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A feed-forward neural logic based on synaptic and volume transmission.

Bruno Apolloni¹, Simone Bassis

¹Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, Via Comelico 39/41, 20135 Milan, Italy. apolloni@dsi.unimi.it

Brain Research Reviews

|April 10, 2007

Summary

This study introduces a novel homeostatic mechanism for feed-forward neural networks, preventing fixed states and overfitting. The system uses local neuron parameter increases and global feedback for stable, adaptive learning in artificial neural networks.

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Machine Learning

Background:

Maintaining plasticity in feed-forward neural networks is crucial for processing long signal sequences.
Existing methods often struggle to prevent networks from reaching fixed points or overfitting.
Local feedback mechanisms can be insufficient for global network stability.

Purpose of the Study:

To propose and formalize a homeostatic mechanism for feed-forward neural networks.
To ensure network plasticity and prevent undesirable states like fixed points or overfitting.
To investigate a novel global feedback system emerging from local neuron behaviors.

Main Methods:

A novel homeostatic mechanism is introduced, relying on monotonic neuron parameter increases.
Global feedback is achieved through volume transmission of a homeostatic signal.
The model is formally described and implemented using a specialized pi-calculus version.
Numerical simulations are conducted to observe network dynamics.

Main Results:

The proposed mechanism allows neurons to increase parameters beyond the mean of their peers.
A global feedback emerges from the collective behavior of individual neurons.
Simulations demonstrate behaviors with potential biological interpretations, suggesting effective homeostasis.
The network avoids falling into fixed points and mitigates overfitting.

Conclusions:

The developed homeostatic mechanism effectively maintains neural network plasticity.
The model offers a biologically plausible approach to network stability and adaptive learning.
This work provides a foundation for more robust and dynamic artificial neural network architectures.