The correspondence between deterministic and stochastic digital neurons: analysis and methodology.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computational Neuroscience

Background:

• Deterministic neural networks (DNNs) and stochastic neural networks (SNNs) are widely used in various applications.
• Establishing a direct correspondence between DNNs and SNNs is crucial for understanding and designing hybrid models.
• Existing literature lacks comprehensive guidelines for ensuring this correspondence, particularly concerning neuron activation functions and output noise.

Purpose of the Study:

• To analyze the criteria for direct correspondence between deterministic and stochastic neural networks.
• To derive and present guidelines for establishing this correspondence during neural network application design.
• To address the specific roles of neuron activation function slope and bias, and output noise in this correspondence.

Main Methods:

• Theoretical analysis of the mathematical conditions for direct correspondence.
• Derivation of design guidelines based on theoretical findings.
• Simulation of relevant application examples to validate theoretical results.

Main Results:

• Identified key criteria for direct correspondence between deterministic and stochastic neural networks.
• Established specific guidelines related to neuron activation function slope and bias.
• Quantified the impact of output noise on the correspondence.
• Validated theoretical findings through simulations of practical examples.

Conclusions:

• The derived guidelines facilitate the design of neural network applications with predictable behavior.
• Understanding the interplay between activation function properties and noise is essential for accurate DNN-SNN correspondence.
• This work bridges a gap in the literature, offering practical insights for researchers and practitioners in neural network design.