Using Heterogeneity to Predict Inhibitory Network Model Characteristics

Skinner, Frances K.; Chung, Ji Yeon; Ncube, Israel; Murray, Peter A.; Campbell, Sue Ann

doi:10.1152/jn.00619.2004

Cited by 19 publications

(11 citation statements)

References 40 publications

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“…The GABA A synaptic model from LNs to PNs was adapted from ref. 54 and is detailed in SI Text, Model. Network simulations were performed with SIR-ENE, a C-based neural simulator developed by our team and available at http://sirene.gforge.inria.fr.…”

Section: Methodsmentioning

confidence: 99%

Interaction of cellular and network mechanisms for efficient pheromone coding in moths

Belmabrouk

Nowotny

Rospars³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Sensory systems, both in the living and in machines, have to be optimized with respect to their environmental conditions. The pheromone subsystem of the olfactory system of moths is a particularly well-defined example in which rapid variations of odor content in turbulent plumes require fast, concentration-invariant neural representations. It is not clear how cellular and network mechanisms in the moth antennal lobe contribute to coding efficiency. Using computational modeling, we show that intrinsic potassium currents (I A and I SK ) in projection neurons may combine with extrinsic inhibition from local interneurons to implement a dual latency code for both pheromone identity and intensity. The mean latency reflects stimulus intensity, whereas latency differences carry concentration-invariant information about stimulus identity. In accordance with physiological results, the projection neurons exhibit a multiphasic response of inhibition-excitation-inhibition. Together with synaptic inhibition, intrinsic currents I A and I SK account for the first and second inhibitory phases and contribute to a rapid encoding of pheromone information. The first inhibition plays the role of a reset to limit variability in the time to first spike. The second inhibition prevents responses of excessive duration to allow tracking of intermittent stimuli. To prevent crossattraction, the pheromone blend is distinguishable not only by the identity of the individual components but also by their precise ratio. Behavioral experiments revealed that male moths are tuned to the specific proportions emitted by their conspecific females. Slight changes in component ratios, for example, can inhibit the anemotactic response of males of one species, while attracting males from another species (4-6). Pheromones are passively transported in the air and mixed by atmospheric turbulence, and therefore, during flight, male moths encounter pheromone filaments with all components present in the same proportions as released (7) but over a broad range of concentrations (8, 9). In the race for mating, flying male moths have to solve a difficult pattern recognition problem (i.e., recognize, in real time, pheromone compounds and their proportions independently of blend concentration). To do so, the information delivered by olfactory receptor neurons (ORNs) is integrated in the antennal lobe by a neural network involving inhibitory local neurons (LNs) and excitatory projection neurons (PNs). All synaptic connections between these neurons take place in a subset of enlarged, sexually dimorphic glomeruli, the macroglomerular complex (MGC). The circuitry of the MGC is similar to the one of the generalist olfactory subsystem. In the generalist subsystem, prolonged odor stimulations (up to several seconds) create local field potential oscillations and transient PN assemblies that synchronize to them and evolve in time in a stimulus-specific manner (10-12). Using time in such a manner as a coding dimension, however, poses a serious problem for a flying insect. In nat...

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Section: Methodsmentioning

confidence: 99%

Interaction of cellular and network mechanisms for efficient pheromone coding in moths

Belmabrouk

Nowotny

Rospars³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…[for more physiologically elaborate models, see, for example, Skinner et al (2005) and references therein]. By default, the reversal potential is set much lower than the spike threshold to allow a slow ramping of activity.…”

Section: Methodsmentioning

confidence: 99%

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

Thivierge¹,

Cisek²

2008

J. Neurosci.

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Neural synchronization is of wide interest in neuroscience and has been argued to form the substrate for conscious attention to stimuli, movement preparation, and the maintenance of task-relevant representations in active memory. Despite a wealth of possible functions, the mechanisms underlying synchrony are still poorly understood. In particular, in vitro preparations have demonstrated synchronization with no apparent periodicity, which cannot be explained by simple oscillatory mechanisms. Here, we investigate the possible origins of nonperiodic synchronization through biophysical simulations. We show that such aperiodic synchronization arises naturally under a simple set of plausible assumptions, depending crucially on heterogeneous cell properties. In addition, nonperiodicity occurs even in the absence of stochastic fluctuation in membrane potential, suggesting that it may represent an intrinsic property of interconnected networks. Simulations capture some of the key aspects of population-level synchronization in spontaneous network spikes (NSs) and suggest that the intrinsic nonperiodicity of NSs observed in reduced cell preparations is a phenomenon that is highly robust and can be reproduced in simulations that involve a minimal set of realistic assumptions. In addition, a model with spike timing-dependent plasticity can overcome a natural tendency to exhibit nonperiodic behavior. After rhythmic stimulation, the model does not automatically fall back to a state of nonperiodic behavior, but keeps replaying the pattern of evoked NSs for a few cycles. A cluster analysis of synaptic strengths highlights the importance of population-wide interactions in generating this result and describes a possible route for encoding temporal patterns in networks of neurons.

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“…It is known that heterogeneity (in terms of different intrinsic properties and afferent drives) in networks coupled by mutual inhibition can (mathematically) bring about different modes and mechanisms of attaining synchronous activity (e.g., see Skinner et al 2005b;White et al 1998). However, consideration of homogeneous mutual inhibitory networks help uncover interesting and nonintuitive dynamics in the first place (i.e., synchronous activity in purely inhibitory networks) (see Wang and Rinzel 1992).…”

Section: Heterogeneous Networkmentioning

confidence: 99%

Distal Gap Junctions and Active Dendrites Can Tune Network Dynamics

Saraga

Ng²,

Skinner³

2006

Journal of Neurophysiology

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Saraga, Fernanda, Leo Ng, and Frances K. Skinner. Distal gap junctions and active dendrites can tune network dynamics. J Neurophysiol 95: 1669 -1682, 2006. First published December 7, 2005 doi:10.1152/jn.00662.2005. Gap junctions allow direct electrical communication between CNS neurons. From theoretical and modeling studies, it is well known that although gap junctions can act to synchronize network output, they can also give rise to many other dynamic patterns including antiphase and other phase-locked states. The particular network pattern that arises depends on cellular, intrinsic properties that affect firing frequencies as well as the strength and location of the gap junctions. Interneurons or GABAergic neurons in hippocampus are diverse in their cellular characteristics and have been shown to have active dendrites. Furthermore, parvalbumin-positive GABAergic neurons, also known as basket cells, can contact one another via gap junctions on their distal dendrites. Using two-cell network models, we explore how distal electrical connections affect network output. We build multi-compartment models of hippocampal basket cells using NEURON and endow them with varying amounts of active dendrites. Two-cell networks of these model cells as well as reduced versions are explored. The relationship between intrinsic frequency and the level of active dendrites allows us to define three regions based on what sort of network dynamics occur with distal gap junction coupling. Weak coupling theory is used to predict the delineation of these regions as well as examination of phase response curves and distal dendritic polarization levels. We find that a nonmonotonic dependence of network dynamic characteristics (phase lags) on gap junction conductance occurs. This suggests that distal electrical coupling and active dendrite levels can control how sensitive network dynamics are to gap junction modulation. With the extended geometry, gap junctions located at more distal locations must have larger conductances for pure synchrony to occur. Furthermore, based on simulations with heterogeneous networks, it may be that one requires active dendrites if phase-locking is to occur in networks formed with distal gap junctions. Interneurons, or inhibitory GABAergic neurons, in hippocampus are diverse in their cellular characteristics and have been shown and suggested to have active dendrites (Maccaferri et al. 2004;Martina et al. 2000;McBain and Fisahn 2001;Traub and Miles 1995). Furthermore, parvalbumin-positive GABAergic neurons, also known as basket cells, are known to contact one another via gap junctions on their distal dendrites Kosaka 2000, 2003;Katsumaru et al. 1988;Kosaka and Hama 1985). Because gap junctions represent a form of direct, bidirectional electrical communication, it is reasonable to think of their primary role as that of mediating synchrony. However, theoretical and modeling studies show that although gap junctions can act to synchronize network output in firing (oscillating) neurons, they can also give rise to many...

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Using Heterogeneity to Predict Inhibitory Network Model Characteristics

Cited by 19 publications

References 40 publications

Interaction of cellular and network mechanisms for efficient pheromone coding in moths

Interaction of cellular and network mechanisms for efficient pheromone coding in moths

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

Distal Gap Junctions and Active Dendrites Can Tune Network Dynamics

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