Efficient Coding and Energy Efficiency Are Promoted by Balanced Excitatory and Inhibitory Synaptic Currents in Neuronal Network.

Lianchun Yu,Zhou Shen,Chen Wang,Yuguo Yu

doi:10.3389/fncel.2018.00123

Lianchun Yu, Zhou Shen + Show 2 more

Open Access

https://doi.org/10.3389/fncel.2018.00123

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Abstract

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.SummaryWe 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.

Highlights

Through evolution, the morphology, physiology, and behavior of animals’ organs are shaped by selective pressures that act to increase the ratio of benefits accrued to costs incurred to ensure fitness for survival
These studies demonstrate the possibility that a trade-off between energy cost and information processing capacity driven by selective pressure could shape the morphology and physiology of neural systems to optimize for energy efficiency
This study is organized as follows: The network model and the methods involved in the calculation of the mutual information and energy cost are presented in section Model and Method, along with the mean field approximation solution for bistable neuron model incorporated with net synaptic currents; In section Results, we describe the response of the network to pulse inputs and the dependence of the mutual information and energy cost on the excitation and inhibition (E/I) cell ratio

Summary

Introduction

The morphology, physiology, and behavior of animals’ organs are shaped by selective pressures that act to increase the ratio of benefits accrued to costs incurred to ensure fitness for survival. Substantial advances have been made to determine the strategy used by neural systems to work efficiently while saving energy, including optimizing ion channel kinetics (Alle et al, 2009; Schmidt-Hieber and Bischofberger, 2010), developing a warm body temperature to minimize the energy cost of single action potentials (Yu et al, 2012), optimizing the number of channels on single neurons and the number of neurons in neuronal networks (Schreiber et al, 2002; Yu and Liu, 2014; Yu et al, 2016), maintaining a low probability of releasing neurotransmitters at synapses (Levy and Baxter, 2002; Harris et al, 2012), representing information with sparse spikes (Olshausen and Field, 2004; Lorincz et al, 2012; Yu et al, 2014), optimizing the inter- and intra-regional wiring of the cortex (Mitchison, 1991; Chklovskii and Koulakov, 2004), arranging functional connectivity among brain regions in the form of a “small world” network (Bassett and Bullmore, 2006; Tomasi et al, 2013), and other techniques These studies demonstrate the possibility that a trade-off between energy cost and information processing capacity driven by selective pressure could shape the morphology and physiology of neural systems to optimize for energy efficiency

Results

Discussion

Conclusion