@article {1759,
	title = {Canonical genetic signatures of the adult human brain},
	journal = {Nature Neuroscience},
	volume = {18},
	year = {2015},
	pages = {1844},
	chapter = {1832},
	abstract = {<div>The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry</div>
},
	author = {Michael Hawrylycz and Jeremy A Miller and Vilas Menon and David Feng and Tim Dolbeare and Angela L Guillozet-Bongaarts and Anil G Jegga and Bruce J Aronow and Chang-Kyu Lee and Amy Bernard and Matthew F Glasser and Donna L Dierker and J{\"o}rge Menche and Aaron Szafer and Forrest Collman and Pascal Grange and Kenneth A Berman and Stefan Mihalas and Zizhen Yao and Lance Stewart and Albert-L{\'a}szl{\'o} Barab{\'a}si and Jay Schulkin and John Phillips and Lydia Ng and Chinh Dang and David R Haynor and Allan Jones and David C Van Essen and Christof Koch and Ed Lein}
}
@article {1800,
	title = {Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM)},
	year = {2015},
	month = {09/19/2015},
	type = {Poster},
	abstract = {<p>The Cortical Activity Map will provide neural responses from large sets of simultaneously recorded cells to a diverse set of visual stimuli from awake, behaving mice in multiple layers, regions, and cell types. This data set allows for unprecedented access to population responses and provides a unique opportunity to explore the collective characteristics of neural dynamics. The visual stimuli for CAM include gratings, sparse noise, spatio-temporal noise, simple objects, natural images, and natural movies. We demonstrate the power of this data set by exploring the nature of population coding in the visual system. To assess information processing across visual areas we develop decoders. We analyze and compare the performance of these decoders for each stimulus type. In particular, we compare the performance of correlation based decoders and those with functional connectivity to independent decoders. For more complex stimuli, we use these models as the basis for reconstruction of the visual stimulus. This set of analyses demonstrates that the Cortical Activity Map will be a powerful tool for exploring the joint activity of large populations of neurons.</p>
},
	author = {Michael Buice and Saskia de Vries},
	editor = {Amy Bernard and Brandon Rogeres and Casey White and Chinh Dang and Pete Groblewski and Chris Lau and Cliff Slaughterbeck and Colin Farrell and Derric Williams and Jack Waters and Jed Perkins and Kate Roll and Leonard Kuan and Lydia Ng and Marina Garrett and Natalia Orlova and Shawn Olsen and Sissy Cross and Stefan Mihalas and Thomas Keenan and Wayne Wakeman and John Phillips and Christof Koch and Clay Reid}
}
