Active colonization dynamics and diversity patterns are influenced by dendritic network connectivity and species interactions

Habitat network connectivity influences colonization dynamics, species invasions, and biodiversity patterns. Recent theoretical work suggests dendritic networks, such as those found in rivers, alter expectations regarding colonization and dispersal dynamics compared with other network types. As many native and non‐native species are spreading along river networks, this may have important ecological implications. However, experimental studies testing the effects of network structure on colonization and diversity patterns are scarce. Up to now, experimental studies have only considered networks where sites are connected with small corridors, or dispersal was experimentally controlled, which eliminates possible effects of species interactions on colonization dynamics. Here, we tested the effect of network connectivity and species interactions on colonization dynamics using continuous linear and dendritic (i.e., river‐like) networks, which allow for active dispersal. We used a set of six protist species and one rotifer species in linear and dendritic microcosm networks. At the start of the experiment, we introduced species, either singularly or as a community within the networks. Species subsequently actively colonized the networks. We periodically measured densities of species throughout the networks over 2 weeks to track community dynamics, colonization, and diversity patterns. We found that colonization of dendritic networks was faster compared with colonization of linear networks, which resulted in higher local mean species richness in dendritic networks. Initially, community similarity was also greater in dendritic networks compared with linear networks, but this effect vanished over time. The presence of species interactions increased community evenness over time, compared with extrapolations from single‐species setups. Our experimental findings confirm previous theoretical work and show that network connectivity, species‐specific dispersal ability, and species interactions greatly influence the dispersal and colonization of dendritic networks. We argue that these factors need to be considered in empirical studies, where effects of network connectivity on colonization patterns have been largely underestimated.     

Dendritic network structure and dispersal affect temporal dynamics of diversity and species persistence

Landscape connectivity structure, specifically the dendritic network structure of rivers, is expected to influence community diversity dynamics by altering dispersal patterns, and subsequently the unfolding of species interactions. However, previous comparative and experimental work on dendritic metacommunities has studied diversity mostly from an equilibrium perspective. Here we investigated the effect of dendritic versus linear network structure on local (α‐diversity), among (β‐diversity) and total (γ‐diversity) temporal species community diversity dynamics. Using a combination of microcosm experiments, which allowed for active dispersal of 14 protists and a rotifer species, and numerical analyses, we demonstrate the general importance of spatial network configuration and basic life history tradeoffs as driving factors of different diversity patterns in linear and dendritic systems. We experimentally found that community diversity patterns were shaped by the interaction of dispersal within the networks and local species interactions. Specifically, α‐diversity remained higher in dendritic networks over time, especially at highly connected sites. β‐diversity was initially greater in linear networks, due to increased dispersal limitation, but became more similar to β‐diversity in dendritic networks over time. Comparing the experimental results with a neutral metacommunity model we found that dispersal and network connectivity alone may, to a large extent, explain α‐ and β‐diversity dynamics. However, additional mechanisms, such as variation in carrying capacity and competition–colonization tradeoffs, were needed in the model to capture the detailed temporal diversity dynamics of the experiments, such as a general decline in γ‐diversity and long‐term dynamics in α‐diversity.     

Investigation of close interactions between sympathetic neural fibres and the follicular dendritic cells network in the mouse spleen

In this study, co-localization between sympathetic neural fibres and the follicular dendritic cells (FDCs) network was observed within the mouse spleen by confocal technology. Immunohistochemical techniques were used to reveal the rare interactions between the FDCs network and sympathetic neural fibres. We estimated the frequency of three kinds of close interactions which could be defined as overlaps, contacts or neural fibres closer than 10 microm from a FDCs network. Using these estimates, a comparison was made between five uninfected mouse strains exhibiting the same Prnpa genotype but showing different incubation periods when inoculated with primary bovine spongiform encephalopathy (BSE)-infected brain. In prion disease, infectivity is generally detected in the spleen much earlier than in the brain, especially after peripheral inoculation. The way by which the infectious agent reaches the central nervous system is still unclear. From the five mouse strains, we obtained differences in the proportion of splenic FDCs networks with close interactions. Our work suggests that the percentage of splenic FDCs networks with at least one sympathetic neural fibre in close vicinity may influence the length of incubation period.     

Recurrent fractal neural networks: a strategy for the exchange of local and global information processing in the brain

The regulation of biological networks relies significantly on convergent feedback signaling loops that render a global output locally accessible. Ideally, the recurrent connectivity within these systems is self-organized by a time-dependent phase-locking mechanism. This study analyzes recurrent fractal neural networks (RFNNs), which utilize a self-similar or fractal branching structure of dendrites and downstream networks for phase-locking of reciprocal feedback loops: output from outer branch nodes of the network tree enters inner branch nodes of the dendritic tree in single neurons. This structural organization enables RFNNs to amplify re-entrant input by over-the-threshold signal summation from feedback loops with equivalent signal traveling times. The columnar organization of pyramidal neurons in the neocortical layers V and III is discussed as the structural substrate for this network architecture. RFNNs self-organize spike trains and render the entire neural network output accessible to the dendritic tree of each neuron within this network. As the result of a contraction mapping operation, the local dendritic input pattern contains a downscaled version of the network output coding structure. RFNNs perform robust, fractal data compression, thus coping with a limited number of feedback loops for signal transport in convergent neural networks. This property is discussed as a significant step toward the solution of a fundamental problem in neuroscience: how is neuronal computation in separate neurons and remote brain areas unified as an instance of experience in consciousness? RFNNs are promising candidates for engaging neural networks into a coherent activity and provide a strategy for the exchange of global and local information processing in the human brain, thereby ensuring the completeness of a transformation from neuronal computation into conscious experience.     

Large-Scale Information Flow in Conscious and Unconscious States: an ECoG Study in Monkeys

Consciousness is an emergent property of the complex brain network. In order to understand how consciousness is constructed, neural interactions within this network must be elucidated. Previous studies have shown that specific neural interactions between the thalamus and frontoparietal cortices; frontal and parietal cortices; and parietal and temporal cortices are correlated with levels of consciousness. However, due to technical limitations, the network underlying consciousness has not been investigated in terms of large-scale interactions with high temporal and spectral resolution. In this study, we recorded neural activity with dense electrocorticogram (ECoG) arrays and used the spectral Granger causality to generate a more comprehensive network that relates to consciousness in monkeys. We found that neural interactions were significantly different between conscious and unconscious states in all combinations of cortical region pairs. Furthermore, the difference in neural interactions between conscious and unconscious states could be represented in 4 frequency-specific large-scale networks with unique interaction patterns: 2 networks were related to consciousness and showed peaks in alpha and beta bands, while the other 2 networks were related to unconsciousness and showed peaks in theta and gamma bands. Moreover, networks in the unconscious state were shared amongst 3 different unconscious conditions, which were induced either by ketamine and medetomidine, propofol, or sleep. Our results provide a novel picture that the difference between conscious and unconscious states is characterized by a switch in frequency-specific modes of large-scale communications across the entire cortex, rather than the cessation of interactions between specific cortical regions.     

Artificial neural networks and the study of evolution of prey coloration

In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolution of prey coloration. Therefore, the capability to process patterns as well as to imitate aspects of predator's information processing and responses to visual information makes neural networks a well-suited modelling approach for the study of prey coloration. In addition, their suitability for evolutionary simulations is an advantage when complex or dynamic interactions are modelled. Since not all behaviours of neural network models are necessarily biologically relevant, it is important to validate a neural network model with empirical data. Bringing together knowledge about neural networks with knowledge about topics of prey coloration would provide a potential way to deepen our understanding of the specific appearances of prey coloration.     

Learning Universal Computations with Spikes

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.     

Desynchronization in Networks of Globally Coupled Neurons with Dendritic Dynamics

Effective desynchronization can be exploited as a tool for probing the functional significance of synchronized neural activity underlying perceptual and cognitive processes or as a mild treatment for neurological disorders like Parkinson’s disease. In this article we show that pulse-based desynchronization techniques, originally developed for networks of globally coupled oscillators (Kuramoto model), can be adapted to networks of coupled neurons with dendritic dynamics. Compared to the Kuramoto model, the dendritic dynamics significantly alters the response of the neuron to the stimulation. Under medium stimulation amplitude a bistability of the re- sponse of a single neuron is observed. When stimulated at some initial phases, the neuron displays only modulations of its firing, whereas at other initial phases it stops oscillating entirely. Significant alterations in the duration of stimulation-induced transients are also observed. These transients endure after the end of the stimulation and cause maximal desynchronization to occur not during the stimulation, but with some delay after the stimulation has been turned off. To account for this delayed desynchronization effect, we have designed a new calibration procedure for finding the stimulation parameters that result in optimal desynchronization. We have also developed a new desynchronization technique by low frequency entrainment. The stimulation techniques originally developed for the Kuramoto model, when using the new calibration procedure, can also be applied to networks with dendritic dynamics. However, the mechanism by which desynchronization is achieved is substantially different than for the network of Kuramoto oscillators. In particular, the addition of dendritic dynamics significantly changes the timing of the stimulation required to obtain desynchronization. We propose desynchronization stimulation for experimental analysis of synchronized neural processes and for the therapy of movement disorders.     

Astrocyte- neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments

Neural synchronization is considered as an important mechanism for information processing. In addition, based on recent neurophysiologic findings, it is believed that astrocytes regulate the synaptic transmission of neuronal networks. Therefore, the present study focused on determining the functional contribution of astrocytes in neuronal synchrony using both computer simulations and extracellular field potential recordings. For computer simulations, as a first step, a minimal network model is constructed by connecting two Morris-Lecar neuronal models. In this minimal model, astrocyte-neuron interactions are considered in a functional-based procedure. Next, the minimal network is extended and a biologically plausible neuronal population model is developed which considers functional outcome of astrocyte-neuron interactions too. The employed structure is based on the physiological and anatomical network properties of the hippocampal CA1 area. Utilizing these two different levels of modeling, it is demonstrated that astrocytes are able to change the threshold value of transition from synchronous to asynchronous behavior among neurons. In this way, variations in the interaction between astrocytes and neurons lead to the emergence of synchronous/asynchronous patterns in neural responses. Furthermore, population spikes are recorded from CA1 pyramidal neurons in rat hippocampal slices to validate the modeling results. It demonstrates that astrocytes play a primary role in neuronal firing synchronicity and synaptic coordination. These results may offer a new insight into understanding the mechanism by which astrocytes contribute to stabilizing neural activities.     

Artificial neural networks modeling gene-environment interaction

ABSTRACT: BACKGROUND: Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. RESULTS: In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. CONCLUSION: Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.     

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. 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. 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.     

A Boolean Model of the Gene Regulatory Network Underlying Mammalian Cortical Area Development

The cerebral cortex is divided into many functionally distinct areas. The emergence of these areas during neural development is dependent on the expression patterns of several genes. Along the anterior-posterior axis, gradients of Fgf8, Emx2, Pax6, Coup-tfi, and Sp8 play a particularly strong role in specifying areal identity. However, our understanding of the regulatory interactions between these genes that lead to their confinement to particular spatial patterns is currently qualitative and incomplete. We therefore used a computational model of the interactions between these five genes to determine which interactions, and combinations of interactions, occur in networks that reproduce the anterior-posterior expression patterns observed experimentally. The model treats expression levels as Boolean, reflecting the qualitative nature of the expression data currently available. We simulated gene expression patterns created by all possible networks containing the five genes of interest. We found that only of these networks were able to reproduce the experimentally observed expression patterns. These networks all lacked certain interactions and combinations of interactions including auto-regulation and inductive loops. Many higher order combinations of interactions also never appeared in networks that satisfied our criteria for good performance. While there was remarkable diversity in the structure of the networks that perform well, an analysis of the probability of each interaction gave an indication of which interactions are most likely to be present in the gene network regulating cortical area development. We found that in general, repressive interactions are much more likely than inductive ones, but that mutually repressive loops are not critical for correct network functioning. Overall, our model illuminates the design principles of the gene network regulating cortical area development, and makes novel predictions that can be tested experimentally.     

On looking for neural networks and “cell assemblies” that underlie behavior: II. Neural realization of the mathematical model

Part I (P. H. Greene,Bull. Math. Biophysics,24, 247–275, 1962) discussed a number of formal properties of animal behavior, and presented evidence that these properties would follow naturally from a model in which patterns of neural activity in perception or motor action constituted the resonant responses of linear neural networks. Equations were derived for parameters characterizing networks which would possess desired resonant responses. These equations expressed purely mathematical requirements. The present paper shows that a simple neural model would be entirely adequate to meet these requirements. According to this model, an input locus may become functionally connected to a particular resonant response mode by firing at a frequency which comes to approach the resonant frequency of that mode. The information in a complicated “cell assembly” of the type considered could be transmitted through a nerve tract by a very simple frequency code. One neurological guess is that frequency-coded inputs excite the transients in dendritic networks. If the amplitude of the pattern becomes large, as it would near resonance, the all-or-none axonal response would become excited. This axonal response would tend to augment resonant patterns and disrupt other patterns, for a reason inherent in any linear network. Since resonant responses are automatically present in any linear network, unless special processes suppress them, they must have led to overt behavior in animals first possessing such networks. Evolution either suppressed this feature or exploited it. Since its properties resemble those of animal behavior, the latter might be suspected. Some implications are presented regarding what a physiologist might have to look for when he studies a neural system. This research was supported by the Office of Naval Research under Contract No. Nonr 2121(17) NR 049-148. Reproduction in whole or in part is permitted for any purpose of the United States Government.     

Dependence of Transformation of Intrinsic Rhythmic Impulse Activity of Neurons on Spatio-Temporal Organization of Synaptic Actions on Dendrites: A Simulation Study

The aim of the study to elucidate the biophysical mechanisms able to determine specific transformations of the patterns of output signals of neurons (neuronal impulse codes) depending on the spatio-temporal organization of synaptic actions coming to the dendrites. We studied mathematical models of the neocortical layer 5 pyramidal neurons built according to the results of computer reconstruction of their dendritic arborizations and experimental data on the voltage-dependent conductivities of their dendritic membrane. This work is a continuation of our previous studies that showed the existence of certain relations between the complexity of neural impulse codes, on the one hand, and the complexity, size, metrical asymmetry of branching, and nonlinear membrane properties of the dendrites, on the other hand. This relation determines synchronous (with some phase shifts) or asynchronous transitions of asymmetrical dendritic subtrees between high and low depolarization states during the generation of output impulse patterns in response to distributed tonic activation of dendritic inputs. In this work we demonstrate the first time that the appearance and pattern of transformations of complex periodical impulse trains at the neuron’s output associated with receiving a short series of presynaptic action potentials are determined not only by the time of arrival of such a series, but also by their spatial addressing to asymmetric dendritic subtrees; the latter, in this case, may be in the same (synchronous transitions) or different (asynchronous transitions) electrical states. Biophysically, this phenomenon is based on a significant excess of the driving potential for a synaptic excitatory current in low-depolarization regions, as compared with that in high-depolarization dendritic regions receiving phasic synaptic stimuli. These findings open a novel aspect of the functioning of neurons and neuronal networks.     

A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns

The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.     

Deep-learning-based 3D cellular force reconstruction directly from volumetric images

The forces exerted by single cells in the three-dimensional (3D) environments play a crucial role in modulating cellular functions and behaviors closely related to physiological and pathological processes. Cellular force microscopy (CFM) provides a feasible solution for quantifying mechanical interactions, which usually regains cellular forces from deformation information of extracellular matrices embedded with fluorescent beads. Owing to computational complexity, traditional 3D-CFM is usually extremely time consuming, which makes it challenging for efficient force recovery and large-scale sample analysis. With the aid of deep neural networks, this study puts forward a novel, data-driven 3D-CFM to reconstruct 3D cellular force fields directly from volumetric images with random fluorescence patterns. The deep-learning-based network is established through stacking deep convolutional neural networks (DCNN) and specific function layers. Some necessary physical information associated with constitutive relation of extracellular matrix material is coupled to the data-driven network. The mini-batch stochastic-gradient-descent and back-propagation algorithms are introduced to ensure its convergence and training efficiency. The networks not only have good generalization ability and robustness but also can recover 3D cellular forces directly from the input fluorescence image pairs. Particularly, the computational efficiency of the deep-learning-based network is at least one to two orders of magnitude higher than that of traditional 3D-CFM. This study provides a novel scheme for developing high-performance 3D-CFM to quantitatively characterize mechanical interactions between single cells and surrounding extracellular matrices, which is of vital importance for quantitative investigations in biomechanics and mechanobiology.     

Functional interactions in hierarchically organized neural networks studied with spatiotemporal firing patterns and phase-coupling frequencies

A scalable hardware/software hybrid module--called Ubidule--endowed with bio-inspired ontogenetic and epigenetic features is configured to run a neural networks simulation with developmental and evolvable capabilities. We simulated the activity of hierarchically organized spiking neural networks characterized by an initial developmental phase featuring cell death followed by spike timing dependent synaptic plasticity in presence of background noise. An upstream 'sensory' network received a spatiotemporally organized external input and downstream networks were activated only via the upstream network. Precise firing sequences, formed by recurrent patterns of spikes intervals above chance levels, were observed in all recording conditions, thus suggesting the build-up of a connectivity able to sustain temporal information processing. The activity of a Ubinet--a network of Ubidules--is analyzed by means of virtual electrodes that recorded neural signals similar to EEG. The analysis of these signals was compared with a small set of human recordings and revealed common patterns of shift in quadratic phase coupling. The results suggest some interpretations of changes and plasticity of functional interactions between cortical areas driven by external stimuli and by learning/cognitive