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Effects of spike frequency adaptation (SFA) on the synchronous behavior of population neurons are investigated in electrically coupled networks with a scale-free property. By a computational approach, we corroborate that pairwise correlations between neurons would decrease if neurons exhibit the feature of SFA, which is similar to previous experimental observations. However, unlike the case of pairwise correlations, population activities of neurons show a rather complex variation mode: compared with those of non-adapted neurons, neurons in the networks having weak-degrees of SFA will impair population synchronizations; while neurons exhibiting strong-degrees of SFA will enhance population synchronizations. Moreover, a variation of coupling strength between neurons will not alter this phenomenon significantly, unless the coupling strength is too weak. Our results suggest that synchronous activity of electrically coupled population neurons is adaptation-dependent, and this adaptive feature may imply some coding strategies of neuronal populations. [ABSTRACT FROM AUTHOR]

Studies have suggested that thalamus is involved in temporal lobe epilepsy, but the role of thalamus is still unclear. We obtained local filed potentials (LFPs) and single-unit activities from CA1 of hippocampus and parafascicular nucleus of thalamus during the development of epileptic seizures induced by pilocarpine in mice. Two measures, redundancy and directionality index, were used to analyze the electrophysiological characters of neuronal activities and the information flow between thalamus and hippocampus. We found that LFPs became more regular during the seizure in both hippocampus and thalamus, and in some cases LFPs showed a transient disorder at seizure onset. The variation tendency of the peak values of cross-correlation function between neurons matched the variation tendency of the redundancy of LFPs. The information tended to flow from thalamus to hippocampus during seizure initiation period no matter what the information flow direction was before the seizure. In some cases the information flow was symmetrically bidirectional, but none was found in which the information flowed from hippocampus to thalamus during the seizure initiation period. In addition, inactivation of thalamus by tetrodotoxin (TTX) resulted in a suppression of seizures. These results suggest that thalamus may play an important role in the initiation of epileptic seizures. [ABSTRACT FROM AUTHOR]

The hippocampus plays an important role in the genesis of mesial temporal lobe epilepsy, and the entorhinal cortex (EC) may affect the hippocampal network activity because of the heavy interconnection between them. However, the mechanism by which the EC affects the discharge patterns and the transmission mode of epileptiform discharges within the hippocampus needs further study. Here,multielectrode recording techniques were used to study the spatiotemporal characteristics of epileptiform discharges in adult mouse hippocampal slices and combined EC-hippocampal slices and determine whether and how the EC affects the hippocampal neuron discharge patterns. The results showed that low-Mg2+ artificial cerebrospinal fluid induced interictal discharges in hippocampal slices, whereas, in combined EC-hippocampal slices the discharge pattern was alternated between interictal and ictal discharges, and ictal discharges initiated in the EC and propagated to the hippocampus. The pharmacological effect of the antiepileptic drug valproate (VPA) was tested. VPA reversibly suppressed the frequency of interictal discharges but did not change the initiation site and propagation speed, and it completely blocked ictal discharges. Our results suggested that EC was necessary for the hippocampal ictal discharges, and ictal discharges were more sensitive than interictal discharges in response to VPA. [ABSTRACT FROM AUTHOR]

Spike sorting is the mandatory first step in analyzing multiunit recording signals for studying information processing mechanisms within the nervous system. Extracellular recordings usually contain overlapped spikes produced by a number of neurons adjacent to the electrode, together with unknown background noise, which in turn induce some difficulties in neural signal identification. In this paper, we propose a robust method to deal with these problems, which employs an automatic overlap decomposition technique based on the relaxation algorithm that requires simple fast Fourier transforms. The performance of the presented system was tested at various signal-to-noise ratio levels based on synthetic data that were generated from real recordings. [ABSTRACT FROM AUTHOR]