Active hippocampal networks undergo spontaneous synaptic modification.

• Main Authors: Masako Tsukamoto-Yasui, Takuya Sasaki, Wataru Matsumoto, Ayako Hasegawa, Takeshi Toyoda, Atsushi Usami, Yuichi Kubota, Taku Ochiai, Tomokatsu Hori, Norio Matsuki, Yuji Ikegaya
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2007-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC2082078?pdf=render

The brain is self-writable; as the brain voluntarily adapts itself to a changing environment, the neural circuitry rearranges its functional connectivity by referring to its own activity. How the internal activity modifies synaptic weights is largely unknown, however. Here we report that spontaneous activity causes complex reorganization of synaptic connectivity without any external (or artificial) stimuli. Under physiologically relevant ionic conditions, CA3 pyramidal cells in hippocampal slices displayed spontaneous spikes with bistable slow oscillations of membrane potential, alternating between the so-called UP and DOWN states. The generation of slow oscillations did not require fast synaptic transmission, but their patterns were coordinated by local circuit activity. In the course of generating spontaneous activity, individual neurons acquired bidirectional long-lasting synaptic modification. The spontaneous synaptic plasticity depended on a rise in intracellular calcium concentrations of postsynaptic cells, but not on NMDA receptor activity. The direction and amount of the plasticity varied depending on slow oscillation patterns and synapse locations, and thus, they were diverse in a network. Once this global synaptic refinement occurred, the same neurons now displayed different patterns of spontaneous activity, which in turn exhibited different levels of synaptic plasticity. Thus, active networks continuously update their internal states through ongoing synaptic plasticity. With computational simulations, we suggest that with this slow oscillation-induced plasticity, a recurrent network converges on a more specific state, compared to that with spike timing-dependent plasticity alone.