Retinal connectomics: towards complete, accurate networks.

Connectomics is a strategy for mapping complex neural networks based on high-speed automated electron optical imaging, computational assembly of neural data volumes, web-based navigational tools to explore 10(12)-10(15) byte (terabyte to petabyte) image volumes, and annotation and markup tools to convert images into rich networks with cellular metadata. These collections of network data and associated metadata, analyzed using tools from graph theory and classification theory, can be merged with classical systems theory, giving a more completely parameterized view of how biologic information processing systems are implemented in retina and brain. Networks have two separable features: topology and connection attributes. The first findings from connectomics strongly validate the idea that the topologies of complete retinal networks are far more complex than the simple schematics that emerged from classical anatomy. In particular, connectomics has permitted an aggressive refactoring of the retinal inner plexiform layer, demonstrating that network function cannot be simply inferred from stratification; exposing the complex geometric rules for inserting different cells into a shared network; revealing unexpected bidirectional signaling pathways between mammalian rod and cone systems; documenting selective feedforward systems, novel candidate signaling architectures, new coupling motifs, and the highly complex architecture of the mammalian AII amacrine cell. This is but the beginning, as the underlying principles of connectomics are readily transferrable to non-neural cell complexes and provide new contexts for assessing intercellular communication.

Removing the frontal lobes: the effects of engaging executive functions on perceptual category learning.

The present study examined the impact of engaging frontal-mediated working memory processes on implicit and explicit category learning. Two stimulus dimensions were relevant to categorization, but in some conditions, a third, irrelevant dimension was also presented. Results indicated that in both implicit and explicit conditions, the inclusion of the irrelevant dimension impaired performance by increasing the reliance on suboptimal unidimensional strategies. With three-dimensional stimuli, a striking dissociation was observed between implicit and explicit category learning when participants performed a sequential working memory task. With explicit category learning, performance was impaired further, and there was an increased use of suboptimal unidimensional strategies. However, with implicit category learning, the performance impairment decreased, and there was an increased use of optimal strategies. These findings demonstrate the paradoxical situation in which learning can be improved under sequential-task conditions and have important implications for training, decision making, and understanding interactive memory systems.