Journal of Computational Neuroscience最新文献

Recurrent neural networks of spiking neurons can exhibit long lasting and even persistent activity. Such networks are often not robust and exhibit spike and firing rate statistics that are inconsistent with experimental observations. In order to overcome this problem most previous models had to assume that recurrent connections are dominated by slower NMDA type excitatory receptors. Usually, the single neurons within these networks are very simple leaky integrate and fire neurons or other low dimensional model neurons. However real neurons are much more complex, and exhibit a plethora of active conductances which are recruited both at the sub and supra threshold regimes. Here we show that by including a small number of additional active conductances we can produce recurrent networks that are both more robust and exhibit firing-rate statistics that are more consistent with experimental results. We show that this holds both for bi-stable recurrent networks, which are thought to underlie working memory and for slowly decaying networks which might underlie the estimation of interval timing. We also show that by including these conductances, such networks can be trained to using a simple learning rule to predict temporal intervals that are an order of magnitude larger than those that can be trained in networks of leaky integrate and fire neurons.

In this article, we elucidate the roles of divalent ion condensation and highly polarized immobile water molecules on the propagation of ionic calcium waves along actin filaments. We introduced a novel electrical triple layer model and used a non-linear Debye-Huckel theory with a non-linear, dissipative, electrical transmission line model to characterize the physicochemical properties of each monomer in the filament. This characterization is carried out in terms of an electric circuit model containing monomeric flow resistances and ionic capacitances in both the condensed and diffuse layers. We considered resting and excited states of a neuron using representative mono and divalent electrolyte mixtures. Additionally, we used 0.05V and 0.15V voltage inputs to study ionic waves along actin filaments in voltage clamp experiments. Our results reveal that the physicochemical properties characterizing the condensed and diffuse layers lead to different electrical conductive mediums depending on the ionic species and the neuron state. This region specific propagation mechanism provides a more realistic avenue of delivery by way of cytoskeleton filaments for larger charged cationic species. A new direct path for transporting divalent ions might be crucial for many electrical processes found in localized neuron elements such as at mitochondria and dendritic spines.

Fish escape from approaching threats via a stereotyped escape behavior. This behavior, and the underlying neural circuit organized around the Mauthner cell command neurons, have both been extensively investigated experimentally, mainly in two laboratory model organisms, the goldfish and the zebrafish. However, fish biodiversity is enormous, a number of variants of the basal escape behavior exist. In marine gobies (a family of small benthic fishes) which share burrows with alpheid shrimp, the escape behavior has likely been partially modified into a tactile communication system which allow the fish to communicate the approach of a predatory fish to the shrimp. In this communication system, the goby responds to intermediate-strength threats with a brief tail-flick which the shrimp senses with its antennae.We investigated the shrimp goby escape and communication system with computational models. We asked how the circuitry of the basal escape behavior could be modified to produce behavior akin to the shrimp-goby communication system. In a simple model, we found that mutual inhibitions between Mauthner cells can be tuned to produce an oscillatory response to intermediate strength inputs, albeit only in a narrow parameter range.Using a more detailed model, we found that two modifications of the fish locomotor system transform it into a model reproducing the shrimp goby behavior. These modifications are: 1. modifying the central pattern generator which drives swimming such that it is quiescent when receiving no inputs; 2. introducing a direct sensory input to this central pattern generator, bypassing the Mauthner cells.

We propose a novel phase based analysis with the purpose of quantifying the periodic bursts of activity observed in various neuronal systems. The way bursts are intiated and propagate in a spatial network is still insufficiently characterized. In particular, we investigate here how these spatiotemporal dynamics depend on the mean connection length. We use a simplified description of a neuron's state as a time varying phase between firings. This leads to a definition of network bursts, that does not depend on the practitioner's individual judgment as the usage of subjective thresholds and time scales. This allows both an easy and objective characterization of the bursting dynamics, only depending on system's proper scales. Our approach thus ensures more reliable and reproducible measurements. We here use it to describe the spatiotemporal processes in networks of intrinsically oscillating neurons. The analysis rigorously reveals the role of the mean connectivity length in spatially embedded networks in determining the existence of "leader" neurons during burst initiation, a feature incompletely understood observed in several neuronal cultures experiments. The precise definition of a burst with our method allowed us to rigorously characterize the initiation dynamics of bursts and show how it depends on the mean connectivity length. Although presented with simulations, the methodology can be applied to other forms of neuronal spatiotemporal data. As shown in a preliminary study with MEA recordings, it is not limited to in silico modeling.

Intrinsic oscillators in the central nervous system play a preeminent role in the neural control of rhythmic behaviors, yet little is known about how the ionic milieu regulates their output patterns. A powerful system to address this question is the pacemaker nucleus of the weakly electric fish Apteronotus leptorhynchus. A neural network comprised of an average of 87 pacemaker cells and 20 relay cells produces tonic oscillations, with higher frequencies in males compared to females. Previous empirical studies have suggested that this sexual dimorphism develops and is maintained through modulation of buffering of extracellular K⁺ by a massive meshwork of astrocytes enveloping the pacemaker and relay cells. Here, we constructed a model of this neural network that can generate sustained spontaneous oscillations. Sensitivity analysis revealed the potassium equilibrium potential, E_K (as a proxy of extracellular K⁺ concentration), and corresponding somatic channel conductances as critical determinants of oscillation frequency and amplitude. In models of both the pacemaker nucleus network and isolated pacemaker and relay cells, the frequency increased almost linearly with E_K, whereas the amplitude decreased nonlinearly with increasing E_K. Our simulations predict that this frequency increase is largely caused by a shift in the minimum K⁺ conductance over one oscillation period. This minimum is close to zero at more negative E_K, converging to the corresponding maximum at less negative E_K. This brings the resting membrane potential closer to the threshold potential at which voltage-gated Na⁺ channels become active, increasing the excitability, and thus the frequency, of pacemaker and relay cells.

Place cells and grid cells are important neurons involved in spatial navigation in the mammalian brain. Grid cells are believed to play an important role in forming a cognitive map of the environment. Experimental observations in recent years showed that the grid pattern is not invariant but is influenced by the shape of the spatial environment. However, the cause of this deformation remains elusive. Here, we focused on the functional interactions between place cells and grid cells, utilizing the information of location relationships between the firing fields of place cells to optimize the previous grid cell feedforward generation model and expand its application to more complex environmental scenarios. Not only was the regular equilateral triangle periodic firing field structure of the grid cells reproduced, but the expected results were consistent with the experiment for the environment with various complex boundary shapes and environmental deformation. Even in the field of three-dimensional spatial grid patterns, forward-looking predictions have been made. This provides a possible model explanation for how the coupling of grid cells and place cells adapt to the diversity of the external environment to deepen our understanding of the neural basis for constructing cognitive maps.

It has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.