Self-organizing neural plasticity
Guo-Qiang Bi, University of Science and Technology of China

The brain consists of billions of neurons that connect to one another to form complex yet highly ordered networks. A hallmark of these neural networks is that they are capable of reconfiguring themselves according to the history of neuronal activity. This process, termed neural plasticity, often occurring as modifications of inter-neuronal synaptic connections, allows the self-organization of precise brain circuits and to carry out higher functions including cognition, learning and memory.

More than half a century ago, Donald Hebb postulated that a specific rule of synaptic plasticity could allow a group of loosely connected neurons to self-organize into a closed-loop circuit that he called the cell assembly. Within these circuits, characteristic reverberatory neuronal activity can persist after the cessation of input stimuli. Capable of carrying online memory trace, the cell assembly is thought to serve as an elementary unit in thought process and learning.

Although persistent neuronal activity has been observed in vivo, it has been difficult to investigate the exact spatiotemporal dynamics of such activity, its emergence and development, and the underlying role of synaptic plasticity. To address these issues, small networks of interconnected neurons were grown in vitro and studies by patch-clamp recording and optical imaging. These networks exhibit persistent reverberatory activity consisting of specific patterns of population activation. I will discuss the observed dynamic properties of network reverberation, as well as its interaction with synaptic plasticity during its emergence and growth. These observations suggest some basic principles underlying the self-organization of neuronal circuits.

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