Dept of Sciences biologiques, University of Montreal Qc H3C 3J7, Canada.
Front Biosci (Landmark Ed). 2012 Jan 1;17(2):583-606. doi: 10.2741/3946.
Many studies explored mechanisms through which the brain encodes sensory inputs allowing a coherent behavior. The brain could identify stimuli via a hierarchical stream of activity leading to a cardinal neuron responsive to one particular object. The opportunity to record from numerous neurons offered investigators the capability of examining simultaneously the functioning of many cells. These approaches suggested encoding processes that are parallel rather than serial. Binding the many features of a stimulus may be accomplished through an induced synchronization of cell's action potentials. These interpretations are supported by experimental data and offer many advantages but also several shortcomings. We argue for a coding mechanism based on a sparse synchronization paradigm. We show that synchronization of spikes is a fast and efficient mode to encode the representation of objects based on feature bindings. We introduce the view that sparse synchronization coding presents an interesting venue in probing brain encoding mechanisms as it allows the functional establishment of multi-layered and time-conditioned neuronal networks or multislice networks. We propose a model based on integrate-and-fire spiking neurons.
许多研究探索了大脑对感觉输入进行编码的机制，从而实现了协调的行为。大脑可以通过一个分层的活动流来识别刺激，从而产生一个对特定物体有反应的主要神经元。有机会记录大量神经元为研究人员提供了同时检查许多细胞功能的能力。这些方法表明，编码过程是并行的，而不是串行的。通过诱导细胞动作电位的同步，可以实现对刺激的许多特征的绑定。这些解释得到了实验数据的支持，并提供了许多优势，但也存在一些缺点。我们主张基于稀疏同步范例的编码机制。我们表明，尖峰的同步是一种快速有效的模式，可基于特征绑定来编码对象的表示。我们提出了一种观点，即稀疏同步编码为探测大脑编码机制提供了一个有趣的途径，因为它允许建立多层和时间条件的神经元网络或多片网络。我们提出了一个基于积分和点火尖峰神经元的模型。
