Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

The paper presents a neurorobotics cognitive model explaining the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. The dataset used for training was obtained from object manipulation tasks with a humanoid robot platform; it includes 9 motor actions and 9 objects placing placed in 6 different locations), which enables the robot to learn to handle real-world objects and actions. Based on the multiple time-scale recurrent neural networks, this study demonstrates its generalisation capability using a large data-set, with which the robot was able to generalise semantic representation of novel combinations of noun-verb sentences, and therefore produce the corresponding motor behaviours. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network

A comparison of methods to assess the antimicrobial activity of nanoparticle combinations on bacterial cells

Copyright: © 2018 Bankier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BACKGROUND: Bacterial cell quantification after exposure to antimicrobial compounds varies widely throughout industry and healthcare. Numerous methods are employed to quantify these antimicrobial effects. With increasing demand for new preventative methods for disease control, we aimed to compare and assess common analytical methods used to determine antimicrobial effects of novel nanoparticle combinations on two different pathogens. METHODS: Plate counts of total viable cells, flow cytometry (LIVE/DEAD BacLight viability assay) and qPCR (viability qPCR) were used to assess the antimicrobial activity of engineered nanoparticle combinations (NPCs) on Gram-positive (Staphylococcus aureus) and Gram-negative (Pseudomonas aeruginosa) bacteria at different concentrations (0.05, 0.10 and 0.25 w/v%). Results were analysed using linear models to assess the effectiveness of different treatments. RESULTS: Strong antimicrobial effects of the three NPCs (AMNP0-2) on both pathogens could be quantified using the plate count method and flow cytometry. The plate count method showed a high log reduction (>8-log) for bacteria exposed to high NPC concentrations. We found similar antimicrobial results using the flow cytometry live/dead assay. Viability qPCR analysis of antimicrobial activity could not be quantified due to interference of NPCs with qPCR amplification. CONCLUSION: Flow cytometry was determined to be the best method to measure antimicrobial activity of the novel NPCs due to high-throughput, rapid and quantifiable results.Peer reviewe

Evolving Synaptic Plasticity with an Evolutionary Cellular Development Model

Since synaptic plasticity is regarded as a potential mechanism for memory formation and learning, there is growing interest in the study of its underlying mechanisms. Recently several evolutionary models of cellular development have been presented, but none have been shown to be able to evolve a range of biological synaptic plasticity regimes. In this paper we present a biologically plausible evolutionary cellular development model and test its ability to evolve different biological synaptic plasticity regimes. The core of the model is a genomic and proteomic regulation network which controls cells and their neurites in a 2D environment. The model has previously been shown to successfully evolve behaving organisms, enable gene related phenomena, and produce biological neural mechanisms such as temporal representations. Several experiments are described in which the model evolves different synaptic plasticity regimes using a direct fitness function. Other experiments examine the ability of the model to evolve simple plasticity regimes in a task -based fitness function environment. These results suggest that such evolutionary cellular development models have the potential to be used as a research tool for investigating the evolutionary aspects of synaptic plasticity and at the same time can serve as the basis for novel artificial computational systems

Language development after cochlear implantation: an epigenetic model

Growing evidence supports the notion that dynamic gene expression, subject to epigenetic control, organizes multiple influences to enable a child to learn to listen and to talk. Here, we review neurobiological and genetic influences on spoken language development in the context of results of a longitudinal trial of cochlear implantation of young children with severe to profound sensorineural hearing loss in the Childhood Development after Cochlear Implantation study. We specifically examine the results of cochlear implantation in participants who were congenitally deaf (N = 116). Prior to intervention, these participants were subject to naturally imposed constraints in sensory (acoustic–phonologic) inputs during critical phases of development when spoken language skills are typically achieved rapidly. Their candidacy for a cochlear implant was prompted by delays (n = 20) or an essential absence of spoken language acquisition (n = 96). Observations thus present an opportunity to evaluate the impact of factors that influence the emergence of spoken language, particularly in the context of hearing restoration in sensitive periods for language acquisition. Outcomes demonstrate considerable variation in spoken language learning, although significant advantages exist for the congenitally deaf children implanted prior to 18 months of age. While age at implantation carries high predictive value in forecasting performance on measures of spoken language, several factors show significant association, particularly those related to parent–child interactions. Importantly, the significance of environmental variables in their predictive value for language development varies with age at implantation. These observations are considered in the context of an epigenetic model in which dynamic genomic expression can modulate aspects of auditory learning, offering insights into factors that can influence a child’s acquisition of spoken language after cochlear implantation. Increased understanding of these interactions could lead to targeted interventions that interact with the epigenome to influence language outcomes with intervention, particularly in periods in which development is subject to time-sensitive experience