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Published on: November 12, 2019
An implementation of a spike-response model with escape noise using an avalanche diode
IEEE Transactions on Biomedical Circuits and Systems
|July 16, 2013
Summary
This study models neural network noise using avalanche diodes and spike-response curves. The novel probabilistic model accurately reproduces brain region behaviors, advancing understanding of neural pattern generation.
Area of Science:
- Computational Neuroscience
- Biophysics
Background:
- Tonic synaptic input is hypothesized to drive many cell types in the ventral medial hypothalamic (VMH) region.
- Neural patterns in the VMH may arise from spike response to noise rather than network interconnections.
Purpose of the Study:
- To introduce a novel probabilistic spike-response model.
- To investigate the role of noise in neural pattern generation.
- To model tonic noise synaptic input in neural networks.
Main Methods:
- Combined avalanche diode-generated Poisson noise with a standard exponential decay-based spike-response curve.
- Experimentally verified the characteristics of the noise source from a 0.35-μm single-photon avalanche diode.
- Implemented the spike-response model on a field-programmable gate-array (FPGA).
Main Results:
- The developed model successfully reproduced seven out of eight behaviors observed in the VMH.
- The model demonstrated the ability to simulate tonic noise synaptic input.
- The independent nature of the noise sources was confirmed.
Conclusions:
- The novel probabilistic spike-response model effectively captures key neural behaviors.
- This modeling approach provides a method for studying networks of noise-fueled neurons.
- The findings offer insights into the mechanisms of pattern generation within the brain.
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