Mikula Shawn, Niebur Ernst
Zanvyl Krieger Mind/Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA.
Neural Comput. 2005 Apr;17(4):881-902. doi: 10.1162/0899766053429408.
We provide an analytical recurrent solution for the firing rates and cross-correlations of feedforward networks with arbitrary connectivity, excitatory or inhibitory, in response to steady-state spiking input to all neurons in the first network layer. Connections can go between any two layers as long as no loops are produced. Mean firing rates and pairwise cross-correlations of all input neurons can be chosen individually. We apply this method to study the propagation of rate and synchrony information through sample networks to address the current debate regarding the efficacy of rate codes versus temporal codes. Our results from applying the network solution to several examples support the following conclusions: (1) differential propagation efficacy of rate and synchrony to higher layers of a feedforward network is dependent on both network and input parameters, and (2) previous modeling and simulation studies exclusively supporting either rate or temporal coding must be reconsidered within the limited range of network and input parameters used. Our exact, analytical solution for feedforward networks of coincidence detectors should prove useful for further elucidating the efficacy and differential roles of rate and temporal codes in terms of different network and input parameter ranges.
我们针对具有任意连接性（兴奋性或抑制性）的前馈网络的发放率和互相关，给出了一个解析递归解，该网络响应于第一层网络中所有神经元的稳态脉冲输入。只要不产生回路，连接可以在任意两层之间进行。所有输入神经元的平均发放率和成对互相关可以单独选择。我们应用此方法来研究速率和同步信息通过样本网络的传播，以解决当前关于速率编码与时间编码有效性的争论。我们将网络解应用于几个例子的结果支持以下结论：（1）速率和同步向前馈网络更高层的差异传播效率取决于网络和输入参数两者，并且（2）以前专门支持速率编码或时间编码的建模和模拟研究必须在所用网络和输入参数的有限范围内重新考虑。我们针对重合检测器前馈网络的精确解析解，应该有助于进一步阐明在不同网络和输入参数范围内速率编码和时间编码的有效性及不同作用。
