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Guimaraes, R.

Publications and source records attributed to Guimaraes, R..

2 recordsLinked to original sources

Rapid spatial cognition in mice, with and without neocortex and hippocampus

Rapid learning, memory, and generalization are often attributed to circuits of the neocortex and hippocampus, but their specific role remains unclear. To examine these cognitive abilities together in individual mice, we observed mice navigating the Manhattan Maze, a reconfigurable 3D labyrinth. Naive wildtype mice improved within two rewards, approached efficient paths within about 20 rewards, retained a 9-turn route overnight, and learned faster in new configurations. Much of the few-shot improvement follows from a rule-based forward bias that emerged even before any reward. In a maze with loops, where that rule is less useful, mice learned within a few rewards to prefer one bottleneck corridor far from the reward location, while choices elsewhere stayed flexible. To begin linking these different components of learning to brain function, we presented the same task to mutant mice that lack the hippocampus and most of the neocortex. They were impaired during initial exploration, where repetitive scanning made them about 3-fold slower to obtain the first few rewards. Past that stage, learning, retention over weeks to months, and generalization to new mazes were largely preserved. Learning this fast is hard to reconcile with trial and error, in which value propagates backwards from the reward. A neuromorphic circuit model instead accounts for the bottleneck choice: it builds a map of the environment without reward, and needs neither cortical nor hippocampal circuit motifs. Therefore, learning is possible without the involvement of neocortex and hippocampus. Structure learned before the first reward, rather than the reward itself, may be what makes few-shot learning possible.

neuroscience↗

Ultra-fast joint-genotyping with SparkGOR

MotivationOur aim was to simplify and speedup joint-genotyping, from sequence based variation data of individual samples, while maintaining as high sensitivity and specificity as possible. ResultsWe have leveraged versatile GOR data structures to store biallelic representations of variants and sequence read coverage in a very efficient way, allowing for very fast joint-genotyping that is an order of magnitude faster than any joint-genotyping method published to date. Furthermore, it can be easily extended and executed much faster in an incremental fashion. Concordance analysis based on the Genome In A Bottle (GIAB) samples shows favorable results when compared with the de-facto standard approach, using gVCF files and GATK joint-calling. Additionally, we have developed variant quality classification using XGBoost and variant training sets derived from the GIAB samples. The entire business logic is implemented efficiently and concisely in SparkGOR. AvailabilitySparkGOR is open-source and freely available at https://github.com/gorpipe. Contacthakon@genuitysci.com

bioinformatics↗