework in neuronal modeling and data analysis

Cortical spike trains in vivo often show a high irregularity, reflected by a coefficient of variation (CV) close to one [1]. Such irregular single neuron spiking has consequently been associated with Poisson processes, and many models involving neuronal spike trains inherited this ideology. This viewpoint is further supported by the Palm-Khintchine theorem, which states that a superposition of a large number of renewal processes with very small intensity behaves like a Poisson process. It was demonstrated, however, that this theorem doesn't always apply to the superposition of neuronal spike trains [2-4]. Moreover, Poisson processes lack the temporal properties observed in population responses to input modulation either via a stimulus in vivo [5] or via electrical stimulation in vitro [6]. In this study, we report on new techniques to deal with non-Possonian aspects of stationary neuronal spike trains, as well as non-equilibrium population responses [7], based on Markov point processes (MPP), commonly known as continuous time Markov chains (CTMC). We compute the interspike interval (ISI) distribution by algebraically solving the first passage time problem for MPP neuron models, and compute the transient population responses with a similar technique. The same technique is used to compute exact cross-correlation functions for a shared input paradigm [8]. We advertise MPPs as a new powerful framework in neural network modeling and neural data analysis with many possible applications.

Shared Input Multimodal Mobile Interfaces: Interaction Modality Effects on Menu Selection in Single-Task and Dual-Task Environments

Audio and visual modalities are two common output channels in the user interfaces embedded in today’s mobile devices. However, these user interfaces typically center on the visual modality as the primary output channel, with audio output serving a secondary role. This paper argues for an increased need for shared input multimodal user interfaces for mobile devices. A shared input multimodal interface can be operated independently using a specific output modality, leaving users to choose the preferred method of interaction in different scenarios. We evaluate the value of a shared input multimodal menu system in both a single-task desktop setting and in a dynamic dual-task setting, in which the user was required to interact with the shared input multimodal menu system while driving a simulated vehicle. Results indicate that users were faster at locating a target item in the menu when visual feedback was provided in the single-task desktop setting, but in the dual-task driving setting, visual output presented a significant source of visual distraction that interfered with driving performance. In contrast, auditory output mitigated some of the risk associated with menu selection while driving. A shared input multimodal interface allows users to take advantage of multiple feedback modalities properly, providing a better overall experience.

Disentangling the cost efficiency of jointly provided water and wastewater services

Providing operators with objective incentives for cost efficiency and continuous improvement in the provision of public services are major concerns for regulators. Measuring efficiency empirically is complex and this complexity is accentuated when the same operator is responsible for delivering more than one service (e.g. in order to explore potential economies of scope). Based on a sample of operators that provide water and wastewater services, this paper uses a shared input data envelopment analysis model to measure separately the efficiency of each service. The results show that a single measure may not provide enough information for monitoring multi-utilities. Together with other indicators, the proposed model can assist decision-makers in prioritizing efforts to improve overall efficiency.

Evaluating the efficiency of municipalities in collecting and processing municipal solid waste: A shared input DEA-model

This paper proposed an adjusted “shared-input” version of the popular efficiency measurement technique Data Envelopment Analysis (DEA) that enables evaluating municipality waste collection and processing performances in settings in which one input (waste costs) is shared among treatment efforts of multiple municipal solid waste fractions. The main advantage of this version of DEA is that it not only provides an estimate of the municipalities overall cost efficiency but also estimates of the municipalities’ cost efficiency in the treatment of the different fractions of municipal solid waste (MSW). To illustrate the practical usefulness of the shared input DEA-model, we apply the model to data on 293 municipalities in Flanders, Belgium, for the year 2008.

Coupled projects, core imputations, and the CAPM

Projects, private or public, that share input factors or output requirements had better be construed as members of a portfolio. Present risk, the capital asset pricing model may facilitate valuation of each member. Chief results of that model are derived and generalized here as core solutions to a transferable-utility production game. Shadow prices define stochastic discount factors that determine values of individual projects. Variance aversion largely affects such prices whence optimal allocations.

Experiencing the Impact of Organizational Structure on Planning and Visioning Tasks

The various ways natural resource agencies and programs are structured and how that impacts leadership style and products is an important concept for students to understand. Leadership style and organizational structure determine visions, missions, goals and objectives that set the tone for organizations. This exercise demonstrates organizational structure and the impact of leadership style by having students work collaboratively to write a mission statement, goals and objectives within the confines of assigned structures. The class was divided into organizations with different structures by using role cards. The groups had contrasting experiences resulting in dissimilar products. The partnership group for example, was very collaborative and had a comprehensive, well-planned product as a result of shared input. The students deemed the activity as an engaging, helpful and fun way to experience and learn about the implications of organizational structure; while writing the mission, goals and objectives helped students learn the concepts.

Neural Synaptic Weighting With a Pulse-Based Memristor Circuit

A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed. In the memristor weighting circuit, both positive and negative multiplications are performed via a charge-dependent Ohm's law (). The circuit is composed of five memristors with bridge-like connections and operates like an artificial synapse with pulse-based processing and adjustability. The sign switching pulses, weight setting pulses and synaptic processing pulses are applied through a shared input terminal. Simulations are done with both linear memristor and window-based nonlinear memristor models.

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells

Synchronized spontaneous firing among retinal ganglion cells (RGCs), on timescales faster than visual responses, has been reported in many studies. Two candidate mechanisms of synchronized firing include direct coupling and shared noisy inputs. In neighboring parasol cells of primate retina, which exhibit rapid synchronized firing that has been studied extensively, recent experimental work indicates that direct electrical or synaptic coupling is weak, but shared synaptic input in the absence of modulated stimuli is strong. However, previous modeling efforts have not accounted for this aspect of firing in the parasol cell population. Here we develop a new model that incorporates the effects of common noise, and apply it to analyze the light responses and synchronized firing of a large, densely-sampled network of over 250 simultaneously recorded parasol cells. We use a generalized linear model in which the spike rate in each cell is determined by the linear combination of the spatio-temporally filtered visual input, the temporally filtered prior spikes of that cell, and unobserved sources representing common noise. The model accurately captures the statistical structure of the spike trains and the encoding of the visual stimulus, without the direct coupling assumption present in previous modeling work. Finally, we examined the problem of decoding the visual stimulus from the spike train given the estimated parameters. The common-noise model produces Bayesian decoding performance as accurate as that of a model with direct coupling, but with significantly more robustness to spike timing perturbations.

Conservation of aTritoniaPedal peptides network in gastropods

Adults of the nudibranch mollusc Tritonia diomedea crawl using mucociliary locomotion. Crawling is controlled in part by the large Pedal 5 (Pd5) and Pedal 6 (Pd6) neurons that produce Tritonia Pedal peptides (TPeps). TPeps elicit an increase in ciliary beat frequency, thereby increasing crawling speed. In adults of T. diomedea, an extensive network of TPep-containing neurites adjacent to the basement membrane of the pedal epithelium delivers TPeps to the ciliated cells. In this study, we show that diverse nudibranchs all have a pattern of TPep-like immunoreactivity similar to that of T. diomedea, with thin tracts of TPep-like immunoreactive (TPep-LIR) neurites projecting to the epithelial layer. We also show that members of two non-nudibranch gastropod species have a pattern of TPep-innervation similar to that of the nudibranchs. In addition, we characterized two pairs of motor neurons in adults of the nudibranch Armina californica that are possible homologues of the Pd5 and Pd6 cells in T. diomedea. Activity in one of these pairs, the Pedal Peptidergic Dorsal 1 (PPD1) cells, was correlated with mucociliary locomotion. The second pair, the Pedal Peptidergic Ventral 1 cells, shared synchronous synaptic input with the PPD1 cells, a pattern consistent with the shared synaptic input of the T. diomedea Pd5 and Pd6 cells. These findings suggest that the roles of the Pd5 and Pd6 cells as mucociliary motor neurons in nudibranchs are conserved evolutionarily. Additionally, the extensive network of TPep-LIR neurites seen in the foot of T. diomedea appears likely to be a common feature among gastropods.

Cone photoreceptor contributions to noise and correlations in the retinal output

Transduction and synaptic noise generated in retinal cone photoreceptors determines the fidelity with which light inputs are encoded, while the readout of cone signals by downstream circuits determines whether this fidelity is used for vision. We examined the impact of cone noise on visual signals by measuring its contribution to correlated noise in primate retinal ganglion cells. Correlated noise was strong in responses of dissimilar cell types with shared cone inputs. The dynamics of cone noise could account for rapid correlations in ganglion cell activity, and the extent of shared cone input could explain correlation strength. Further, correlated noise limited the fidelity with which visual signals were encoded by populations of ganglion cells. Thus a simple picture emerges: cone noise, traversing the retina through diverse pathways, accounts for most of the noise and correlations in the retinal output and constrains how higher centers exploit signals carried by parallel visual pathways.

Dendritic coding of multiple sensory inputs in single cortical neurons in vivo

Single cortical neurons in the mammalian brain receive signals arising from multiple sensory input channels. Dendritic integration of these afferent signals is critical in determining the amplitude and time course of the neurons' output signals. As of yet, little is known about the spatial and temporal organization of converging sensory inputs. Here, we combined in vivo two-photon imaging with whole-cell recordings in layer 2 neurons of the mouse vibrissal cortex as a means to analyze the spatial pattern of subthreshold dendritic calcium signals evoked by the stimulation of different whiskers. We show that the principle whisker and the surrounding whiskers can evoke dendritic calcium transients in the same neuron. Distance-dependent attenuation of dendritic calcium transients and the corresponding subthreshold depolarization suggest feed-forward activation. We found that stimulation of different whiskers produced multiple calcium hotspots on the same dendrite. Individual hotspots were activated with low probability in a stochastic manner. We show that these hotspots are generated by calcium signals arising in dendritic spines. Some spines were activated uniquely by single whiskers, but many spines were activated by multiple whiskers. These shared spines indicate the existence of presynaptic feeder neurons that integrate and transmit activity arising from multiple whiskers. Despite the dendritic overlap of whisker-specific and shared inputs, different whiskers are represented by a unique set of activation patterns within the dendritic field of each neuron.

Correlation transmission of spiking neurons is boosted by synchronous input

Ever since the discovery of precisely timed events of cortical neurons [1], their role for information processing has been highly debated. The widespread belief that synchrony is an epiphenomenon caused by shared afferents among neurons [2] has constantly been challenged by reports observing task related modulation of synchrony, lately in primary visual cortex [3] and motor cortex [4]. More so, the recently found decorrelation in cortical networks [5] suggests that the ground state of recurrent balanced networks provides a suitable substrate on top of which synchronized events can represent information. In this work we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to closely time-locked spiking activity of pairs of neurons [6]. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation [7] to pulse-coupling, allowing us to introduce spiking correlations in the afferent synaptic activity. We compare situations in which the covariance in the input to a pair of model neurons is kept constant, but is realized by different proportions of common afferents and spiking synchrony. This allows us to address the question how much synchrony is caused by afferent synchronized events and how much is intrinsic to cortex due to its structure. We find that at fixed input covariance, already weakly synchronous inputs boost the synchrony in the outgoing spiking activity compared to shared input alone (Fig. ​(Fig.1A),1A), sharpening the correlation functions (Fig. ​(Fig.1B).1B). In the regime of strong input synchrony we observe that the output correlation becomes even higher than the correlation in the input (cf. gray area in Fig. ​Fig.1A).1A). Recent theoretical insights [8] into the non-linear response properties of neurons enable us to explain how such correlation transmission gain > 1 is possible. Figure 1 Correlation transmission of a pair of model neurons. A Output correlation at 1 ms precision as a function of input correlation. Transmission for 10 per cent pairwise synchronous input spikes in black, no input synchrony in gray. The correlation gain exceeds ... Partially supported by BMBF Grant 01IB001A (brain@work), the Helmholtz Alliance on Systems Biology, the Next-Generation Supercomputer Project of MEXT, EU Grant 15879 (FACETS), EU Grant 269921 (BrainScaleS). All network simulations carried out with NEST (http://www.nest-initiative.org).

Shared Inputs, Entrainment, and Desynchrony in Elliptic Bursters: From Slow Passage to Discontinuous Circle Maps

What input signals will lead to synchrony versus desynchrony in a group of biological oscillators? This question connects with both classical dynamical systems analyses of entrainment and phase locking and with emerging studies of stimulation patterns for controlling neural network activity. Here, we focus on the response of a population of uncoupled, elliptically bursting neurons to a common pulsatile input. We extend a phase reduction from the literature to capture inputs of varied strength, leading to a circle map with discontinuities of various orders. In a combined analytical and numerical approach, we apply our results to both a normal form model for elliptic bursting and to a biophysically based neuron model from the basal ganglia. We find that, depending on the period and amplitude of inputs, the response can appear either chaotic (with provably positive Lyapunov exponent for the associated circle maps) or periodic with a broad range of phase-locked periods. Throughout, we discuss the critical und...

DEA model with shared resources and efficiency decomposition

Data envelopment analysis (DEA) has proved to be an excellent approach for measuring performance of decision making units (DMUs) that use multiple inputs to generate multiple outputs. In many real world scenarios, DMUs have a two-stage network process with shared input resources used in both stages of operations. For example, in hospital operations, some of the input resources such as equipment, personnel, and information technology are used in the first stage to generate medical record to track treatments, tests, drug dosages, and costs. The same set of resources used by first stage activities are used to generate the second-stage patient services. Patient services also use the services generated by the first stage operations of housekeeping, medical records, and laundry. These DMUs have not only inputs and outputs, but also intermediate measures that exist in-between the two-stage operations. The distinguishing characteristic is that some of the inputs to the first stage are shared by both the first and second stage, but some of the shared inputs cannot be conveniently split up and allocated to the operations of the two stages. Recognizing this distinction is critical for these types of DEA applications because measuring the efficiency of the production for first-stage outputs can be misleading and can understate the efficiency if DEA fails to consider that some of the inputs generate other second-stage outputs. The current paper develops a set of DEA models for measuring the performance of two-stage network processes with non splittable shared inputs. An additive efficiency decomposition for the two-stage network process is presented. The models are developed under the assumption of variable returns to scale (VRS), but can be readily applied under the assumption of constant returns to scale (CRS). An application is provided.

Decorrelation of low-frequency neural activity by inhibitory feedback

To correctly judge the functional role of cooperative neural activity it is essential to understand how neural correlations are determined by the structure and dynamics of neural networks. Shared presynaptic input is one of the major sources of correlated synaptic activity in such systems. In the asynchronous state of recurrent neural network models, however, spike correlations are considerably smaller than what one would expect based on the amount of shared presynaptic sources [1,2]. A similar lack of correlations in the spiking activity of neighbouring cortical neurons has been observed experimentally [3]. Recently, it has been pointed out that shared-input correlations can be actively suppressed by the dynamics of recurrent networks [4]. Here, we show that both in networks with purely inhibitory coupling (Fig. ​(Fig.1A)1A) and in those with mixed excitatory-inhibitory coupling (Fig. ​(Fig.1B)1B) this active decorrelation affects mainly the activity at low frequencies (<20 Hz). High-frequency activity, in contrast, is rather unaffected. Simulations rule out that this phenomenon is the result of refractoriness. By means of a simple linear population-rate model we demonstrate that the effect is essentially explained by inhibitory feedback. Figure 1 Population-rate power spectra for an inhibitory (A) and a balanced recurrent network (B) of leaky integrate-and-fire model neurons (black curves). Grey curves represent population-rate spectra of ensembles of unconnected neurons receiving stationary Poisson ...