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Changes to sensory experience result in plasticity of synapses in the cortex. This experience-dependent plasticity (EDP) is a fundamental property of the brain. Yet, while much is known about neuronal roles in EDP, very little is known about the role of astrocytes. To address this issue, we used the well-described mouse whiskers-to-barrel cortex system, which expresses a number of forms of EDP. We found that all-whisker deprivation induced characteristic experience-dependent Hebbian depression (EDHD) followed by homeostatic upregulation in L2/3 barrel cortex of wild type mice. However, these changes were not seen in mutant animals (IP3R2–/–) that lack the astrocyte-expressed IP3 receptor subtype. A separate paradigm, the single-whisker experience, induced potentiation of whisker-induced response in both wild-type (WT) mice and IP3R2–/– mice. Recordings in ex vivo barrel cortex slices reflected the in vivo results so that long-term depression (LTD) could not be elicited in slices from IP3R2–/– mice, but long-term potentiation (LTP) could. Interestingly, 1 Hz stimulation inducing LTD in WT paradoxically resulted in NMDAR-dependent LTP in slices from IP3R2–/– animals. The LTD to LTP switch was mimicked by acute buffering astrocytic [Ca2+]i in WT slices. Both WT LTD and IP3R2–/– 1 Hz LTP were mediated by non-ionotropic NMDAR signaling, but only WT LTD was P38 MAPK dependent, indicating an underlying mechanistic switch. These results demonstrate a critical role for astrocytic [Ca2+]i in several EDP mechanisms in neocortex.

Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult to age because they remain morphologically similar once they have gone through the initial transformation upon hatching. However, being able to age them is of interest and importance within the field. This study examined the cuticular hydrocarbons (CHC) of Diptera: Calliphoridae species Lucilia sericata, Calliphora vicina and Calliphora vomitoria. The CHCs were extracted from the cuticles of adult flies and analysed using Gas Chromatography–Mass Spectrometry (GC–MS). The chemical profiles were examined for the two Calliphora species at intervals of day 1, 5, 10, 20 and 30 and up to day 10 for L. sericata. The results show significant chemical changes occurring between the immature and mature adult flies over the extraction period examined in this study. With the aid of a Principal Component Analysis (PCA) and Artificial Neural Networks (ANN), samples were seen to cluster, allowing for the age to be established within the aforementioned time frames. The use of ANNs allowed for the automatic classification of novel samples with very good performance. This was a proof of concept study, which developed a method allowing to age post-emergence adults by using their chemical profiles.

Echo State Networks (ESNs) and a Nonlinear Auto-Regressive Moving Average model with eXogenous inputs (NARMAX) have been applied to multi-sensor time-series data arising from a test footbridge which has been subjected to multiple potentially damaging interventions. The aim of the work was to automatically classify known potentially damaging events, while also allowing engineers to observe and localise any long term damage trends. The techniques reported here used data from ten temperature sensors as inputs and were tasked with predicting the output signal from eight tilt sensors embedded at various points over the bridge. Initially, interventions were identified by both ESNs and NARMAX. In addition, training ESNs using data up to the first event, and determining the ESNs’ subsequent predictions, allowed inferences to be made not only about when and where the interventions occurred, but also the level of damage caused, without requiring any prior data pre-processing or extrapolation. Finally, ESNs were successfully used as classifiers to characterise various different types of intervention that had taken place.

Cuticular hydrocarbons were extracted daily from the larvae of two closely related blowflies Calliphora vicina and Calliphora vomitoria (Diptera: Calliphoridae). The hydrocarbons were then analysed using Gas Chromatography–Mass Spectrometry (GC–MS), with the aim of observing changes within their chemical profiles in order to determine the larval age. The hydrocarbons were examined daily for each species from 1 day old larvae until pupariation. The results show significant chemical changes occurring from the younger larvae to the post-feeding larvae. With the aid of a multivariate statistical method (Principal Component Analysis and Artificial Neural Networks), samples were clustered and classified, allowing for the larval age to be established. Results from this study allowed larvae to be aged to the day with at worst, 87% accuracy, which suggests there is great potential for the use of cuticular hydrocarbons present on larvae to give an indication of their age and hence potentially a valuable tool for minimum PMI estimations.

This article discusses how detecting defects within reinforced concrete is vital to the safety and durability of infrastructure. A non-invasive technique, ElectroMagnetic Anomaly Detection (EMAD) is used in this article to provide information into the electromagnetic properties of reinforcing steel for which data analysis is currently performed visually. The first use of two neural network approaches to automate the analysis of this data is investigated in this article. These approaches are called Echo State Networks (ESNs) and Extreme Learning Machines (ELMs) where fast and efficient training procedures allow networks to be trained and evaluated in less time than traditional neural network approaches. Data collected from real-world concrete structures are analyzed in this article using these two approaches as well as using a simple threshold measure and a standard recurrent neural network. Two ESN architectures provided the best performance for a mesh-reinforced concrete structure, while the ELM approach offers a large improvement in the performance of a single tendon-reinforced structure.

In this article, we report on the application of neural networks to the problem of making an element-specific determination of unknown metal targets based on the characteristics of their transmitted X-ray signatures. Our method was applied to two groups of metal targets that we characterised as light elements (atomic numbers between 40 and 50) and heavy elements (atomic numbers between 73 and 83). In all cases their X-ray signatures were pre-processed; randomly allocated into training and testing datasets; and then presented to a self-organising map neural network in order to perform the element-specific determination. The technique was able to make a correct determination of unknown metal targets with an accuracy of 95% for the heavy elements and 99% for the light elements.

Changing the whisker complement on a rodent’s snout can lead to two forms of experience-dependent plasticity (EDP) in the neurons of the barrel cortex, where whiskers are somatotopically represented. One form, termed coding plasticity, concerns changes in synaptic transmission and connectivity between neurons. This is thought to underlie learning and memory processes and so adaptation to a changing environment. The second, called homeostatic plasticity, serves to maintain a restricted dynamic range of neuronal activity thus preventing its saturation or total downregulation. Current explanatory models of cortical EDP are almost exclusively neurocentric. However, in recent years, increasing evidence has emerged on the role of astrocytes in brain function, including plasticity. Indeed, astrocytes appear as necessary partners of neurons at the core of the mechanisms of coding and homeostatic plasticity recorded in neurons. In addition to neuronal plasticity, several different forms of astrocytic plasticity have recently been discovered. They extend from changes in receptor expression and dynamic changes in morphology to alteration in gliotransmitter release. It is however unclear how astrocytic plasticity contributes to the neuronal EDP. Here, we review the known and possible roles for astrocytes in the barrel cortex, including its plasticity.

In analytical chemistry large datasets are collected using a variety of instruments for multiple tasks, where manual analysis can be time-consuming. Ideally, it is desirable to automate this process while obtaining an acceptable level of accuracy, two aims that artificial neural networks (ANNs) can fulfil. ANNs possess the ability to classify novel data based on their knowledge of the domain to which they have been exposed. ANNs can also analyse non-linear data, tolerate noise within data and are capable of reducing time taken to classify large amounts of novel data once trained, making them well-suited to the field of analytical chemistry where large datasets are present (such as that collected from gas chromatography–mass spectrometry (GC–MS)). In this study, the use of ANNs for the autonomous analysis of GC–MS profiles of Lucilia sericata larvae is investigated, where ANNs are required to estimate the age of the larvae to aid in the estimation of the post mortem interval (PMI). Two ANN analysis approaches are presented, where the ANN correctly classified the data with accuracy scores of 80.8% and 87.7% and Cohen's Kappa coefficients of 0.78 and 0.86. Inspection of these results shows the ANN to confuse two consecutive days which are of the same life stage and as a result are very similar in their chemical profile, which can be expected. The grouping of these two days into one class further improved results where accuracy scores 89% and 97.5% were obtained for the two analysis approaches.

Reservoir Computing is a relatively new field of Recurrent Neural Networks in which only the output weights are re-calculated by the training process, removing the problems associated with traditional gradient descent algorithms. As the reservoir is recurrent, it can possess short term memory, but there is a trade-off between the amount of memory a reservoir can have and its nonlinear mapping capabilities. A new, custom architecture was recently proposed to overcome this by combining a reservoir with an extreme learning machine to deliver improved results. This paper extends this architecture further by introducing a ranking and pruning algorithm which removes neurons according to their significance. This provides further insight into the type of reservoir characteristics needed for a given task, and is supported by further reservoir measures of non-linearity and memory. These techniques are demonstrated on artificial and real world data.