Balanced Input Allows Optimal Encoding in a Stochastic Binary Neural Network Model: An Analytical Study

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.近期的神经生理学实验已揭示注意对底层神经活动的显著调控效应，首次证实信息编码（information encoding）确实受到注意的主动调控。单细胞记录（single cell recordings）结果显示，与非注意条件相比，注意条件下的神经活动变异性与相关性均有所降低。这种变异性与冗余性的削减，能够增强与注意靶刺激的检测及后续加工相关的信息表征。脱离注意范式的范畴后，神经环路（neural circuit）的局部活动可通过多种途径实现调控，这引出了一个核心科学问题：如何理解此类环路的活动对这类相对微弱调控的响应特性。本研究采用解析可解的神经网络模型，阐明了当兴奋性与抑制性突触电流处于平衡状态时，信息增强效应的产生机制。具体而言，我们证实了以费希尔信息（Fisher information）为衡量指标的网络编码敏感性，在完全平衡的状态下达到最大值。此外，针对更具生物学真实性的脉冲神经网络（spiking neural network）模型，我们得到了一致的研究结论。鉴于输入平衡机制已被实验观测证实，本研究结果表明，从信息编码的视角出发，该机制具备重要的功能意义。