Architecture for Living Neuronal Network Control of a mobile robot

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This paper details the components of the overall animat closed loop system architecture and reports on the evaluation of the results from preliminary real-life and simulated robot experiments.

Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots.

Architecture for neuronal cell control of a mobile robot

It is usually expected that the intelligent controlling mechanism of a robot is a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot - thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. In particular, the use of rodent primary dissociated cultured neuronal networks for the control of mobile `animals' (artificial animals, a contraction of animal and materials) is a novel approach to discovering the computational capabilities of networks of biological neurones. A dissociated culture of this nature requires appropriate embodiment in some form, to enable appropriate development in a controlled environment within which appropriate stimuli may be received via sensory data but ultimate influence over motor actions retained. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animal) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This 'closed loop' interaction with the environment through both sensing and effecting will enable investigation of its learning capacity This paper details the components of the overall animat closed loop system and reports on the evaluation of the results from the experiments being carried out with regard to robot behaviour.

Modular non-computational-connectionist-hybrid neural network approach to robotic systems

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Both black and symbolically white: The 'Bajan-Brit' return migrant as post-colonial hybrid

The research presented in this article centres on an under-researched demographic group of young return migrants, namely, second-generation Barbadians, or 'Bajan-Brits', who have decided to 'return' to the birthplace of their parents. Based on 51 in-depth interviews, the essay examines the experiences of second-generation return migrants from an interpretative perspective framed within post-colonial discourse. The article first considers the Bajan-Brits and issues of race in the UK before their decision to migrate. It is then demonstrated that on 'return', in certain respects, these young, black English migrants occupy a liminal position of cultural, racial and economic privilege, based on their 'symbolic' or 'token' whiteness within the post-colonial context of Barbados. But this very hybridity and inbetweeness means that they also face difficulties and associated feelings of social alienation and discrimination. The ambivalent status of this transnational group of migrants serves to challenge traditional notions of Barbadian racial identity.

Evaluation of two hybrid metric-conceptual models, for simulating phosphorus transfer from agricultural land in the river enborne, a lowland UK catchment

Across Europe, elevated phosphorus (P) concentrations in lowland rivers have made them particularly susceptible to eutrophication. This is compounded in southern and central UK by increasing pressures on water resources, which may be further enhanced by the potential effects of climate change. The EU Water Framework Directive requires an integrated approach to water resources management at the catchment scale and highlights the need for modelling tools that can distinguish relative contributions from multiple nutrient sources and are consistent with the information content of the available data. Two such models are introduced and evaluated within a stochastic framework using daily flow and total phosphorus concentrations recorded in a clay catchment typical of many areas of the lowland UK. Both models disaggregate empirical annual load estimates, derived from land use data, as a function of surface/near surface runoff, generated using a simple conceptual rainfall-runoff model. Estimates of the daily load from agricultural land, together with those from baseflow and point sources, feed into an in-stream routing algorithm. The first model assumes constant concentrations in runoff via surface/near surface pathways and incorporates an additional P store in the river-bed sediments, depleted above a critical discharge, to explicitly simulate resuspension. The second model, which is simpler, simulates P concentrations as a function of surface/near surface runoff, thus emphasising the influence of non-point source loads during flow peaks and mixing of baseflow and point sources during low flows. The temporal consistency of parameter estimates and thus the suitability of each approach is assessed dynamically following a new approach based on Monte-Carlo analysis. (c) 2004 Elsevier B.V. All rights reserved.

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.

Nitrogen fertilizer and seed rate effects on Hagberg failing number of hybrid wheats and their parents are associated with alpha-amylase activity, grain cavity size and dormancy

Field experiments were carried out to assess the effects of nitrogen fertilization and seed rate on the Hagberg falling number (HFN) of commercial wheat hybrids and their parents. Applying nitrogen (200 kg N ha(-1)) increased HFN in two successive years. The HFN of the hybrid Hyno Esta was lower than either of its parents (Estica and Audace), particularly when nitrogen was not applied. Treatment effects on HFN were negatively associated with a-amylase activity. Phadebas grain blotting suggested two populations of grains with different types of a-amylase activity: Estica appeared to have a high proportion of grains with low levels of late maturity endosperm a-amylase activity (LMEA); Audace had a few grains showing high levels of germination amylase; and the hybrid, Hyno Esta, combined the sources from both parents to show heterosis for a-amylase activity. Applying nitrogen reduced both apparent LMEA and germination amylase. The effects on LMEA were associated with the size and disruption of the grain cavity, which was greater in Hyno Esta and Estica and in zero-nitrogen treatments. External grain morphology failed to explain much of the variation in LMEA and cavity size, but there was a close negative correlation between cavity size and protein content. Applying nitrogen increased post-harvest dormancy of the grain. Dormancy was greatest in Estica and least in Audace. It is proposed that effects of seed rate, genotype and nitrogen fertilizer on HFN are mediated through factors affecting the size and disruption of the grain cavity and therefore LMEA, and through factors affecting dormancy and therefore germination amylase. (c) 2004 Society of Chemical Industry.

Effect of two opposing changes in photoperiod upon age at first egg in layer-hybrid pullets

An experiment was designed to test the response of growing pullets to two changes in photoperiod (an increase from 8 to 14 h followed 5 weeks later by the reverse change, or a decrease from 14 to 8 h followed by an increase). The first change was made either at 35 days or at 56 days of age, to test the influence of age on the responses observed. Control groups were kept oil constant 8-h and constant 14-h photoperiods and the responses to appropriate single changes were also tested. Mean age at first egg varied from 111 days for birds given a single increment at 56 days to 166 days for pullets given an increase in photoperiod at 35 days followed by a reduction at 70 days. Responses to the single changes confirmed earlier reports that sensitivity to change in photoperiod varies with age ill a manner that is quantitatively predictable. Responses to the double changes could be explained by Postulating that the initial change altered the 'physiological age' of the bird to all extent that was also quantitatively predictable. An early increase in photoperiod advances sexual development and makes the bird more sensitive to a subsequent decrease than would be expected by reference to its chronological age. An early decrease in photoperiod delays sexual development, which can have the effect of making the bird more or less sensitive to a subsequent increase since, ill layer-strain pullets, sensitivity to an increment in photoperiod normally increases Lip to about 9 weeks of age but decreases thereafter. Mean age at first egg predicted using these concepts was very highly correlated with observed age at first egg. The results provide a rational basis for constructing a model to predict age at first egg for any combination of increases and decreases in photoperiod applied to growing pullets.