Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.
PLoS One. 2012;7(2):e30723. doi: 10.1371/journal.pone.0030723. Epub 2012 Feb 16.
Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity--as measured by the Fisher information--is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint.
最近的神经生理学实验证明了注意力对潜在神经活动的显著影响，这首次表明信息编码确实受到注意力的主动影响。单细胞记录显示，与非注意状态相比，注意会降低注意状态下的神经变异性和相关性。这种变异性和冗余性的降低增强了与检测和进一步处理注意刺激相关的信息。除了注意范式之外，神经回路中的局部活动可以通过多种方式进行调制，从而引出了一个一般性问题，即了解这些相对较小的调制如何影响这些回路的活动。在这里，我们使用一个可分析的神经网络模型来证明，当兴奋性和抑制性突触电流平衡时，信息的增强是如何出现的。具体来说，我们表明，网络编码的敏感性（由 Fisher 信息来衡量）在精确平衡时达到最大值。此外，我们在一个更现实的尖峰神经网络模型中也得到了类似的结果。由于输入的平衡状态已经在实验中观察到，这些结果表明，从信息编码的角度来看，这种状态在功能上是很重要的。
