Guyonneau Rudy, VanRullen Rufin, Thorpe Simon J
Centre de Recherche Cerveau et Cognition, Toulouse 31000, France.
Neural Comput. 2005 Apr;17(4):859-79. doi: 10.1162/0899766053429390.
Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between input and output spikes. In many brains areas, temporal aspects of spike trains have been found to be highly reproducible. How will STDP affect a neuron's behavior when it is repeatedly presented with the same input spike pattern? We show in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic response latency. This was obtained under various conditions of background noise, and even under conditions where spiking latencies and firing rates, or synchrony, provided conflicting informations. The key role of first spikes demonstrated here provides further support for models using a single wave of spikes to implement rapid neural processing.
尖峰时间依赖性可塑性(STDP)是一种学习规则,它根据输入和输出尖峰之间的精确时间关系来改变神经元突触的强度。在许多脑区,已发现尖峰序列的时间特征具有高度可重复性。当同一输入尖峰模式反复呈现给神经元时,STDP会如何影响其行为呢?我们在这项理论研究中表明,重复输入会系统性地导致神经元选择性的塑造,突出其最初的输入尖峰,同时稳步缩短突触后反应潜伏期。这一结果是在各种背景噪声条件下获得的,甚至在尖峰潜伏期、发放率或同步性提供相互冲突信息的条件下也是如此。此处所展示的首个尖峰的关键作用,为使用单波尖峰来实现快速神经处理的模型提供了进一步支持。