(C) 2008 Elsevier Ltd. All rights reserved.”
“Extracellular signal-regulated kinase (ERK) is highly sensitive to regulation by neuronal activity and is critically involved in several forms of synaptic plasticity. These features suggested that alterations in ERK signaling might occur in epilepsy. Previous studies have described
increased ERK phosphorylation immediately after the induction of severe seizures, but patterns of ERK activation in epileptic animals during the chronic period have not been determined. Thus, the localization and abundance of phosphorylated extracellular signal-regulated kinase (pERK) were examined in a pilocarpine model of recurrent seizures in C57BL/6 mice during the seizure-free period and at short intervals after spontaneous seizures. Immunolabeling of pERK in control animals revealed an abundance of distinctly-labeled neurons within the hippocampal Pexidartinib formation. However, in pilocarpine-treated mice during the seizure-free period, the numbers of pERK-labeled neurons were substantially decreased throughout much of the hippocampal formation. Double labeling with a general neuronal marker suggested that the decrease in pERK-labeled neurons was not due primarily to cell loss. The decreased ERK phosphorylation in seizure-prone animals was interpreted
Tideglusib in vitro as a compensatory response to increased neuronal excitability within the network. Nevertheless, striking increases in pERK labeling occurred at the time of spontaneous seizures and were evident in large populations of neurons at very short intervals (as early as 2 min) after detection of a behavioral seizure. These findings suggest that increased selleck chemical pERK labeling could be one of the earliest immunohistochemical indicators
of neurons that are activated at the time of a spontaneous seizure. (C) 2008 IBRO. Published by Elsevier Ltd. All rights reserved.”
“It is still unclear how information is actually stored in biological neural networks. We propose here that information could be first orthogonalized and then stored. This could happen in a manner similar to how a set of vectors is transformed into a set of orthogonalized (i.e. mutually perpendicular) vectors. Orthogonalization may overcome the limits of conventional artificial networks, particularly the catastrophic interference caused by interference between stored inputs. The features needed to allow orthogonalization are common to biological networks, suggesting that it may be a common network mechanism. To illustrate this hypothesis, we characterize the underlying features that an archetypal biological network must have in order to perform orthogonalization, and point out that a number of actual networks show this archetypal network organization. (C) 2008 Elsevier Ltd. All rights reserved.