Role of synaptic dynamics and heterogeneity in neuronal learning of temporal code.
Role of synaptic dynamics and heterogeneity in neuronal learning of temporal code.
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DOI：
10.1152/jn.00454.2013
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发表时间：
2013-11
影响因子：
2.5
通讯作者：
Ziv Rotman;V. Klyachko
中科院分区：
文献类型：
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作者：
Ziv Rotman;V. Klyachko
时间编码被认为在信息的神经元表征中发挥着重要作用。因此，神经元分类和学习时间尖峰模式的能力对于成功提取和处理信息至关重要。然而，由于突触传递的内在随机性，理解时间编码的神经元学习变得很复杂。使用学习神经元的计算模型（tempotron），我们研究了突触不可靠性和短期动态对神经元学习尖峰计时规则的能力的影响。我们的结果表明，即使突触不可靠，这样的模型神经元也可以学习对尖峰时序模式进行分类，尽管成功率显着降低。我们探索了改进正确尖峰时序分类的策略，发现发射集群尖峰脉冲可以显着提高学习性能。此外，通过动态突触的真实模型实现突触不可靠性的快速活动依赖性调节，进一步改进了不同突发特性和尖峰计时模式的分类。仅具有促进或仅抑制输入的神经元模型表现出对特定类型的尖峰计时规则的偏好，但促进和抑制突触的混合可以大大改善对多种规则的学习。我们通过考虑具有兴奋性海马突触中发现的自然分布输入释放概率的神经元学习模型来测试这些发现对真实神经元的适用性。我们的结果表明，尖峰爆发包含多种编码模式，可以通过随机动态突触有效学习，并且分布式释放概率显着提高学习性能。因此，突触的不可靠性和动态性可能在神经元在解码过程中学习尖峰计时规则的能力中发挥重要作用。
Temporal codes are believed to play important roles in neuronal representation of information. Neuronal ability to classify and learn temporal spiking patterns is thus essential for successful extraction and processing of information. Understanding neuronal learning of temporal code has been complicated, however, by the intrinsic stochasticity of synaptic transmission. Using a computational model of a learning neuron, the tempotron, we studied the effects of synaptic unreliability and short-term dynamics on the neuron's ability to learn spike timing rules. Our results suggest that such a model neuron can learn to classify spike timing patterns even with unreliable synapses, albeit with a significantly reduced success rate. We explored strategies to improve correct spike timing classification and found that firing clustered spike bursts significantly improves learning performance. Furthermore, rapid activity-dependent modulation of synaptic unreliability, implemented with realistic models of dynamic synapses, further improved classification of different burst properties and spike timing modalities. Neuronal models with only facilitating or only depressing inputs exhibited preference for specific types of spike timing rules, but a mixture of facilitating and depressing synapses permitted much improved learning of multiple rules. We tested applicability of these findings to real neurons by considering neuronal learning models with the naturally distributed input release probabilities found in excitatory hippocampal synapses. Our results suggest that spike bursts comprise several encoding modalities that can be learned effectively with stochastic dynamic synapses, and that distributed release probabilities significantly improve learning performance. Synaptic unreliability and dynamics may thus play important roles in the neuron's ability to learn spike timing rules during decoding.