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

Area of Science:

• Computational neuroscience
• Neural coding
• Systems neuroscience

Background:

• Neural systems are shaped by selective pressures to maximize information processing with minimal energy expenditure.
• The ratio of excitation to inhibition (E/I) in cortical circuits is crucial for energy consumption and information transmission efficiency.

Purpose of the Study:

• To investigate the relationship between E/I synaptic current ratio, energy cost, and information transmission in a recurrent neural network.
• To identify optimal conditions for maximizing information processing efficiency in neural networks.

Main Methods:

• Construction of a recurrent Hodgkin-Huxley network model.
• Numerical simulations and mathematical analysis using bistable neuron response functions.
• Examination of coding energy efficiency (mutual information divided by energy cost).

Main Results:

• An optimal E/I synaptic current ratio exists that maximizes information transmission at a low energy cost.
• Maximum coding energy efficiency is achieved with balanced synaptic currents.
• An optimal noise intensity enhances information transmission and energy efficiency at the optimal E/I ratio.

Conclusions:

• A balanced E/I synaptic current ratio enables cortical networks to operate at high information transmission rates with low energy costs.
• The findings suggest a general principle for efficient neural communication and coding in cortical circuits.
• Optimal net synaptic currents maximize both mutual information and energy efficiency.