The acreage planted in corn and soybean crops is vast, and these crops contribute substantially to the world economy. The agricultural practices employed for farming these crops have major effects on ecosystem health at a worldwide scale. The microbial communities living in agricultural soils significantly contribute to nutrient uptake and cycling and can have both positive and negative impacts on the crops growing with them. In this study, we examined the impact of the crop planted and soil tillage on nutrient levels, microbial communities, and the biochemical pathways present in the soil. We found that farming practice, that is conventional tillage versus no‐till, had a much greater impact on nearly everything measured compared to the crop planted. No‐till fields tended to have higher nutrient levels and distinct microbial communities. Moreover, no‐till fields had more DNA sequences associated with key nitrogen cycle processes, suggesting that the microbial communities were more active in cycling nitrogen. Our results indicate that tilling of agricultural soil may magnify the degree of nutrient waste and runoff by altering nutrient cycles through changes to microbial communities. Currently, a minority of acreage is maintained without tillage despite clear benefits to soil nutrient levels, and a decrease in nutrient runoff—both of which have ecosystem‐level effects and both direct and indirect effects on humans and other organisms.

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Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

A genetic ,'dgorithm is used to select the inputs to A neural network function ApproximAtor. lit the application considered, modeling criticM parameters of the Space Shuttle Main Engine (SSME), the functional rel,_tionslfip between mea._ured parameters is unknown and coxuplex. Furthermore, the number of possible input parameters is quite large. MAlty approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due the optimization altd space searching capabities of genetic Mgorithms they were employed in this paper to systematize the input selection process. The results suggest that the genetic Mgorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

In earlier work, we derived the dynamical behavior of a network of oscillatory units described by the amplitude and phase of oscillations. The dynamics were derived from an objective function that rewards both the faithfulness and the sparseness of representation. After unsupervised learning, the network is capable of separating mixtures of inputs, and also segmenting the inputs into components that most contribute to the classification of a given input object.In the current paper, we extend our analysis to multi-layer networks, and demonstrate that the dynamical equations derived earlier can be successfully applied to multi-layer networks. The topological connectivity between the different layers are derived from biological observations in primate visual cortex, and consist of receptive fields that are topographically mapped between layers. We explore the role of feedback connections, and show that increasing the diffusivity of feedback connections significantly improves segmentation performance, but does not affect separation performance.

One of the important features of the human visual system is that it is able to recognize objects in a scale and translational invariant manner. However, achieving this desirable behavior through biologically realistic networks is a challenge.The synchronization of neuronal firing patterns has been suggested as a possible solution to the binding problem (where a biological mechanism is sought to explain how features that represent an object can be scattered across a network, and yet be unified). This observation has led to neurons being modeled as oscillatory dynamical units. It is possible for a network of these dynamical units to exhibit synchronized oscillations under the right conditions. These network models have been applied to solve signal deconvolution or blind source separation problems. However, the use of the same network to achieve properties that the visual sytem exhibits, such as scale and translational invariance have not been fully explored.Some approaches investigated in the literature (Wallis, 1996) involve the use of non-oscillatory elements that are arranged in a hierarchy of layers. The objects presented are allowed to move, and the network utilizes a trace learning rule, where a time averaged output value is used to perform Hebbian learning with respect to the input value. This is a modification of the standard Hebbian learning rule, which typically uses instantaneous values of the input and output.In this paper we present a network of oscillatory amplitude-phase units connected in two layers. The types of connections include feedforward, feedback and lateral. The network consists of amplitude-phase units that can exhibit synchronized oscillations. We have previously shown that such a network can segment the components of each input object that most contribute to its classification. Learning is unsupervised and based on a Hebbian update, and the architecture is very simple.We extend the ability of this network to address the problem of translational invariance. We show that by adopting a specific treatment of the phase values of the output layer, the network exhibits translational invariant object representation. The scheme used in training is as follows. The network is presented with an input, which then moves. During the motion the amplitude and phase of the upper layer units is not reset, but continues with the past value before the introduction of the object in the new position. Only the input layer is changed instantaneously to reflect the moving object. The network behavior is such that it categorizes the translated objects with the same label as the stationary object, thus establishing an invariant categorization with respect to translation. This is a promising result as it uses the same framework of oscillatory units that achieves synchrony, and introduces motion to achieve translational invariance.

We present a modelling framework for cortical processing aimed at understanding how, maintaining biological plausibility, neural network models can: (a) approximate general inference algorithms like belief propagation, combining bottom-up and top-down information, (b) solve Rosenblatt's classical superposition problem, which we link to the binding problem, and (c) do so based on an unsupervised learning approach. The framework leads to two related models: the first model shows that the use of top-down feedback significantly improves the network's ability to perform inference of corrupted inputs; the second model, including oscillatory behavior in the processing units, shows that the superposition problem can be efficiently solved based on the unit's phases.