Glutamate infused posttraining into the hippocampus or caudate-putamen differentially strengthens place and response learning

A cross-maze task that can be acquired through either place or response learning was used to examine the hypothesis that posttraining neurochemical manipulation of the hippocampus or caudate-putamen can bias an animal toward the use of a specific memory system. Male Long-Evans rats received four trials per day for 7 days, a probe trial on day 8, further training on days 9–15, and an additional probe trial on day 16. Training occurred in a cross-maze task in which rats started from a consistent start-box (south), and obtained food from a consistent goal-arm (west). On days 4–6 of training, rats received posttraining intrahippocampal (1 μg/0.5 μl) or intracaudate (2 μg/0.5 μl) injections of either glutamate or saline (0.5 μl). On days 8 and 16, a probe trial was given in which rats were placed in a novel start-box (north). Rats selecting the west goal-arm were designated “place” learners, and those selecting the east goal-arm were designated “response” learners. Saline-treated rats predominantly displayed place learning on day 8 and response learning on day 16, indicating a shift in control of learned behavior with extended training. Rats receiving intrahippocampal injections of glutamate predominantly displayed place learning on days 8 and 16, indicating that manipulation of the hippocampus produced a blockade of the shift to response learning. Rats receiving intracaudate injections of glutamate displayed response learning on days 8 and 16, indicating an accelerated shift to response learning. The findings suggest that posttraining intracerebral glutamate infusions can (i) modulate the distinct memory processes mediated by the hippocampus and caudate-putamen and (ii) bias the brain toward the use of a specific memory system to control learned behavior and thereby influence the timing of the switch from the use of cognitive memory to habit learning to guide behavior.

Statistical modeling of large microarray data sets to identify stimulus-response profiles

A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Brain localization for arbitrary stimulus categories: a simple account based on Hebbian learning.

A central theme of cognitive neuroscience is that different parts of the brain perform different functions. Recent evidence from neuropsychology suggests that even the processing of arbitrary stimulus categories that are defined solely by cultural conventions (e.g., letters versus digits) can become spatially segregated in the cerebral cortex. How could the processing of stimulus categories that are not innate and that have no inherent structural differences become segregated? We propose that the temporal clustering of stimuli from a given category interacts with Hebbian learning to lead to functional localization. Neural network simulations bear out this hypothesis.