Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex

Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure—characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain

Identification of criticality in neuronal avalanches: II. A theoretical and empirical investigation of the Driven case

The observation of apparent power laws in neuronal systems has led to the suggestion that the brain is at, or close to, a critical state and may be a self-organised critical system. Within the framework of self-organised criticality a separation of timescales is thought to be crucial for the observation of power-law dynamics and computational models are often constructed with this property. However, this is not necessarily a characteristic of physiological neural networks—external input does not only occur when the network is at rest/a steady state. In this paper we study a simple neuronal network model driven by a continuous external input (i.e. the model does not have an explicit separation of timescales from seeding the system only when in the quiescent state) and analytically tuned to operate in the region of a critical state (it reaches the critical regime exactly in the absence of input—the case studied in the companion paper to this article). The system displays avalanche dynamics in the form of cascades of neuronal firing separated by periods of silence. We observe partial scale-free behaviour in the distribution of avalanche size for low levels of external input. We analytically derive the distributions of waiting times and investigate their temporal behaviour in relation to different levels of external input, showing that the system’s dynamics can exhibit partial long-range temporal correlations. We further show that as the system approaches the critical state by two alternative ‘routes’, different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides

Identification of criticality in neuronal avalanches: I. A theoretical investigation of the non-driven case

In this paper, we study a simple model of a purely excitatory neural network that, by construction, operates at a critical point. This model allows us to consider various markers of criticality and illustrate how they should perform in a finite-size system. By calculating the exact distribution of avalanche sizes, we are able to show that, over a limited range of avalanche sizes which we precisely identify, the distribution has scale free properties but is not a power law. This suggests that it would be inappropriate to dismiss a system as not being critical purely based on an inability to rigorously fit a power law distribution as has been recently advocated. In assessing whether a system, especially a finite-size one, is critical it is thus important to consider other possible markers. We illustrate one of these by showing the divergence of susceptibility as the critical point of the system is approached. Finally, we provide evidence that power laws may underlie other observables of the system that may be more amenable to robust experimental assessment

Qualitative physics in virtual environments

In this paper, we describe a new approach to the creation of virtual environments, which uses qualitative physics to implement object behaviour. We adopted Qualitative Process Theory as a qualitative reasoning formalism, due to its representational properties (e.g., its orientation towards process ontologies and its explicit formulation of process’ pre-conditions). The system we describe is developed using a game engine and takes advantage of its event-based system to integrate qualitative process simulation in an interactive fashion. We use a virtual kitchen as a test environment. In this virtual world, we have implemented various behavioural aspects: physical object behaviour, complex device behaviour (appliances) and “alternative” (i.e. non-realistic) behaviours, which can all be simulated in user real-time. After a presentation of the system architecture and its implementation, we discuss example results from the prototype. This approach has potential applications in simulation and training, as well as in entertainment and digital arts. This work also constitutes a test case for the integration of an Artificial Intelligence technique into 3D user interfaces

A Bayesian method for calibration and aggregation of expert judgement

This paper outlines a Bayesian framework for structured expert judgement (sej) that can be utilised as an alternative to the traditional non-Bayesian methods (including the commonly used Cooke's Classical model [13]). We provide an overview of the structure of an expert judgement study and outline opinion pooling techniques noting the benefits and limitations of these approaches. Some new tractable Bayesian models are highlighted, before presenting our own model which aims to combine and enhance the best of these existing Bayesian frameworks. In particular: clustering, calibrating and then aggregating the experts' judgements utilising a Supra-Bayesian parameter updating approach combined with either an agglomerative hierarchical clustering or an embedded Dirichlet process mixture model. We illustrate the benefit of our approach by analysing data from a number of existing studies in the healthcare domain, specifically in the two contexts of health insurance and transmission risks for chronic wasting disease

Evidence for Active Uptake and Deposition of Si-based defenses in Tall Fescue

Silicon (Si) is taken up from the soil as monosilicic acid by plant roots, transported to leaves and deposited as phytoliths, amorphous silica (SiO2) bodies, which are a key component of anti-herbivore defense in grasses. Silicon transporters have been identified in many plant species, but the mechanisms underpinning Si transport remain poorly understood. Specifically, the extent to which Si uptake is a passive process, driven primarily by transpiration, or has both passive and active components remains disputed. Increases in foliar Si concentration following herbivory suggest plants may exercise some control over Si uptake and distribution. In order to investigate passive and active controls on Si accumulation, we examined both genetic and environmental influences on Si accumulation in the forage grass Festuca arundinacea. We studied three F. arundinacea varieties that differ in the levels of Si they accumulate. Varieties not only differed in Si concentration, but also in increases in Si accumulation in response to leaf damage. The varietal differences in Si concentration generally reflected differences in stomatal density and stomatal conductance, suggesting passive, transpiration-mediated mechanisms underpin these differences. Bagging plants after damage was employed to minimize differences in stomatal conductance between varieties and in response to damage. This treatment eliminated constitutive differences in leaf Si levels, but did not impair the damage-induced increases in Si uptake: damaged, bagged plants still had more leaf Si than undamaged, bagged plants in all three varieties. Preliminary differential gene expression analysis revealed that the active Si transporter Lsi2 was highly expressed in damaged unbagged plants compared with undamaged unbagged plants, suggesting damage-induced Si defenses are regulated at gene level. Our findings suggest that although differences in transpiration may be partially responsible for varietal differences in Si uptake, they cannot explain damage-induced increases in Si uptake and deposition, suggesting that wounding causes changes in Si uptake, distribution and deposition that likely involve active processes and changes in gene expression. Introductio

New behavioural approaches for virtual environments

We describe a new approach to the behaviour of 3D environments that supports the definition of physical processes and interactive phenomena. The work takes as a starting point the traditional event-based architecture that underlies most game engines. These systems discretise the environments' Physics by separating the objects' kinematics from the physical processes corresponding to objects interactions. This property has been used to insert a new behavioural layer, which implements AI-based simulation techniques. We introduce the rationale behind AI-based simulation and the techniques we use for qualitative Physics, as well as a new approach to world behaviour based on the induction of causal impressions. This is illustrated through several examples on a test environment. This approach has implications for the definition of complex world behaviour or non-standard physics, as required in creative applications

The prevalence of constant supportive observations in a high, medium and low secure service

Aims and Method We explored the prevalence and use of constant supportive observations (CSO) in high, medium and low secure in-patient services in a single NHS mental health trust. From clinical records, we extracted data on the length of time on CSO, the reason for the initiation of CSO and associated adverse incidents for all individuals who were placed on CSO between July 2013 and June 2014. Results A small number of individuals accounted for a disproportionately large amount of CSO hours in each setting. Adverse incident rates were higher on CSO than when not on CSO. There was considerable variation between different settings in terms of CSO use and the reasons for commencing CSO. Clinical Implications The study describes the prevalence and nature of CSO in secure forensic mental health services and the associated organisational costs. The marked variation in CSO use between settings suggests that mental health services continue to face challenges in balancing risk management with minimising restrictive interventions