Hetereogeneity in Neuronal Intrinsic Properties: A Possible Mechanism for Hub-Like Properties of the Rat Anterior Cingulate Cortex during Network Activity.

The anterior cingulate cortex (ACC) is vital for a range of brain functions requiring cognitive control and has highly divergent inputs and outputs, thus manifesting as a hub in connectomic analyses. Studies show diverse functional interactions within the ACC are associated with network oscillations in the β (20-30 Hz) and γ (30-80 Hz) frequency range. Oscillations permit dynamic routing of information within cortex, a function that depends on bandpass filter-like behavior to selectively respond to specific inputs. However, a putative hub region such as ACC needs to be able to combine inputs from multiple sources rather than select a single input at the expense of others. To address this potential functional dichotomy, we modeled local ACC network dynamics in the rat in vitro. Modal peak oscillation frequencies in the β- and γ-frequency band corresponded to GABAAergic synaptic kinetics as seen in other regions; however, the intrinsic properties of ACC principal neurons were highly diverse. Computational modeling predicted that this neuronal response diversity broadened the bandwidth for filtering rhythmic inputs and supported combination-rather than selection-of different frequencies within the canonical γ and β electroencephalograph bands. These findings suggest that oscillating neuronal populations can support either response selection (routing) or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances.Wellcome Trus

Contrasting consequences of climate change for migratory geese:Predation, density dependence and carryover effects offset benefits of high-arctic warming

Climate change is most rapid in the Arctic, posing both benefits and challenges for migratory herbivores. However, population-dynamic responses to climate change are generally difficult to predict, due to concurrent changes in other trophic levels. Migratory species are also exposed to contrasting climate trends and density regimes over the annual cycle. Thus, determining how climate change impacts their population dynamics requires an understanding of how weather directly or indirectly (through trophic interactions and carryover effects) affects reproduction and survival across migratory stages, while accounting for density dependence. Here, we analyse the overall implications of climate change for a local non-hunted population of high-arctic Svalbard barnacle geese, Branta leucopsis, using 28 years of individual-based data. By identifying the main drivers of reproductive stages (egg production, hatching and fledging) and age-specific survival rates, we quantify their impact on population growth. Recent climate change in Svalbard enhanced egg production and hatching success through positive effects of advanced spring onset (snow melt) and warmer summers (i.e. earlier vegetation green-up) respectively. Contrastingly, there was a strong temporal decline in fledging probability due to increased local abundance of the Arctic fox, the main predator. While weather during the non-breeding season influenced geese through a positive effect of temperature (UK wintering grounds) on adult survival and a positive carryover effect of rainfall (spring stopover site in Norway) on egg production, these covariates showed no temporal trends. However, density-dependent effects occurred throughout the annual cycle, and the steadily increasing total flyway population size caused negative trends in overwinter survival and carryover effects on egg production. The combination of density-dependent processes and direct and indirect climate change effects across life history stages appeared to stabilize local population size. Our study emphasizes the need for holistic approaches when studying population-dynamic responses to global change in migratory species.</p

Dynamic Configuration of Large-Scale Cortical Networks: A Useful Framework for Clarifying the Heterogeneity Found in Attention-Deficit/Hyperactivity Disorder

The heterogeneity of attention-deficit/hyperactivity disorder(ADHD) traits (inattention vs. hyperactivity/impulsivity) complicates diagnosis and intervention. Identifying how the configuration of large-scale functional brain networks during cognitive processing correlate with this heterogeneity could help us understand the neural mechanisms altered across ADHD presentations. Here, we recorded high-density EEG while 62 non-clinical participants (ages 18-24; 32 male) underwent an inhibitory control task (Go/No-Go). Functional EEG networks were created using sensors as nodes and across-trial phase-lag index values as edges. Using cross-validated LASSO regression, we examined whether graph-theory metrics applied to both static networks (averaged across time-windows: -500–0ms, 0–500ms) and dynamic networks (temporally layered with 2ms intervals), were associated with hyperactive/impulsive and inattentive traits. Network configuration during response execution/inhibition was associated with hyperactive/impulsive (mean R2across test sets = .20, SE = .02), but not inattentive traits. Post-stimulus results at higher frequencies (Beta, 14-29Hz; Gamma, 30-90Hz) showed the strongest association with hyperactive/impulsive traits, and predominantly reflected less burst-like integration between modules in oscillatory beta networks during execution, and increased integration/small-worldness in oscillatory gamma networks during inhibition. We interpret the beta network results as reflecting weaker integration between specialized pre-frontal and motor systems during motor response preparation, and the gamma results as reflecting a compensatory mechanism used to integrate processing between less functionally specialized networks. This research demonstrates that the neural network mechanisms underlying response execution/inhibition might be associated with hyperactive/impulsive traits, and that dynamic, task-related changes in EEG functional networks may be useful in disentangling ADHD heterogeneity

Diversity, variability and persistence elements for a non-equilibrium theory of eco-evolutionary dynamics

Natural ecosystems persist in variable environments by virtue of a suite of traits that span from the individual to the community, and from the ecological to the evolutionary scenarios. How these internal characteristics operate to allow living beings to cope with the uncertainty present in their environments is the subject matter of quantitative theoretical ecology. Under the framework of structural realism, the present dissertation project has advocated for the strategy of mathematical modeling as a strategy of abstraction. The goal is to explore if a range of natural ecosystems display the features of complex systems, and evaluate whether these features provide insights into how they persist in their current environments, and how might they cope with changing environments in the future. A suite of inverse, linear and non-linear dynamical mathematical models, including non-equilibrium catastrophe models, and structured demographic approaches is applied to five case studies of natural systems fluctuating in the long-term in diverse scenarios: phytoplankton in the global ocean, a mixotrophic plankton food web in a marine coastal environment, a wintering waterfowl community in a major Mediterranean biodiversity hot-spot, a breeding colony of a keystone avian scavenger in a mountainous environment and the shorebird community inhabiting the coast of UK. In all case studies, there is strong evidence that ecosystems are able to closely track their common environment through several strategies. For example, in global phytoplankton communities, a latitudinal gradient in the positive impact of functional diversity on community stability counteracts the increasing environmental variability with latitude. Mixotrophy, by linking several feeding strategies in a food web, internally drives community dynamics to the edge of instability while maximizing network complexity. In contrast, an externally generated major perturbation, operating through planetary climatic disruptions, induce an abrupt regime shift between alternative stable states in the wintering waterfowl community. Overall, the natural systems studied are shown to posses features of complex systems: connectivity, autonomy, emergence, non-equilibrium, non-linearity, self-organization and coevolution. In rapidly changing environments, these features are hypothesized to allow natural system to robustly respond to stress and disturbances to a large extent. At the same time, future scenarios will be probably characterized by conditions never experienced before by the studied systems. How will they respond to them, is an open question. Based on the results of this dissertation, future research directions in theoretical quantitative ecology will likely benefit from non-autonomous dynamical system approaches, where model parameters are a function of time, and from the deeper exploration of global attractors and the non-equilibriumness of dynamical systems

Predictive coding with spiking neurons and feedforward gist signaling

Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature