A minimal model of the interaction of social and individual learning.

Learning is thought to be achieved by the selective, activity dependent, adjustment of synaptic connections. Individual learning can also be very hard and/or slow. Social, supervised, learning from others might amplify individual, possibly mainly unsupervised, learning by individuals, and might underlie the development and evolution of culture. We studied a minimal neural network model of the interaction of individual, unsupervised, and social supervised learning by communicating "agents". Individual agents attempted to learn to track a hidden fluctuating "source", which, linearly mixed with other masking fluctuations, generated observable input vectors. In this model data are generated linearly, facilitating mathematical analysis. Learning was driven either solely by direct observation of input data (unsupervised, Hebbian) or, in addition, by observation of another agent's output (supervised, Delta rule). To make learning more difficult, and to enhance biological realism, the learning rules were made slightly connection-inspecific, so that incorrect individual learning sometimes occurs. We found that social interaction can foster both correct and incorrect learning. Useful social learning therefore presumably involves additional factors some of which we outline.

Soil and Foliar Arthropod Abundance and Diversity in Five Cropping Systems in the Coastal Plains of North Carolina.

Soil and foliar arthropod populations in agricultural settings respond to environmental disturbance and degradation, impacting functional biodiversity in agroecosystems. The objective of this study was to evaluate system level management effects on soil and foliar arthropod abundance and diversity in corn and soybean. Our field experiment was a completely randomized block design with three replicates for five farming systems which included: Conventional clean till, conventional long rotation, conventional no-till, organic clean till, and organic reduced till. Soil arthropod sampling was accomplished by pitfall trapping. Foliar arthropod sampling was accomplished by scouting corn and sweep netting soybean. Overall soil arthropod abundance was significantly impacted by cropping in corn and for foliar arthropods in soybeans. Conventional long rotation and organic clean till systems were highest in overall soil arthropod abundance for corn while organic reduced till systems exceeded all other systems for overall foliar arthropod abundance in soybeans. Foliar arthropod abundance over sampling weeks was significantly impacted by cropping system and is suspected to be the result of in-field weed and cover crop cultivation practices. This suggests that the sum of management practices within production systems impact soil and foliar arthropod abundance and diversity and that the effects of these systems are dynamic over the cropping season. Changes in diversity may be explained by weed management practices as sources of disturbance and reduced arthropod refuges via weed reduction. Furthermore, our results suggest agricultural systems lower in management intensity, whether due to organic practices or reduced levels of disturbance, foster greater arthropod diversity.

Hebbian crosstalk prevents nonlinear unsupervised learning.

Learning is thought to occur by localized, activity-induced changes in the strength of synaptic connections between neurons. Recent work has shown that induction of change at one connection can affect changes at others ("crosstalk"). We studied the role of such crosstalk in nonlinear Hebbian learning using a neural network implementation of independent components analysis. We find that there is a sudden qualitative change in the performance of the network at a threshold crosstalk level, and discuss the implications of this for nonlinear learning from higher-order correlations in the neocortex.