Donders Institute, Radboud University, Nijmegen 6525, the Netherlands; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem 91904, Israel.
Neuron. 2014 May 21;82(4):925-38. doi: 10.1016/j.neuron.2014.03.026. Epub 2014 Apr 24.
To signal the onset of salient sensory features or execute well-timed motor sequences, neuronal circuits must transform streams of incoming spike trains into precisely timed firing. To address the efficiency and fidelity with which neurons can perform such computations, we developed a theory to characterize the capacity of feedforward networks to generate desired spike sequences. We find the maximum number of desired output spikes a neuron can implement to be 0.1-0.3 per synapse. We further present a biologically plausible learning rule that allows feedforward and recurrent networks to learn multiple mappings between inputs and desired spike sequences. We apply this framework to reconstruct synaptic weights from spiking activity and study the precision with which the temporal structure of ongoing behavior can be inferred from the spiking of premotor neurons. This work provides a powerful approach for characterizing the computational and learning capacities of single neurons and neuronal circuits.
为了表示显著感觉特征的出现或执行时机精确的运动序列，神经元回路必须将输入的尖峰列车流转换为精确计时的发射。为了解决神经元执行此类计算的效率和保真度问题，我们开发了一种理论来描述前馈网络生成所需尖峰序列的能力。我们发现，神经元可以实现的期望输出尖峰的最大数量为每个突触 0.1-0.3。我们进一步提出了一种具有生物学合理性的学习规则，允许前馈和递归网络在输入和期望尖峰序列之间学习多个映射。我们将此框架应用于从尖峰活动中重建突触权重，并研究从运动前神经元的尖峰中推断正在进行的行为的时间结构的精确性。这项工作为表征单个神经元和神经元回路的计算和学习能力提供了一种强大的方法。
