Shadlen M N, Newsome W T
Department of Physiology and Biophysics and Regional Primate Research Center, University of Washington, Seattle, Washington 98195-7290, USA.
J Neurosci. 1998 May 15;18(10):3870-96. doi: 10.1523/JNEUROSCI.18-10-03870.1998.
Cortical neurons exhibit tremendous variability in the number and temporal distribution of spikes in their discharge patterns. Furthermore, this variability appears to be conserved over large regions of the cerebral cortex, suggesting that it is neither reduced nor expanded from stage to stage within a processing pathway. To investigate the principles underlying such statistical homogeneity, we have analyzed a model of synaptic integration incorporating a highly simplified integrate and fire mechanism with decay. We analyzed a "high-input regime" in which neurons receive hundreds of excitatory synaptic inputs during each interspike interval. To produce a graded response in this regime, the neuron must balance excitation with inhibition. We find that a simple integrate and fire mechanism with balanced excitation and inhibition produces a highly variable interspike interval, consistent with experimental data. Detailed information about the temporal pattern of synaptic inputs cannot be recovered from the pattern of output spikes, and we infer that cortical neurons are unlikely to transmit information in the temporal pattern of spike discharge. Rather, we suggest that quantities are represented as rate codes in ensembles of 50-100 neurons. These column-like ensembles tolerate large fractions of common synaptic input and yet covary only weakly in their spike discharge. We find that an ensemble of 100 neurons provides a reliable estimate of rate in just one interspike interval (10-50 msec). Finally, we derived an expression for the variance of the neural spike count that leads to a stable propagation of signal and noise in networks of neurons-that is, conditions that do not impose an accumulation or diminution of noise. The solution implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.
皮层神经元在其放电模式中，尖峰的数量和时间分布表现出巨大的变异性。此外，这种变异性似乎在大脑皮层的大片区域中得以保留，这表明它在处理通路中从一个阶段到另一个阶段既没有减少也没有扩大。为了研究这种统计同质性背后的原理，我们分析了一个突触整合模型，该模型包含一个高度简化的带有衰减的积分发放机制。我们分析了一种“高输入状态”，在此状态下，神经元在每个峰间期会接收数百个兴奋性突触输入。为了在这种状态下产生分级反应，神经元必须使兴奋与抑制保持平衡。我们发现，一个具有平衡兴奋和抑制的简单积分发放机制会产生高度可变的峰间期，这与实验数据一致。关于突触输入时间模式的详细信息无法从输出尖峰的模式中恢复，并且我们推断皮层神经元不太可能通过尖峰放电的时间模式来传递信息。相反，我们认为数量是以50 - 100个神经元组成的集合中的速率编码来表示的。这些柱状集合能够容忍大部分共同的突触输入，但其尖峰放电之间的协变却很弱。我们发现，100个神经元的集合在仅仅一个峰间期（10 - 50毫秒）内就能提供可靠的速率估计。最后，我们推导了一个关于神经尖峰计数方差的表达式，该表达式能使信号和噪声在神经元网络中稳定传播——也就是说，这些条件不会导致噪声的积累或减少。该解决方案意味着单个神经元执行类似于平均的简单代数运算，而更复杂的计算则是由于来自外部源的新输入组合在皮层柱中的解剖学汇聚而产生的。
