8/02/2006

Excitatory Reverberations in Hippocampus Culture

A recent article by Lau and Bi (PNAS, 2005) explores the mechanisms that support persistent reverberatory neural activity in rat hippocampal cultures, as a model for how perssitent reverberatory activity may accomplish active maintenance in humans. The authors found that brief stimulation of just a single neuron was enough to evoke reverberatory activity for several seconds across a much larger network. Experimentation with AMPA- and NMDA-blockers strongly suggested that this up-state of activity depends on recurrent excitatory connections, and that it is enhanced if stimulation is paired-pulse, with interpulse intervals of 200-400 ms. Based on further manipulations in which the authors interfered with intracellular calcium storage mechanisms, they suggest that paired pulses facilitate reverbatory activity because it supports the release of calcium.

Some cultured networks, however, did not show the same profile as networks that did successfully reverberate in response to electrical stimulation. The authors hypothesized, and later verified, that this was due to an imbalance in excitatory to inhibitory neurons. The authors also observed that reverberatory activity was typically partially synchronized, with a frequency similar to the theta band, as observed in some working memory research.

The idea that a critical balance between excitatory and inhibitory activity makes unique neural behaviors possible (such as these persistent reverberations) is not new, but it is important that this has been observed in real neurons (as opposed to merely in computational models). The authors found that networks with greater than 10-20% inhibitory neurons were impaired in their ability to persistently reverberate. However, the usual caveats apply: these were rat neurons, not human neurons; these were hippocampal neurons, not prefrontal neurons; and these were grown in a dish, not in a skull.

Related Posts:

Sequential Order in Precise Phase Timing

Models of Active Maintenance as Oscillation

Neural Network Models of the Hippocampus

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