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Related Experiment Video

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Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

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Published on: January 7, 2019

Mobility timing for agent communities, a cue for advanced connectionist systems.

Bruno Apolloni¹, Simone Bassis, Elena Pagani

¹Department of Computer Science, University of Milan, Milan 20122, Italy. apolloni@dsi.unimi.it

IEEE Transactions on Neural Networks

|November 4, 2011

Summary

This study introduces a novel wait-and-chase model for mobile artificial neural networks, describing inter-contact times between communicating neurons. The model offers a new way to train neural networks by considering neuron mobility.

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Network Science

Background:

Biological neurons and social network agents exhibit agent mobility for interaction.
Current artificial neural network (ANN) training lacks models for mobile agents and their contact dynamics.
Understanding agent movement and contact timing is crucial for developing advanced ANNs.

Purpose of the Study:

To introduce a "wait-and-chase" scheme modeling contact times between mobile agents in a connectionist system.
To develop a novel mobility model for artificial neurons that exchange data only when in proximity.
To characterize the inter-contact time distribution for mobile neurons in ANNs.

Main Methods:

Developed a "wait-and-chase" scheme inspired by biological neurons and social network agents.
Modeled inter-contact times as a random process with memory, driven by stochastic goal-hitting.
Introduced a new inter-contact time distribution merging negative exponential and Pareto laws.
Provided a constructive model description, implementation, and analytical form with parameter estimation.

Main Results:

The proposed mobility model successfully characterizes inter-contact times between mobile neurons.
Numerical experiments validated the model's robustness for ANN applications.
Demonstrated the non-independence of inter-contact times and the feasibility of model inversion for parameter inference.

Conclusions:

The novel mobility model and inference tools are suitable for training artificial neural networks with mobile agents.
This work provides a new framework for understanding and implementing agent mobility in connectionist systems.
The model addresses key requirements for practical ANN applications involving mobile neurons.