Sigma-delta modulation can be used to model the waveforrn coding process of biological neurons. The discrete-time model can greatly simplify the analysis that are nontrivial with traditional continuous time models formulated with differential equations. In the σ-δ modulation neuronal model, we made observations that the neural information is transmitted and received reliably within the neural network due to the noise shaping and 'dithering' inherent in biological neurons....[ Read more ]

Sigma-delta modulation can be used to model the waveforrn coding process of biological neurons. The discrete-time model can greatly simplify the analysis that are nontrivial with traditional continuous time models formulated with differential equations. In the σ-δ modulation neuronal model, we made observations that the neural information is transmitted and received reliably within the neural network due to the noise shaping and 'dithering' inherent in biological neurons.

Aside from the analytical aspect, the model is also a good candidate for VLSI implementations. Sigma-delta neural networks have the same circuit complexity as the pulse coded neural networks. Sigma-delta neural networks is thus an efficient and feasible candidate on large scale implementation of silicon neural systems.