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Updated: May 9, 2026
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Transmission efficiency in ring, brain inspired neuronal networks. Information and energetic aspects
Bartosz Paprocki1, Janusz Szczepanski
1Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Kopernika 1, Poland.
Brain Research
|July 30, 2013
Summary
Brain-inspired neural networks with inhibitory neurons and long-range connections boost information transmission efficiency. Smaller networks are more efficient, with size increases drastically reducing performance.
Area of Science:
- Computational Neuroscience
- Neural Networks
- Information Theory
Background:
- Organisms evolve through compromises, often related to energy efficiency.
- Energetic costs of information transmission in the brain are a key research area.
- Brain-inspired neural networks offer models to study these processes.
Purpose of the Study:
- To analyze information transmission rate per energy used in neural networks.
- To investigate the impact of specific neural components on information-energetic efficiency.
- To determine the effect of network size on efficiency.
Main Methods:
- Utilized a probabilistic neuron model based on Levy and Baxter (2002).
- Studied ring neural networks with excitatory and inhibitory neurons and long-range connections.
- Assessed information transmission rate relative to energy consumption.
Main Results:
- All network components significantly improved information-energetic efficiency under various conditions.
- Inhibitory neurons enhanced efficiency by up to 50%.
- Long-range connections improved efficiency by up to 70%.
- Smaller network sizes were found to be most effective, with size increases leading to decreased efficiency.
Conclusions:
- Inhibitory neurons and long-range connections are crucial for efficient information processing in neural networks.
- Network size is a critical factor, with smaller architectures demonstrating superior information-energetic efficiency.
- These findings contribute to understanding neural coding and optimizing artificial neural systems.
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