Population coding by globally coupled phase oscillators

A system of globally coupled phase oscillators subject to an external input is considered as a simple model of neural circuits coding external stimulus. The information coding efficiency of the system in its asynchronous state is quantified using Fisher information. The effect of coupling and noise on the information coding efficiency in the stationary state is analyzed. The relaxation process of the system after the presentation of an external input is also studied. It is found that the information coding efficiency exhibits a large transient increase before the system relaxes to the final stationary state.Comment: 7 pages, 9 figures, revised version, new figures added, to appear in JPSJ Vol 75, No.

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. \noindent The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. \noindent The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded as separate file

Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons

The modulation of the sensitivity, or gain, of neural responses to input is an important component of neural computation. It has been shown that divisive gain modulation of neural responses can result from a stochastic shunting from balanced (mixed excitation and inhibition) background activity. This gain control scheme was developed and explored with static inputs, where the membrane and spike train statistics were stationary in time. However, input statistics, such as the firing rates of pre-synaptic neurons, are often dynamic, varying on timescales comparable to typical membrane time constants. Using a population density approach for integrate-and-fire neurons with dynamic and temporally rich inputs, we find that the same fluctuation-induced divisive gain modulation is operative for dynamic inputs driving nonequilibrium responses. Moreover, the degree of divisive scaling of the dynamic response is quantitatively the same as the steady-state responses—thus, gain modulation via balanced conductance fluctuations generalizes in a straight-forward way to a dynamic setting

Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here

CONCEPTT: Continuous Glucose Monitoring in Women with Type 1 Diabetes in Pregnancy Trial: A multi-center, multi-national, randomized controlled trial - Study protocol.

BACKGROUND: Women with type 1 diabetes strive for optimal glycemic control before and during pregnancy to avoid adverse obstetric and perinatal outcomes. For most women, optimal glycemic control is challenging to achieve and maintain. The aim of this study is to determine whether the use of real-time continuous glucose monitoring (RT-CGM) will improve glycemic control in women with type 1 diabetes who are pregnant or planning pregnancy. METHODS/DESIGN: A multi-center, open label, randomized, controlled trial of women with type 1 diabetes who are either planning pregnancy with an HbA1c of 7.0 % to ≤10.0 % (53 to ≤ 86 mmol/mol) or are in early pregnancy (<13 weeks 6 days) with an HbA1c of 6.5 % to ≤10.0 % (48 to ≤ 86 mmol/mol). Participants will be randomized to either RT-CGM alongside conventional intermittent home glucose monitoring (HGM), or HGM alone. Eligible women will wear a CGM which does not display the glucose result for 6 days during the run-in phase. To be eligible for randomization, a minimum of 4 HGM measurements per day and a minimum of 96 hours total with 24 hours overnight (11 pm-7 am) of CGM glucose values are required. Those meeting these criteria are randomized to RT- CGM or HGM. A total of 324 women will be recruited (110 planning pregnancy, 214 pregnant). This takes into account 15 and 20 % attrition rates for the planning pregnancy and pregnant cohorts and will detect a clinically relevant 0.5 % difference between groups at 90 % power with 5 % significance. Randomization will stratify for type of insulin treatment (pump or multiple daily injections) and baseline HbA1c. Analyses will be performed according to intention to treat. The primary outcome is the change in glycemic control as measured by HbA1c from baseline to 24 weeks or conception in women planning pregnancy, and from baseline to 34 weeks gestation during pregnancy. Secondary outcomes include maternal hypoglycemia, CGM time in, above and below target (3.5-7.8 mmol/l), glucose variability measures, maternal and neonatal outcomes. DISCUSSION: This will be the first international multicenter randomized controlled trial to evaluate the impact of RT- CGM before and during pregnancy in women with type 1 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01788527 Registration Date: December 19, 2012

The Octopamine Receptor OAMB Mediates Ovulation via Ca2+/Calmodulin-Dependent Protein Kinase II in the Drosophila Oviduct Epithelium

Ovulation is an essential physiological process in sexual reproduction; however, the underlying cellular mechanisms are poorly understood. We have previously shown that OAMB, a Drosophila G-protein-coupled receptor for octopamine (the insect counterpart of mammalian norepinephrine), is required for ovulation induced upon mating. OAMB is expressed in the nervous and reproductive systems and has two isoforms (OAMB-AS and OAMB-K3) with distinct capacities to increase intracellular Ca2+ or intracellular Ca2+ and cAMP in vitro. Here, we investigated tissue specificity and intracellular signals required for OAMB's function in ovulation. Restricted OAMB expression in the adult oviduct epithelium, but not the nervous system, reinstated ovulation in oamb mutant females, in which either OAMB isoform was sufficient for the rescue. Consistently, strong immunoreactivities for both isoforms were observed in the wild-type oviduct epithelium. To delineate the cellular mechanism by which OAMB regulates ovulation, we explored protein kinases functionally interacting with OAMB by employing a new GAL4 driver with restricted expression in the oviduct epithelium. Conditional inhibition of Ca2+/Calmodulin-dependent protein kinase II (CaMKII), but not protein kinase A or C, in the oviduct epithelium inhibited ovulation. Moreover, constitutively active CaMKII, but not protein kinase A, expressed only in the adult oviduct epithelium fully rescued the oamb female's phenotype, demonstrating CaMKII as a major downstream molecule conveying the OAMB's ovulation signal. This is consistent with the ability of both OAMB isoforms, whose common intracellular signal in vitro is Ca2+, to reinstate ovulation in oamb females. These observations reveal the critical roles of the oviduct epithelium and its cellular components OAMB and CaMKII in ovulation. It is conceivable that the OAMB-mediated cellular activities stimulated upon mating are crucial for secretory activities suitable for egg transfer from the ovary to the uterus

The Octopamine Receptor OAMB Mediates Ovulation via Ca2+/Calmodulin-Dependent Protein Kinase II in the Drosophila Oviduct Epithelium

Ovulation is an essential physiological process in sexual reproduction; however, the underlying cellular mechanisms are poorly understood. We have previously shown that OAMB, a Drosophila G-protein-coupled receptor for octopamine (the insect counterpart of mammalian norepinephrine), is required for ovulation induced upon mating. OAMB is expressed in the nervous and reproductive systems and has two isoforms (OAMB-AS and OAMB-K3) with distinct capacities to increase intracellular Ca2+ or intracellular Ca2+ and cAMP in vitro. Here, we investigated tissue specificity and intracellular signals required for OAMB's function in ovulation. Restricted OAMB expression in the adult oviduct epithelium, but not the nervous system, reinstated ovulation in oamb mutant females, in which either OAMB isoform was sufficient for the rescue. Consistently, strong immunoreactivities for both isoforms were observed in the wild-type oviduct epithelium. To delineate the cellular mechanism by which OAMB regulates ovulation, we explored protein kinases functionally interacting with OAMB by employing a new GAL4 driver with restricted expression in the oviduct epithelium. Conditional inhibition of Ca2+/Calmodulin-dependent protein kinase II (CaMKII), but not protein kinase A or C, in the oviduct epithelium inhibited ovulation. Moreover, constitutively active CaMKII, but not protein kinase A, expressed only in the adult oviduct epithelium fully rescued the oamb female's phenotype, demonstrating CaMKII as a major downstream molecule conveying the OAMB's ovulation signal. This is consistent with the ability of both OAMB isoforms, whose common intracellular signal in vitro is Ca2+, to reinstate ovulation in oamb females. These observations reveal the critical roles of the oviduct epithelium and its cellular components OAMB and CaMKII in ovulation. It is conceivable that the OAMB-mediated cellular activities stimulated upon mating are crucial for secretory activities suitable for egg transfer from the ovary to the uterus

Impact of network structure and cellular response on spike time correlations

Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neurons. As a result there is increasing interest in the structure of cooperative -- or correlated -- activity in neural populations, and in the possible impact of such correlations on the neural code. A fundamental theoretical challenge is to understand how the architecture of network connectivity along with the dynamical properties of single cells shape the magnitude and timescale of correlations. We provide a general approach to this problem by extending prior techniques based on linear response theory. We consider networks of general integrate-and-fire cells with arbitrary architecture, and provide explicit expressions for the approximate cross-correlation between constituent cells. These correlations depend strongly on the operating point (input mean and variance) of the neurons, even when connectivity is fixed. Moreover, the approximations admit an expansion in powers of the matrices that describe the network architecture. This expansion can be readily interpreted in terms of paths between different cells. We apply our results to large excitatory-inhibitory networks, and demonstrate first how precise balance --- or lack thereof --- between the strengths and timescales of excitatory and inhibitory synapses is reflected in the overall correlation structure of the network. We then derive explicit expressions for the average correlation structure in randomly connected networks. These expressions help to identify the important factors that shape coordinated neural activity in such networks

How Structure Determines Correlations in Neuronal Networks

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks