Yu Lianchun, Shen Zhou, Wang Chen, Yu Yuguo
Institute of Theoretical Physics, Lanzhou University, Lanzhou, China.
The School of Nationalities' Educators, Qinghai Normal University, Xining, China.
Front Cell Neurosci. 2018 May 3;12:123. doi: 10.3389/fncel.2018.00123. eCollection 2018.
Selective pressure may drive neural systems to process as much information as possible with the lowest energy cost. Recent experiment evidence revealed that the ratio between synaptic excitation and inhibition (E/I) in local cortex is generally maintained at a certain value which may influence the efficiency of energy consumption and information transmission of neural networks. To understand this issue deeply, we constructed a typical recurrent Hodgkin-Huxley network model and studied the general principles that governs the relationship among the E/I synaptic current ratio, the energy cost and total amount of information transmission. We observed in such a network that there exists an optimal E/I synaptic current ratio in the network by which the information transmission achieves the maximum with relatively low energy cost. The coding energy efficiency which is defined as the mutual information divided by the energy cost, achieved the maximum with the balanced synaptic current. Although background noise degrades information transmission and imposes an additional energy cost, we find an optimal noise intensity that yields the largest information transmission and energy efficiency at this optimal E/I synaptic transmission ratio. The maximization of energy efficiency also requires a certain part of energy cost associated with spontaneous spiking and synaptic activities. We further proved this finding with analytical solution based on the response function of bistable neurons, and demonstrated that optimal net synaptic currents are capable of maximizing both the mutual information and energy efficiency. These results revealed that the development of E/I synaptic current balance could lead a cortical network to operate at a highly efficient information transmission rate at a relatively low energy cost. The generality of neuronal models and the recurrent network configuration used here suggest that the existence of an optimal E/I cell ratio for highly efficient energy costs and information maximization is a potential principle for cortical circuit networks.
We conducted numerical simulations and mathematical analysis to examine the energy efficiency of neural information transmission in a recurrent network as a function of the ratio of excitatory and inhibitory synaptic connections. We obtained a general solution showing that there exists an optimal E/I synaptic ratio in a recurrent network at which the information transmission as well as the energy efficiency of this network achieves a global maximum. These results reflect general mechanisms for sensory coding processes, which may give insight into the energy efficiency of neural communication and coding.
选择压力可能会驱使神经系统以最低的能量成本处理尽可能多的信息。最近的实验证据表明，局部皮层中突触兴奋与抑制（E/I）的比率通常维持在一定值，这可能会影响神经网络的能量消耗效率和信息传递。为了深入理解这个问题，我们构建了一个典型的循环霍奇金-赫胥黎网络模型，并研究了支配E/I突触电流比率、能量成本和信息传递总量之间关系的一般原则。我们在这样一个网络中观察到，网络中存在一个最优的E/I突触电流比率，通过该比率信息传递能够以相对较低的能量成本实现最大化。定义为互信息除以能量成本的编码能量效率，在突触电流平衡时达到最大值。尽管背景噪声会降低信息传递并带来额外的能量成本，但我们发现，在这个最优的E/I突触传递比率下，存在一个最优噪声强度，能产生最大的信息传递和能量效率。能量效率的最大化还需要与自发发放和突触活动相关的一定部分能量成本。我们基于双稳神经元的响应函数用解析解进一步证明了这一发现，并表明最优净突触电流能够使互信息和能量效率都最大化。这些结果表明，E/I突触电流平衡的发展可以使皮层网络以相对较低的能量成本在高效的信息传递速率下运行。这里使用的神经元模型和循环网络配置的一般性表明，存在一个用于高效能量成本和信息最大化的最优E/I细胞比率是皮层电路网络的一个潜在原则。
我们进行了数值模拟和数学分析，以研究循环网络中神经信息传递的能量效率作为兴奋性和抑制性突触连接比率的函数。我们得到了一个一般解，表明在循环网络中存在一个最优的E/I突触比率，在该比率下该网络的信息传递以及能量效率达到全局最大值。这些结果反映了感觉编码过程的一般机制，这可能有助于深入了解神经通信和编码的能量效率。
