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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Constructing multilayered neural networks with sparse, data-driven connectivity using biologically-inspired,

Robert A Baxter¹, William B Levy²

¹Department of Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA 22908, United States of America; Baxter Adaptive Systems, Bedford, MA 01730, United States of America.

Neural Networks : the Official Journal of the International Neural Network Society

|November 2, 2019

Summary

This study introduces adaptive synaptogenesis networks with novel brain development aspects. Simulations show improved performance and energy efficiency through controlled neuron survival and synaptogenesis timing.

Keywords:

Apoptosis Brain development Energy efficient Neural network Synaptogenesis Unsupervised learning

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Area of Science:

Computational Neuroscience
Neuroscience
Artificial Intelligence

Background:

Brain complexity necessitates intrinsic self-regulating mechanisms for construction and control.
Synaptogenesis, the formation of new neural connections, is crucial for neuronal connectivity and brain performance.
Adaptive synaptogenesis networks integrate synaptogenesis, synaptic modification, and shedding for sparse network construction.

Purpose of the Study:

To incorporate novel neuroscientific observations into adaptive synaptogenesis models.
To investigate the trade-off between brain performance and energy expenditure.
To develop a computational model that simulates brain development and function.

Main Methods:

Incorporation of multiple layers, neuron survival/death based on information transmission, and bigrade growth factor signaling.
Development of adaptive synaptogenesis networks with enhanced features.
Simulations to analyze network performance (information loss, classification errors) and energy expenditure (neuron count).

Main Results:

Identified the critical role of intermediate neural layers in regulating synaptogenesis and neuron elimination.
Demonstrated performance and energy-saving benefits from delayed synaptogenesis in subsequent layers.
Showcased how neuron elimination in preceding layers enhances energy savings and code compression without significant performance degradation.

Conclusions:

The refined adaptive synaptogenesis model effectively balances performance and energy efficiency.
Strategic control over synaptogenesis timing and neuron elimination are key to optimizing neural network function.
This research provides insights into efficient brain-like network design for computational applications.