The impacts of reduced connectivity on multiple facets of aquatic insect diversity in floodplain wetlands, Northeast China.

River-floodplain ecosystems are highly complex and dynamic systems that are subjected to human disturbance, such as the construction of levees. Aquatic insects are among the most widely used indicators to assess human impacts on floodplain wetlands. Most studies are still based on taxonomic biodiversity. However, trait-based approaches remain limited, which could impede the development of effective management strategies. Here, we investigated aquatic insect assemblages in eleven pairs of wetlands along the Wusuli River in two seasons (Spring and Autumn) and assessed their responses to the impact of levee construction, considering taxonomic and functional diversity. We found that pooled species richness (73 taxa) in river-connected wetlands was almost twice of that in levee-blocked wetlands (37 taxa). Six and one indicator taxa for river-connected wetlands were identified in May and October, respectively, while no indicator taxon for the levee-blocked wetlands was identified. Moreover, taxonomic and functional alpha diversity in river-connected wetlands was much higher than in levee-blocked wetlands, but beta diversity showed a contrasting pattern. Additionally, multivariate dispersion analysis indicated a more evident difference in beta diversity between river-connected and levee-blocked wetlands in May than in October, likely due to the temporary lateral connection in summer (i.e., water flowed over levees during flood events). Our results revealed that anthropogenic impacts (e.g., levee construction and agricultural activity) weakened the connectivity of floodplain wetland ecosystems, leading to decreased taxonomic and functional diversity of aquatic insects in isolated wetlands. Our study highlights the importance of combining taxonomic and trait-based approaches in biomonitoring programs of floodplain wetland ecosystems. It also underscores the necessity of restoring habitat connectivity of wetland ecosystems (e.g., river-floodplain connectivity and connections between different wetlands) to facilitate biodiversity recovery and enhance ecological functions and services supported by these valuable ecosystems.

A neural model of modified excitation/inhibition and feedback levels in schizophrenia.

Introduction: The strength of certain visual illusions, including contrast-contrast and apparent motion, is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms. Methods: To investigate whether and how these factors influence the perceived contrast-contrast and apparent motion illusions in individuals with schizophrenia, we propose a two-layer network, with top-down feedback from layer 2 to layer 1 that can model visual receptive fields (RFs) and their inhibitory and excitatory subfields. Results: Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia within certain clinical states and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups. Discussion: We propose that the heterogeneity of visual and in general sensory processing in certain clinical states of schizophrenia can be largely explained by the degree of top-down feedback reduction, emphasizing the critical role of top-down feedback in illusion perception, and to a lesser extent on the imbalance of excitation/inhibition. Our neural model provides a mechanistic explanation for the modulated visual percepts of contrast-contrast and apparent motion in schizophrenia with findings that can explain a broad range of visual perceptual observations in previous studies. The two-layer motif of the current model provides a general framework that can be tailored to investigate subcortico-cortical (such as thalamocortical) and cortico-cortical networks, bridging neurobiological changes in schizophrenia and perceptual processing.

Modeling the development of cortical responses in primate dorsal ("where") pathway to optic flow using hierarchical neural field models.

Although there is a plethora of modeling literature dedicated to the object recognition processes of the ventral ("what") pathway of primate visual systems, modeling studies on the motion-sensitive regions like the Medial superior temporal area (MST) of the dorsal ("where") pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons. Model-1 and model-2 each composed of three stages: Direction Selective Mosaic Network (DSMN), Cell Plane Network (CPNW) or the Hebbian Network (HBNW), and the Optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas, respectively, in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian rule. The simulation results show that, neurons in model-1 and model-2 (that are trained on translational, radial, and rotational sequences) develop responses that could account for MSTd cell properties found neurobiologically. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway.

A neural model of modified excitation/inhibition and feedback levels in schizophrenia.

The strength of certain visual illusions is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms. To investigate whether and how these factors influence the perceived illusions in individuals with schizophrenia, we propose a two-layer network that can model visual receptive fields (RFs), their inhibitory and excitatory subfields, and the top-down feedback. Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups. We propose that the heterogeneity of visual and in general sensory processing in schizophrenia can be largely explained by the degree of top-down feedback reduction, emphasizing the critical role of top-down feedback in illusion perception, and to a lesser extent on the imbalance of excitation/inhibition. Our neural model provides a mechanistic explanation for the modulated visual percepts in schizophrenia with findings that can explain a broad range of visual perceptual observations in previous studies. The two-layer motif of the current model provides a general framework that can be tailored to investigate subcortico-cortical (such as thalamocortical) and cortico-cortical networks, bridging neurobiological changes in schizophrenia and perceptual processing.

Freshwater fish biodiversity restoration in floodplain rivers requires connectivity and habitat heterogeneity at multiple spatial scales.

With a sixth mass extinction looming and freshwater biodiversity declining at unprecedented rates, evaluating ecological efficacy of river restoration efforts is critical in combatting global biodiversity loss. Here, we present a comprehensive study of the functioning for fishes of 46 river restoration projects in the river Rhine, one of the world's most heavily engineered lowland rivers. Floodplains with permanent, either one- or two-sided lateral connectivity to the main channel, favour total fish abundance, and are essential as nursery areas for riverine fishes. Habitat heterogeneity had a strong positive effect on species richness but was negatively related with fish abundances. However, the effects of environmental variables varied between ecological groups and spatial scales. Surprisingly, richness of critical rheophilic fishes declined with large-scale habitat heterogeneity (~1000 m), while it increased at small scales (~100 m), possibly because of the presence of unfavourable habitats for this ecological group at larger scales. Clearly, there is no one-size-fits-all design for river restoration projects. Whether a river section is free-flowing or impounded dictates the scope and efficacy of restoration projects and, within a river section, multiple complementary restoration projects might be key to mitigate freshwater fish biodiversity loss. An essential element for success is that these projects should retain permanent lateral connection to the main channel.

On the potential role of lateral connectivity in retinal anticipation.

We analyse the potential effects of lateral connectivity (amacrine cells and gap junctions) on motion anticipation in the retina. Our main result is that lateral connectivity can-under conditions analysed in the paper-trigger a wave of activity enhancing the anticipation mechanism provided by local gain control (Berry et al. in Nature 398(6725):334-338, 1999; Chen et al. in J. Neurosci. 33(1):120-132, 2013). We illustrate these predictions by two examples studied in the experimental literature: differential motion sensitive cells (Baccus and Meister in Neuron 36(5):909-919, 2002) and direction sensitive cells where direction sensitivity is inherited from asymmetry in gap junctions connectivity (Trenholm et al. in Nat. Neurosci. 16:154-156, 2013). We finally present reconstructions of retinal responses to 2D visual inputs to assess the ability of our model to anticipate motion in the case of three different 2D stimuli.

The interplay of flow processes shapes aquatic invertebrate successions in floodplain channels - A modelling applied to restoration scenarios.

The high biotic diversity supported by floodplains is ruled by the interplay of geomorphic and hydrological processes at various time scales, from daily fluctuations to decennial successions. Because understanding such processes is a key question in river restoration, we attempted to model changes in taxonomic richness in an assemblage of 58 macroinvertebrate taxa (21 gastropoda and 37 ephemeroptera, plecoptera and trichoptera, EPT) along two successional sequences typical for former braided channels. Individual models relating the occurrence of taxa to overflow and backflow durations were developed from field measurements in 19 floodplain channels of the Rhône floodplain (France) monitored over 10 years. The models were combined to simulate diversity changes along a progressive alluviation and disconnection sequence after the reconnection with the main river of a previously isolated channel. Two scenarios were considered: (i) an upstream + downstream reconnection creating a lotic channel, (ii) a downstream reconnection creating a semi-lotic channel. Reconnection led to a direct increase in invertebrate richness (on average x2.5). However, taxonomical richness showed a constant decrease as isolation progressed and reached an average of 2 for EPT and 7 for gastropods at the end of the scenarios. With more than 80% of the taxonomic models with an AUC equal or higher than 0.7 and slopes of linear relations between observed and predicted richness of 0.75 (gastropods) and 1 (EPT), the Boosted Regression Trees (BRT) provided a good basis for prediction of species assemblages. These models can be used to quantify a priori the sustainability and ecological efficiency of restoration actions and help floodplain restoration planning and management.

Erratum: Contextual Integration in Cortical and Convolutional Neural Networks.

[This corrects the article DOI: 10.3389/fncom.2020.00031.].

Contextual Integration in Cortical and Convolutional Neural Networks.

It has been suggested that neurons can represent sensory input using probability distributions and neural circuits can perform probabilistic inference. Lateral connections between neurons have been shown to have non-random connectivity and modulate responses to stimuli within the classical receptive field. Large-scale efforts mapping local cortical connectivity describe cell type specific connections from inhibitory neurons and like-to-like connectivity between excitatory neurons. To relate the observed connectivity to computations, we propose a neuronal network model that approximates Bayesian inference of the probability of different features being present at different image locations. We show that the lateral connections between excitatory neurons in a circuit implementing contextual integration in this should depend on correlations between unit activities, minus a global inhibitory drive. The model naturally suggests the need for two types of inhibitory gates (normalization, surround inhibition). First, using natural scene statistics and classical receptive fields corresponding to simple cells parameterized with data from mouse primary visual cortex, we show that the predicted connectivity qualitatively matches with that measured in mouse cortex: neurons with similar orientation tuning have stronger connectivity, and both excitatory and inhibitory connectivity have a modest spatial extent, comparable to that observed in mouse visual cortex. We incorporate lateral connections learned using this model into convolutional neural networks. Features are defined by supervised learning on the task, and the lateral connections provide an unsupervised learning of feature context in multiple layers. Since the lateral connections provide contextual information when the feedforward input is locally corrupted, we show that incorporating such lateral connections into convolutional neural networks makes them more robust to noise and leads to better performance on noisy versions of the MNIST dataset. Decomposing the predicted lateral connectivity matrices into low-rank and sparse components introduces additional cell types into these networks. We explore effects of cell-type specific perturbations on network computation. Our framework can potentially be applied to networks trained on other tasks, with the learned lateral connections aiding computations implemented by feedforward connections when the input is unreliable and demonstrate the potential usefulness of combining supervised and unsupervised learning techniques in real-world vision tasks.

Storm-driven particulate organic matter flux connects a tidal tributary floodplain wetland, mainstem river, and estuary.

The transport of terrestrial plant matter into coastal waters is important to regional and global biogeochemical cycles, and methods for assessing and predicting fluxes in such dynamic environments are needed. We investigated the hypothesis that upon reconnection of a floodplain wetland to its mainstem river, organic matter produced in the wetland would reach other parts of the ecosystem. If so, we can infer that the organic matter would ultimately become a source for the food web in the mainstem river and estuary. To accomplish this, we adapted numerical hydrodynamic and transport modeling methods to estimate the mass of particulate organic matter (POM) derived from the annually senescent aboveground parts of herbaceous marsh plants (H-POM). The Finite-Volume Community Ocean Model (FVCOM), parameterized with flow, tide, and aboveground biomass data, simulated H-POM mobilization from fluid shear stress during tidal exchange, flooding, and variable river flow; entrainment into the water column; transport via channel and overland flow; and entrapment when wetted surfaces dry. We examined export from a recently reconnected, restoring tidal emergent marsh on the Grays River, a tributary to the Columbia River estuary. Modeling indicated that hydrologically reconnecting 65 ha at the site resulted in export of about 96 × 103  kg of H-POM, primarily during pulsed storm flooding events in autumn and early winter. This exported mass amounted to about 19% of the summer peak aboveground biomass measured at the site. Of that 19%, about 48% (47 × 103  kg) was deposited downstream in the Grays River and floodplain wetlands, and the remaining 52% (50 × 103  kg) passed the confluence of the Grays River and the mainstem estuary located about 7 km from the study site. The colonization of the restoring study site largely by nonnative Phalaris arundinacea (reed canarygrass) may have resulted in 18-28% lower H-POM mobilization than typical marsh plant communities on this floodplain, based on estimates from regional studies of marshes dominated by less recalcitrant species. We concluded that restored floodplain wetlands can contribute significant amounts of organic matter to the estuarine ecosystem and thereby contribute to the restoration of historical trophic structure.

Changes in species composition of water insects during 25-year monitoring of the Danube floodplains affected by the Gabcíkovo waterworks.

This study was based on the complex environmental monitoring of the Danube River in Slovakia. Out of 23 monitored sites, three were chosen for the aim of this study. The three sites were sampled regularly three times per year during a 25-year period, which started in 1990, 2 years before the Gabcíkovo waterworks became operational. Each site represented one of the main potamal types, which we recognised according to the habitat characteristics as eupotamal, parapotamal and plesiopotamal. In order to assess changes which occurred during the 25 years, we studied taxocoenoses of Ephemeroptera, Plecoptera and Trichoptera, which sensitively reflect anthropogenic impacts. The changes caused by the Gabcíkovo waterworks manifested in a decrease of the species diversity in eupotamal because of the disappearance of rheophilous taxa. In the parapotamal and plesiopotamal sites, the initial decrease was followed by a partial recovery of biodiversity due to the artificial floodings. After they were stopped, the decrease occurred again and until 2015, the insect taxa became rare. Conversely, in the eupotamal site, there was an increasing trend in biodiversity since 2011 until 2015. Overall increase in the floodplain index values indicated a loss of connectivity between the floodplain habitats and their fauna with the main channel during the last 25 years. In this paper, we also extended the floodplain index with data on habitat values and indication weight for several stonefly species.

Decreased hemispheric connectivity and decreased intra- and inter- hemisphere asymmetry of resting state functional network connectivity in schizophrenia.

Many studies have shown that schizophrenia patients have aberrant functional network connectivity (FNC) among brain regions, suggesting schizophrenia manifests with significantly diminished (in majority of the cases) connectivity. Schizophrenia is also associated with a lack of hemispheric lateralization. Hoptman et al. (2012) reported lower inter-hemispheric connectivity in schizophrenia patients compared to controls using voxel-mirrored homotopic connectivity. In this study, we merge these two points of views together using a group independent component analysis (gICA)-based approach to generate hemisphere-specific timecourses and calculate intra-hemisphere and inter-hemisphere FNC on a resting state fMRI dataset consisting of age- and gender-balanced 151 schizophrenia patients and 163 healthy controls. We analyzed the group differences between patients and healthy controls in each type of FNC measures along with age and gender effects. The results reveal that FNC in schizophrenia patients shows less hemispheric asymmetry compared to that of the healthy controls. We also found a decrease in connectivity in all FNC types such as intra-left (L_FNC), intra-right (R_FNC) and inter-hemisphere (Inter_FNC) in the schizophrenia patients relative to healthy controls, but general patterns of connectivity were preserved in patients. Analyses of age and gender effects yielded results similar to those reported in whole brain FNC studies.