With this model, we conducted a simulation study of the gain adaptation of optokinetic response (OKR) eye movement.
Our model reproduced several important aspects of previously reported experimental results in wild-type and cerebellum-related gene-manipulated mice.
Although many coffee-guzzling late night-studying students will claim otherwise, one of the classic pieces of academic advice is 'sleep will improve your memory'.
Whilst some claims, such as remembering information by listening to tapes while asleep, may be tenuous, there's clear evidence for the role of sleep in memory consolidation.
To examine this possibility, we formulated a simple model of the cerebellum with a minimal number of components based on its known anatomy and physiology, implementing both LTD and long-term potentiation (LTP) at PF–PC synapses and mossy fiber–vestibular nuclear neuron (MF–VN) synapses.
Advanced Index Compression works well on all supported indexes, including those indexes that are not good candidates for the existing prefix compression feature; including indexes with no, or few, duplicate values in the leading columns of the index.
Advanced Index Compression improves the compression ratios significantly while still providing efficient access to the index. Processing of large volumes of data is significantly faster than the exact aggregation, especially for data sets with a large number of distinct values, with negligible deviation from the exact result.
Computer simulations with this model closely reproduced learning behavior for oculomotor reflexes in wild-type mice and explained peculiar learning behavior in genetically manipulated mice.
Long-term depression (LTD) at parallel fiber–Purkinje cell (PF–PC) synapses is thought to underlie memory formation in cerebellar motor learning.
D., The Johns Hopkins University School of Medicine Pierre J.