Eric Kauderer-Abrams, Andrew Gilbert, Aaron R. Voelker, Ben V. Benjamin, Terrence C. Stewart, and Kwabena Boahen. A population-level approach to temperature robustness in neuromorphic systems. In IEEE International Symposium on Circuits and Systems (ISCAS). Baltimore, MD, 05 2017. IEEE. URL: https://www.researchgate.net/publication/315722402_A_Population-Level_Approach_to_Temperature_Robustness_in_Neuromorphic_Systems.
@inproceedings{abrams2017,
author = {Eric Kauderer-Abrams and Andrew Gilbert and Aaron R. Voelker and Ben V. Benjamin and Terrence C. Stewart and Kwabena Boahen},
title = {A Population-Level Approach to Temperature Robustness in Neuromorphic Systems},
booktitle = {IEEE International Symposium on Circuits and Systems (ISCAS)},
organization = {IEEE},
year = {2017},
month = {05},
address = {Baltimore, MD},
pdf = {https://web.stanford.edu/group/brainsinsilicon/documents/abrams2.pdf},
url = {https://www.researchgate.net/publication/315722402_A_Population-Level_Approach_to_Temperature_Robustness_in_Neuromorphic_Systems},
abstract = {We present a novel approach to achieving temperature-robust behavior in neuromorphic systems that operates at the population level, trading an increase in silicon-neuron count for robustness across temperature. Our silicon neurons' tuning curves were highly sensitive to temperature, which could be decoded from a 400-neuron population with a precision of 0.07°C. We overcame this temperature-sensitivity by combining methods from robust optimization theory with the Neural Engineering Framework. We developed two algorithms and compared their temperature-robustness across a range of 2°C by decoding one period of a sinusoid-like function from populations with 25 to 800 neurons. We find that 560 neurons are required to achieve the same precision across this temperature range as 35 neurons achieved at a single temperature.}
}